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is about $5 (US$ 2009) per  
is about $5 (US$ 2009) per  


megawatt-hour (MWh) of wind, or about $0.005 per kilowatt-hour (kWh) of electricity used by customers.  
megawatt-hour (MWh) of wind, or about $0.005 per kilowatt-hour (kWh) of electricity used by customers.
: 2. multiple and geographically diverse wind resources?
: 2. multiple and geographically diverse wind resources?
The study results  
The study results  
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Long-distance operations, contributes substantially to integrating large amounts of long-distance transmission has other value in terms of system robustness that was not completely evaluated in EWITS.
Long-distance operations, contributes substantially to integrating large amounts of long-distance transmission has other value in terms of system robustness that was not completely evaluated in EWITS.
: 4. How do remote wind resources compare to local wind resources?
: 4. How do remote wind resources compare to local wind resources?
In the Eastern Interconnection, the Eastern Wind Data Study database (AWS Truewind 2009) shows that the higher quality winds in the Great Plains have capacity factors that are about 7%-9% higher than onshore wind resources near the high-load urban centers in the East. Offshore plants have capacity factors on par with Great Plains resources but the cost of energy is higher because capital costs are higher.  
In the Eastern Interconnection, the Eastern Wind Data Study database (AWS Truewind 2009) shows that the higher quality winds in the Great Plains have capacity factors that are about 7%-9% higher than onshore wind resources near the high-load urban centers in the East. Offshore plants have capacity factors on par with Great Plains resources but the cost of energy is higher because capital costs are higher.
: 5. How much does geographical diversity, or spreading the wind out across a large area, help reduce system variability and uncertainty?
: 5. How much does geographical diversity, or spreading the wind out across a large area, help reduce system variability and uncertainty?
Quite substantially.  
Quite substantially.
: 6. What is the role and value of wind forecasting? generation, forecasting will play a key role in keeping energy markets system security.  
: 6. What is the role and value of wind forecasting? generation, forecasting will play a key role in keeping energy markets system security.
: 7. wind variability and uncertainty management?
: 7. wind variability and uncertainty management?
This and other recent studies (see Bibliography) reinforce the concept that large operating areas-in terms of load, generating units, and geography-combined with adequate transmission, are the most effective measures for managing wind generation.  
This and other recent studies (see Bibliography) reinforce the concept that large operating areas-in terms of load, generating units, and geography-combined with adequate transmission, are the most effective measures for managing wind generation.
: 8. How does wind generation capacity value affect reliability (i.e., supply resource adequacy)? Wind generation can contribute to system adequacy, and additional transmission can enhance that contribution.
: 8. How does wind generation capacity value affect reliability (i.e., supply resource adequacy)? Wind generation can contribute to system adequacy, and additional transmission can enhance that contribution.
SCENARIO COSTS the degree suggested by the study scenarios would result if many capital investments were made from the present through 2024. Consequently, economic analysis of the scenarios brings to light complicated questions that cannot be answered precisely without a detailed timeline of capital expenditures.
SCENARIO COSTS the degree suggested by the study scenarios would result if many capital investments were made from the present through 2024. Consequently, economic analysis of the scenarios brings to light complicated questions that cannot be answered precisely without a detailed timeline of capital expenditures.
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-io (new transmission for each scenario is based on economic performance for the conditions of the generation scenario) Economic sensitivity simulations of the hourly operation of the power transmission overlayThe contribution made by wind generation to resource adequacy and plan
-io (new transmission for each scenario is based on economic performance for the conditions of the generation scenario) Economic sensitivity simulations of the hourly operation of the power transmission overlayThe contribution made by wind generation to resource adequacy and plan
-ning capacity margin.As part of this study, NREL convened a Technical Review Committee (TRC) with representation from regional electric reliability councils, expert reviewers, 62the study subcontractor, transmission planners, utilities, and wind industry representatives. The TRC met 6 times over 14 months during the project to review study progress; comment on study inputs, methods, and assumptions; assist with collecting data; and review drafts of the study report.The Eastern Wind Data Study (AWS Truewind 2009), a precursor to this study, for the eastern United States. The hourly time series data produced in that focuses on the integration of wind power into the majority of the Eastern PROJECT OBJECTIVESFor this project, the study team evaluated the power system impacts, costs, and conceptual transmission overlays attendant with increasing wind generation capacity to 20% and 30% of retail electric energy sales in 2024 for the study area, the following entities:Portions of the Southeastern Electric Reliability Council (SERC)Southwest Power Pool (SPP)Mid-Continent Area Power Pool (MAPP)and related technical work (see Bibliography), and coordinates with ongoing objective was to produce meaningful, broadly supported results through a technically rigorous and inclusive study process. KEY ISSUES AND QUESTIONShad been conducted for individual or regional entities (see Bibliography). When many of those studies were performed, the "outside world" (i.e., the operation from operating history. Now that the installed capacity of wind generation is approaching 30 GW in the United States (and concentrated in certain areas), and many states have passed legislation mandating that an appreciable fraction of electrical energy be produced by certain renewable resources, interest has grown 63Many of the key questions to be answered in this study are similar to those posed in previous wind integration studies, but the scope and scale are entirely has a limited capacity for accommodating additional wind generation; transmission congestion is already an issue in some areas, including those with the potential for tenfold or greater development in wind capacity. Consequently, evaluating transmission needs was also a major aspect of this study.Key questions posed at the outset of the project include the following:
-ning capacity margin.As part of this study, NREL convened a Technical Review Committee (TRC) with representation from regional electric reliability councils, expert reviewers, 62the study subcontractor, transmission planners, utilities, and wind industry representatives. The TRC met 6 times over 14 months during the project to review study progress; comment on study inputs, methods, and assumptions; assist with collecting data; and review drafts of the study report.The Eastern Wind Data Study (AWS Truewind 2009), a precursor to this study, for the eastern United States. The hourly time series data produced in that focuses on the integration of wind power into the majority of the Eastern PROJECT OBJECTIVESFor this project, the study team evaluated the power system impacts, costs, and conceptual transmission overlays attendant with increasing wind generation capacity to 20% and 30% of retail electric energy sales in 2024 for the study area, the following entities:Portions of the Southeastern Electric Reliability Council (SERC)Southwest Power Pool (SPP)Mid-Continent Area Power Pool (MAPP)and related technical work (see Bibliography), and coordinates with ongoing objective was to produce meaningful, broadly supported results through a technically rigorous and inclusive study process. KEY ISSUES AND QUESTIONShad been conducted for individual or regional entities (see Bibliography). When many of those studies were performed, the "outside world" (i.e., the operation from operating history. Now that the installed capacity of wind generation is approaching 30 GW in the United States (and concentrated in certain areas), and many states have passed legislation mandating that an appreciable fraction of electrical energy be produced by certain renewable resources, interest has grown 63Many of the key questions to be answered in this study are similar to those posed in previous wind integration studies, but the scope and scale are entirely has a limited capacity for accommodating additional wind generation; transmission congestion is already an issue in some areas, including those with the potential for tenfold or greater development in wind capacity. Consequently, evaluating transmission needs was also a major aspect of this study.Key questions posed at the outset of the project include the following:
: 1. What impacts and costs do wind generation variability and uncertainty impose on system operations?  
: 1. What impacts and costs do wind generation variability and uncertainty impose on system operations?
: 2. multiple and geographically diverse wind resources?  
: 2. multiple and geographically diverse wind resources?
: 3. large quantities of remote wind energy to urban markets?  
: 3. large quantities of remote wind energy to urban markets?
: 4. How do remote wind resources compare to local wind resources?  
: 4. How do remote wind resources compare to local wind resources?
: 5. How much does geographical diversity, or spreading the wind out across a large area, help reduce system variability and uncertainty?
: 5. How much does geographical diversity, or spreading the wind out across a large area, help reduce system variability and uncertainty?
: 6. What is the role and value of wind forecasting?  
: 6. What is the role and value of wind forecasting?
: 7. wind variability and uncertainty management?  
: 7. wind variability and uncertainty management?
: 8. How does wind generation capacity value affect supply resource adequacy? OVERVIEW OF PROJECT TASKSEvaluating the impacts of large-scale wind generation development across developing wind plant power outputs, conducting transmission analysis, and studying the implications of high-penetration wind integration. The last two tasks are formally part of the study documented in this report. A reasonably accurate, physically consistent depiction of what wind generation would look like to power system operators has been the critical input to all previous integration studies. Expanding the area of interest to include nearly challenge in this respect. The precursor effort (AWS Truewind 2009), though,  resulted in an extensive database of synthesized, high-resolution, correlated 64quality control process was applied to the raw data, followed by construction of more than 700 GW of wind power plant temporal data, down to a resolution of 10 minutes for a consecutive 3-year period (2004, 2005, and 2006).wind generation scenarios for 2024, three with 20% of the projected electrical wind generation penetration to 30%. represents the leading edge of engineering and economic methods combined with computational horsepower. With the tools and analytical methods described in this report, the study team designed extensive top-down transmission overlays that span the interconnection, and then rigorously analyzed the operating and planning reserve impacts. and operational implications of adding up to 30% penetration by energy of wind generation and solar energy to the WestConnect footprint in the Western aimed at developing, where possible and appropriate, common solutions to wind integration challenges in Europe. The study also seeks to identify arrangements that will make the best use of the pan-European transmission network, allowing And as the amount of wind generation continues to increase, these studies are unlikely to be the last. ORGANIZATION OF THIS REPORT Section 2 describes the data the team used and the process employed to create the four wind generation scenarios. Characteristics of the wind resource on a regional basis are also described.
: 8. How does wind generation capacity value affect supply resource adequacy? OVERVIEW OF PROJECT TASKSEvaluating the impacts of large-scale wind generation development across developing wind plant power outputs, conducting transmission analysis, and studying the implications of high-penetration wind integration. The last two tasks are formally part of the study documented in this report. A reasonably accurate, physically consistent depiction of what wind generation would look like to power system operators has been the critical input to all previous integration studies. Expanding the area of interest to include nearly challenge in this respect. The precursor effort (AWS Truewind 2009), though,  resulted in an extensive database of synthesized, high-resolution, correlated 64quality control process was applied to the raw data, followed by construction of more than 700 GW of wind power plant temporal data, down to a resolution of 10 minutes for a consecutive 3-year period (2004, 2005, and 2006).wind generation scenarios for 2024, three with 20% of the projected electrical wind generation penetration to 30%. represents the leading edge of engineering and economic methods combined with computational horsepower. With the tools and analytical methods described in this report, the study team designed extensive top-down transmission overlays that span the interconnection, and then rigorously analyzed the operating and planning reserve impacts. and operational implications of adding up to 30% penetration by energy of wind generation and solar energy to the WestConnect footprint in the Western aimed at developing, where possible and appropriate, common solutions to wind integration challenges in Europe. The study also seeks to identify arrangements that will make the best use of the pan-European transmission network, allowing And as the amount of wind generation continues to increase, these studies are unlikely to be the last. ORGANIZATION OF THIS REPORT Section 2 describes the data the team used and the process employed to create the four wind generation scenarios. Characteristics of the wind resource on a regional basis are also described.
Section 3 discusses the data and analysis methods used to develop the scenarios for the 2024 study year. The section also describes the tools used for the detailed assessments of wind generation operational impacts and resource adequacy contributions.
Section 3 discusses the data and analysis methods used to develop the scenarios for the 2024 study year. The section also describes the tools used for the detailed assessments of wind generation operational impacts and resource adequacy contributions.
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73Figure 2-6. Nameplate capacity by regionSCENARIO DETAILSinstalled capacity by operating region, sizes and locations of plants, and state-by-state capacity. REFERENCE CASEThis scenario is designed to approximate the current state of wind development plus some expected near-term development guided by interconnection queues and state RPS. This scenario totaled about 6% of the total 2024 projected load  Table 2-3 lists capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-7. TABLE 23. REFERENCE CASE, 6% OF 2024 LOAD REQUIREMENTSREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 8,310 3,000 11,310 33MISO + SAPP 19,732 19,732 63NYISO 4,932 3,000 7,932 20PJM 19,402 1,620 21,022 65SERC 1,009 2,000 3,009 13SPP 7,419 7,419 26TVA 1,247 1,247 4TOTAL 62,051 9,620 71,671 224 74Figure 2-7. Wind plant size and location for Reference Case SCENARIO 1factors across the interconnection. Consequently, it has the largest Great Plains wind capacity of the three 20% scenarios and takes advantage of the best onshore resources in the East. Table 2-4 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-8. Figure 2-9 is a better visual illustration of state-by-state installed capacity.TABLE 24. SCENARIO 120% HIGH CAPACITY FACTOR, ONSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 4,291 0 4,291 13MISO + SAPP 94,808 0 94,808 404NYISO 7,742 0 7,742 22PJM 22,669 0 22,669 64SERC 1,009 0 1,009 3SPP 91,843 0 91,843 234TVA 1,247 0 1,247 4TOTAL 223,609 0 223,609 744 75Figure 2-8. Installed capacity-Scenario 1 Figure 2-9. State map of nameplate capacity-Scenario 1 76SCENARIO 2the East Coast. This scenario corresponds most closely to a 20% scenario studied in a recent collaborative planning effort (JCSP 2008).Table 2-5 shows capacity by operating region. Figure 2-10 shows locations and sizes of individual plants, and Figure 2-11 shows state-by-state installed capacity.TABLE 25. SCENARIO 220% HYBRID WITH OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 8,837 5,000 13,837 46MISO + SAPP 69,444 0 69,444 288NYISO 13,887 2,620 16,507 48PJM 28,192 5,000 33,192 97SERC 1,009 4,000 5,009 16SPP 86,666 0 86,666 245TVA 1,247 0 1,247 4TOTAL 209,282 16,620 225,902 745 Figure 2-10. Installed capacity-Scenario 2 77 Figure 2-11. State map of nameplate capacity-Scenario 2SCENARIO 3To create a contrast with Scenario 1, a large amount of wind generation is moved from the Great Plains nearer to the East Coast load centers. To bring about this shift, a large amount of offshore wind generation is required. Table 2-6 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-12, with the state-by-state illustration in Figure 2-13.TABLE 26. SCENARIO 320% LOCAL, WITH AGGRESSIVE OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 13,887 11,040 24,927 82MISO + SAPP 46,255 0 46,255 189NYISO 13,887 9,280 23,167 71PJM 38,956 39,780 78,736 244SERC 1,009 4,000 5,009 16SPP 50,958 0 50,958 139TVA 1,247 0 1,247 4TOTAL 166,199 64,100 230,299 746 78Figure 2-12. Installed capacity-Scenario 3 Figure 2-13. State map of nameplate capacity-Scenario 3 79SCENARIO 4Reaching 30% energy penetration requires more than 300 GW of wind resources in the NREL database. A large amount of offshore wind is required, and the amounts in the Great Plains are comparable to Scenario 1. Table 2-7 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-14, with the state-by-state illustration in Figure 2-15.TABLE 27. SCENARIO 430% AGGRESSIVE ONSHORE AND OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 13,887 11,040 24,927 82MISO + SAPP 95,046 0 95,046 405NYISO 13,887 9,280 23,167 71PJM 38,956 54,780 93,736 295SERC 1,009 4,000 5,009 16SPP 94,576 0 94,576 243TVA 1,247 0 1,247 4TOTAL 258,608 79,100 337,708 1,116 Figure 2-14. Installed capacity -Scenario 4 80 Figure 2-15. State map of nameplate capacity-Scenario 4 81SECTION 3: ANALYTICAL METHODOLOGY: DATA, MODELS, AND TOOLSThe study analysis focused on three major areas:
73Figure 2-6. Nameplate capacity by regionSCENARIO DETAILSinstalled capacity by operating region, sizes and locations of plants, and state-by-state capacity. REFERENCE CASEThis scenario is designed to approximate the current state of wind development plus some expected near-term development guided by interconnection queues and state RPS. This scenario totaled about 6% of the total 2024 projected load  Table 2-3 lists capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-7. TABLE 23. REFERENCE CASE, 6% OF 2024 LOAD REQUIREMENTSREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 8,310 3,000 11,310 33MISO + SAPP 19,732 19,732 63NYISO 4,932 3,000 7,932 20PJM 19,402 1,620 21,022 65SERC 1,009 2,000 3,009 13SPP 7,419 7,419 26TVA 1,247 1,247 4TOTAL 62,051 9,620 71,671 224 74Figure 2-7. Wind plant size and location for Reference Case SCENARIO 1factors across the interconnection. Consequently, it has the largest Great Plains wind capacity of the three 20% scenarios and takes advantage of the best onshore resources in the East. Table 2-4 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-8. Figure 2-9 is a better visual illustration of state-by-state installed capacity.TABLE 24. SCENARIO 120% HIGH CAPACITY FACTOR, ONSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 4,291 0 4,291 13MISO + SAPP 94,808 0 94,808 404NYISO 7,742 0 7,742 22PJM 22,669 0 22,669 64SERC 1,009 0 1,009 3SPP 91,843 0 91,843 234TVA 1,247 0 1,247 4TOTAL 223,609 0 223,609 744 75Figure 2-8. Installed capacity-Scenario 1 Figure 2-9. State map of nameplate capacity-Scenario 1 76SCENARIO 2the East Coast. This scenario corresponds most closely to a 20% scenario studied in a recent collaborative planning effort (JCSP 2008).Table 2-5 shows capacity by operating region. Figure 2-10 shows locations and sizes of individual plants, and Figure 2-11 shows state-by-state installed capacity.TABLE 25. SCENARIO 220% HYBRID WITH OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 8,837 5,000 13,837 46MISO + SAPP 69,444 0 69,444 288NYISO 13,887 2,620 16,507 48PJM 28,192 5,000 33,192 97SERC 1,009 4,000 5,009 16SPP 86,666 0 86,666 245TVA 1,247 0 1,247 4TOTAL 209,282 16,620 225,902 745 Figure 2-10. Installed capacity-Scenario 2 77 Figure 2-11. State map of nameplate capacity-Scenario 2SCENARIO 3To create a contrast with Scenario 1, a large amount of wind generation is moved from the Great Plains nearer to the East Coast load centers. To bring about this shift, a large amount of offshore wind generation is required. Table 2-6 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-12, with the state-by-state illustration in Figure 2-13.TABLE 26. SCENARIO 320% LOCAL, WITH AGGRESSIVE OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 13,887 11,040 24,927 82MISO + SAPP 46,255 0 46,255 189NYISO 13,887 9,280 23,167 71PJM 38,956 39,780 78,736 244SERC 1,009 4,000 5,009 16SPP 50,958 0 50,958 139TVA 1,247 0 1,247 4TOTAL 166,199 64,100 230,299 746 78Figure 2-12. Installed capacity-Scenario 3 Figure 2-13. State map of nameplate capacity-Scenario 3 79SCENARIO 4Reaching 30% energy penetration requires more than 300 GW of wind resources in the NREL database. A large amount of offshore wind is required, and the amounts in the Great Plains are comparable to Scenario 1. Table 2-7 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-14, with the state-by-state illustration in Figure 2-15.TABLE 27. SCENARIO 430% AGGRESSIVE ONSHORE AND OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 13,887 11,040 24,927 82MISO + SAPP 95,046 0 95,046 405NYISO 13,887 9,280 23,167 71PJM 38,956 54,780 93,736 295SERC 1,009 4,000 5,009 16SPP 94,576 0 94,576 243TVA 1,247 0 1,247 4TOTAL 258,608 79,100 337,708 1,116 Figure 2-14. Installed capacity -Scenario 4 80 Figure 2-15. State map of nameplate capacity-Scenario 4 81SECTION 3: ANALYTICAL METHODOLOGY: DATA, MODELS, AND TOOLSThe study analysis focused on three major areas:
: 1. Developing conceptual transmission to accommodate the levels of wind  
: 1. Developing conceptual transmission to accommodate the levels of wind
: 2. Assessing the impacts of the wind generation in each scenario on grid 3. Determining the level to which wind generation in each scenario contributes to resource adequacy (i.e., its capacity value).The analytical methods used in this study build on those established in previous integration studies  conducted over the past 10 years (see, for example, EnerNex Corporation and Wind Logics 2004; Bai et al. 2007; GE Energy 2008). A chronological data set of wind generation and load data is the critical input for Hourly load data from across the interconnection for years corresponding to the National Renewable Energy Laboratory's (NREL) mesoscale data were obtained loads in 2024. The basic resolution for both load and wind data is 1 hour, although higher resolution (10-minute average) data are available from the mesoscale data for wind generation, and samples of higher resolution data were analytical methods use the chronological wind generation and load data over a 3-year period as inputs. Brief descriptions of the methods follow:Statistical analysis of wind generation and load data, separately and in combination, to assess impacts on operating reservesChronological production simulations, which, if correctly structured, are used to simulate scheduling and operation of the power systemMonte Carlo-based chronological resource adequacy assessment, which uses annual or multiannual hourly data for load and wind generation to determine the probability that available supply resources would not be able to meet demand.
: 2. Assessing the impacts of the wind generation in each scenario on grid 3. Determining the level to which wind generation in each scenario contributes to resource adequacy (i.e., its capacity value).The analytical methods used in this study build on those established in previous integration studies  conducted over the past 10 years (see, for example, EnerNex Corporation and Wind Logics 2004; Bai et al. 2007; GE Energy 2008). A chronological data set of wind generation and load data is the critical input for Hourly load data from across the interconnection for years corresponding to the National Renewable Energy Laboratory's (NREL) mesoscale data were obtained loads in 2024. The basic resolution for both load and wind data is 1 hour, although higher resolution (10-minute average) data are available from the mesoscale data for wind generation, and samples of higher resolution data were analytical methods use the chronological wind generation and load data over a 3-year period as inputs. Brief descriptions of the methods follow:Statistical analysis of wind generation and load data, separately and in combination, to assess impacts on operating reservesChronological production simulations, which, if correctly structured, are used to simulate scheduling and operation of the power systemMonte Carlo-based chronological resource adequacy assessment, which uses annual or multiannual hourly data for load and wind generation to determine the probability that available supply resources would not be able to meet demand.
82Notes: LOLE = loss of load expectation; ELCC = e~ective load-carrying capability.Figure 3-1. Overview of the study processThe consensus approach for assessing wind integration impacts is to simulate the scheduling and operation of the power system with wind generation over and load and their respective variability and uncertainty characteristics are represented in the input data. This prevents focusing only on severe events, like major wind ramps, that would be expected infrequently. the analysis:Development of the transmission overlays, where production simulations for the current system determine both the locations and the economic value of new transmission
82Notes: LOLE = loss of load expectation; ELCC = e~ective load-carrying capability.Figure 3-1. Overview of the study processThe consensus approach for assessing wind integration impacts is to simulate the scheduling and operation of the power system with wind generation over and load and their respective variability and uncertainty characteristics are represented in the input data. This prevents focusing only on severe events, like major wind ramps, that would be expected infrequently. the analysis:Development of the transmission overlays, where production simulations for the current system determine both the locations and the economic value of new transmission
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  = Available Capacity - Peak Coincident Demand        Peak Coincident Demand Peak demand is determined using the noncoincident annual peaks applied to load demand reaches its peak for the system (refer to Table 3-1 for demand assumptions). The available capacity is the maximum capacity available during generation. Firm generation is the percentage of a generator's maximum capacity that is counted toward calculation of the reserve margin. For example, wind units contribute 20% of their maximum capacity toward reserve margin calculations. Table 3-7 shows the modeled reserve targets.
  = Available Capacity - Peak Coincident Demand        Peak Coincident Demand Peak demand is determined using the noncoincident annual peaks applied to load demand reaches its peak for the system (refer to Table 3-1 for demand assumptions). The available capacity is the maximum capacity available during generation. Firm generation is the percentage of a generator's maximum capacity that is counted toward calculation of the reserve margin. For example, wind units contribute 20% of their maximum capacity toward reserve margin calculations. Table 3-7 shows the modeled reserve targets.
96TABLE 37. TARGET RESERVE MARGINS BY REGIONREGION RESERVE TARGET ~%ENTERGY 15.0ISONE 15.0MAPP 15.0MISO 15.0NYISO 15.0PJM 15.5SERC 15.0SPP 15.0TVA 15.0GENERATION EXPANSION ALTERNATIVESturbines, nuclear facilities, and wind facilities. Before using the capacity expansion model, the project team eliminated other alternatives such as biomass, and hydro facilities as options because they were not economically competitive with the conventional resources under the assumptions applied to the analysis. that the capital costs for wind generation in this table are lower than what is assumed later in the report when total costs are tabulated. These lower values were artifacts of an earlier planning study. Because of the approach used here, Wind is given a 20% capacity credit against the required planning margin; all other units produce 100% of available capacity at peak system hours. Because of the wind modeling technique, resource adequacy calculations take into account need for additional capacity.
96TABLE 37. TARGET RESERVE MARGINS BY REGIONREGION RESERVE TARGET ~%ENTERGY 15.0ISONE 15.0MAPP 15.0MISO 15.0NYISO 15.0PJM 15.5SERC 15.0SPP 15.0TVA 15.0GENERATION EXPANSION ALTERNATIVESturbines, nuclear facilities, and wind facilities. Before using the capacity expansion model, the project team eliminated other alternatives such as biomass, and hydro facilities as options because they were not economically competitive with the conventional resources under the assumptions applied to the analysis. that the capital costs for wind generation in this table are lower than what is assumed later in the report when total costs are tabulated. These lower values were artifacts of an earlier planning study. Because of the approach used here, Wind is given a 20% capacity credit against the required planning margin; all other units produce 100% of available capacity at peak system hours. Because of the wind modeling technique, resource adequacy calculations take into account need for additional capacity.
97TABLE 38. MODELED GENERATOR PROTOTYPE DATA VALUES IN US $2008 aALTERNATIVE2008 OVERNIGHTCONSTRUCTION COST~$/KW2008 VARIABLEO&M~$/MWH2008 FIXED O&M~$/KWBOOK LIFEOPERATING LIFECOAL 1,833 4.60 28.22 40 60CC 857 5.17 34.01 30 30CT 597 4.62 17.72 30 30WIND ~ONSHORE1,750 5.70 11.93 25 25WIND~OFFSHORE 2,440 18.67 15.55 25 25NUCLEAR 2,928 4.63 69.57 40 60 a All costs escalated at 3% annually during study period.Notes: kW = kilowatt; MWh = megawatt-hourenergy projections available through the PowerBase database. Demand response, however, is added to the individual areas to maintain existing penetration percentages through the study period, as shown in Table 3-9. The demand response units are modeled much like high-cost combustion turbines to limit capacity factors to values less than 1%.TABLE 39. MODELED PENETRATION OF DEMAND RESPONSE BY REGION ~AS PERCENTAGE OF PEAKSTUDY REGIONNONCOINCIDENT PEAK DEMAND ~MWMODELED DEMAND RESPONSE~MWRATIO OF DEMAND RESPONSE TO PEAK DEMAND ~%ENTERGY 22,712 50 0.18ISONE 28,227 2,400 8.50MISO + MAPP 125,777 4,362 3.47NYISO 35,064 2,014 5.74PJM 142,826 3,239 2.27SERC 96,071 1,745 1.82SPP 47,478 736 1.55TVA 47,633 2,309 4.85 98SECTION 4: DEVELOPING ECONOMIC TRANSMISSION OVERLAYS current installed capacity by nearly an order of magnitude. Transmission issues are already limiting wind energy development in some regions, so it is a near the much higher amounts of wind generation represented in the Eastern Wind This section describes the methodology and results of the transmission BACKGROUNDdeveloped through a planning process that had two basic objectives: (1) to reliability of the bulk power system in the face of growing demand. By building transmission facilities to interconnect with neighbors, capacity resources could be shared in emergencies, reducing the amount of excess capacity an individual utility must maintain to serve load reliably. Opportunities for economic Because it is primarily a source of energy, not capacity, wind generation does system with a major facility out of service. The status of conventional generating units during these periods is usually a given. With large amounts of wind generation, the disposition of other conventional generating units may not be so easily ascertained; in addition, high amounts of wind generation are likely in off-peak hours or seasons that might not be of special interest for reliability issues.A transmission planning method based on economics has been developed, transmission organization (RTO) for insight into transmission needs for 99to develop conceptual interregional transmission plans required for a 5% wind energy scenario and a 20% wind energy scenario. evaluate the transmission that would be needed to facilitate 20% and 30% wind delivery across a large geographical area, energy-based regional transmission planning is necessary to incorporate comprehensive economic assessment using production-cost simulations. By linking the markets across the entire Eastern regional transmission plan could outweigh its cost. Because the JCSP also focused on a 20% scenario, the results from that effort of the high-quality wind resources in the Great Plains and Upper Midwest. A similar level of regional detail was not available for other parts of the Eastern The transmission development methodology is a sequential process that focuses on a snapshot of a single future year. The steps in the process follow:  
97TABLE 38. MODELED GENERATOR PROTOTYPE DATA VALUES IN US $2008 aALTERNATIVE2008 OVERNIGHTCONSTRUCTION COST~$/KW2008 VARIABLEO&M~$/MWH2008 FIXED O&M~$/KWBOOK LIFEOPERATING LIFECOAL 1,833 4.60 28.22 40 60CC 857 5.17 34.01 30 30CT 597 4.62 17.72 30 30WIND ~ONSHORE1,750 5.70 11.93 25 25WIND~OFFSHORE 2,440 18.67 15.55 25 25NUCLEAR 2,928 4.63 69.57 40 60 a All costs escalated at 3% annually during study period.Notes: kW = kilowatt; MWh = megawatt-hourenergy projections available through the PowerBase database. Demand response, however, is added to the individual areas to maintain existing penetration percentages through the study period, as shown in Table 3-9. The demand response units are modeled much like high-cost combustion turbines to limit capacity factors to values less than 1%.TABLE 39. MODELED PENETRATION OF DEMAND RESPONSE BY REGION ~AS PERCENTAGE OF PEAKSTUDY REGIONNONCOINCIDENT PEAK DEMAND ~MWMODELED DEMAND RESPONSE~MWRATIO OF DEMAND RESPONSE TO PEAK DEMAND ~%ENTERGY 22,712 50 0.18ISONE 28,227 2,400 8.50MISO + MAPP 125,777 4,362 3.47NYISO 35,064 2,014 5.74PJM 142,826 3,239 2.27SERC 96,071 1,745 1.82SPP 47,478 736 1.55TVA 47,633 2,309 4.85 98SECTION 4: DEVELOPING ECONOMIC TRANSMISSION OVERLAYS current installed capacity by nearly an order of magnitude. Transmission issues are already limiting wind energy development in some regions, so it is a near the much higher amounts of wind generation represented in the Eastern Wind This section describes the methodology and results of the transmission BACKGROUNDdeveloped through a planning process that had two basic objectives: (1) to reliability of the bulk power system in the face of growing demand. By building transmission facilities to interconnect with neighbors, capacity resources could be shared in emergencies, reducing the amount of excess capacity an individual utility must maintain to serve load reliably. Opportunities for economic Because it is primarily a source of energy, not capacity, wind generation does system with a major facility out of service. The status of conventional generating units during these periods is usually a given. With large amounts of wind generation, the disposition of other conventional generating units may not be so easily ascertained; in addition, high amounts of wind generation are likely in off-peak hours or seasons that might not be of special interest for reliability issues.A transmission planning method based on economics has been developed, transmission organization (RTO) for insight into transmission needs for 99to develop conceptual interregional transmission plans required for a 5% wind energy scenario and a 20% wind energy scenario. evaluate the transmission that would be needed to facilitate 20% and 30% wind delivery across a large geographical area, energy-based regional transmission planning is necessary to incorporate comprehensive economic assessment using production-cost simulations. By linking the markets across the entire Eastern regional transmission plan could outweigh its cost. Because the JCSP also focused on a 20% scenario, the results from that effort of the high-quality wind resources in the Great Plains and Upper Midwest. A similar level of regional detail was not available for other parts of the Eastern The transmission development methodology is a sequential process that focuses on a snapshot of a single future year. The steps in the process follow:
: 1. 2. Determining what generation capacity would be necessary to reliably This is accomplished through a formal generation expansion process that begins with the present and ends in the target year. Wind generation is accounted for by assigning an estimated capacity value, which is capacity for planning purposes. The expansion program then considers the new generation that must be built to meet regional planning margin requirements given the growth in loads and possible retirements of existing generators. Projected capital and operating costs over the planning horizon are used to optimize the expansion by minimizing total costs while maintaining resource adequacy.  
: 1. 2. Determining what generation capacity would be necessary to reliably This is accomplished through a formal generation expansion process that begins with the present and ends in the target year. Wind generation is accounted for by assigning an estimated capacity value, which is capacity for planning purposes. The expansion program then considers the new generation that must be built to meet regional planning margin requirements given the growth in loads and possible retirements of existing generators. Projected capital and operating costs over the planning horizon are used to optimize the expansion by minimizing total costs while maintaining resource adequacy.  


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: a. A "copper sheet" case, where limits on all transmission facilities energy in any hour is the same across the entire system.
: a. A "copper sheet" case, where limits on all transmission facilities energy in any hour is the same across the entire system.
: b. A constrained case, where transmission limits are applied. Congestion will result in unequal prices caused by less-than-generation.
: b. A constrained case, where transmission limits are applied. Congestion will result in unequal prices caused by less-than-generation.
: 4. Comparing results of the copper sheet and constrained cases. Costs of congestion across major transmission lines and interfaces are totaled for the annual period.  
: 4. Comparing results of the copper sheet and constrained cases. Costs of congestion across major transmission lines and interfaces are totaled for the annual period.
: 5. Using the accumulated congestion charges as a guide for developing new transmission.
: 5. Using the accumulated congestion charges as a guide for developing new transmission.
generation scenarios, and this process is described in the following sections.APPLICATION OF EXPANSION METHODOLOGYTo begin the transmission development process, the study team used the Electric Generation Expansion Analysis System (EGEAS) tools described in Section 3 to conduct a regional capacity expansion analysis for each wind scenario. The objective was to maintain an approximate 15% reserve margin across the Eastern production and regional load data, wind generation was assigned a uniform capacity value of 20% in the EGEAS runs. EGEAS GENERATION EXPANSIONand will not depend on capacity in other regions for resource adequacy needs. served and no more, whether located internally or not.Figure 4-1 shows the nameplate capacity expansions required to meet the resource adequacy needs for each region. The information is, however, for 101The effect of wind on the capacity expansion model can be seen by comparing the three 20% wind energy scenarios to the 30% wind energy scenario. The added energy produced from the wind resources tends to be more competitive with the base-load generation in the off-peak hours. As a result, when increased wind resources are forced into the expansion model, the economic result is to remove base-load capacity (e.g., coal and nuclear) from the expansion and leave the more  Notes: CC = combined cycle; CT = combustion turbine; IGCC = integrated gas combined cycle; IGCC/Seq = integrated gas combined cycle with sequestration; CC/Seq = combined cycle with sequestration; RET Coal = coal plant retirements; Replacement CC = replacement combined cycle; DR = demand response.Figure 4-1. Capacity expansion by scenarioSITING OF CAPACITYThe resources forecast from the expansion model for each of the scenarios are a philosophy- and rule-based methodology, and industry expertise, to site the forecast generation. data (AWS Truewind 2009). The thermal capacity was locally sited at various 102energy production) but others require support from wind located in external regions.Areas that meet the target energy on a regional basis, by scenario, are as follows:Areas with less than the target amounts by scenario include the following:RESULTSScenarios 1 through 4, respectively. With the same 20% wind penetration level, Scenarios 1, 2, and 3 have exactly the same thermal generation capacity and siting locations. With the increased 30% wind energy penetration in Scenario 4, 103Figure 4-2. Scenario 1 installed capacity sitesFigure 4-3. Scenario 2 installed capacity sites 104 Figure 4-4. Scenario 3 installed capacity sites 4-5. Scenario 4 installed capacity sites 105TRANSMISSION OVERLAY DEVELOPMENT The following sections describe the interim steps of the transmission expansion methodology along with results for the study scenarios.
generation scenarios, and this process is described in the following sections.APPLICATION OF EXPANSION METHODOLOGYTo begin the transmission development process, the study team used the Electric Generation Expansion Analysis System (EGEAS) tools described in Section 3 to conduct a regional capacity expansion analysis for each wind scenario. The objective was to maintain an approximate 15% reserve margin across the Eastern production and regional load data, wind generation was assigned a uniform capacity value of 20% in the EGEAS runs. EGEAS GENERATION EXPANSIONand will not depend on capacity in other regions for resource adequacy needs. served and no more, whether located internally or not.Figure 4-1 shows the nameplate capacity expansions required to meet the resource adequacy needs for each region. The information is, however, for 101The effect of wind on the capacity expansion model can be seen by comparing the three 20% wind energy scenarios to the 30% wind energy scenario. The added energy produced from the wind resources tends to be more competitive with the base-load generation in the off-peak hours. As a result, when increased wind resources are forced into the expansion model, the economic result is to remove base-load capacity (e.g., coal and nuclear) from the expansion and leave the more  Notes: CC = combined cycle; CT = combustion turbine; IGCC = integrated gas combined cycle; IGCC/Seq = integrated gas combined cycle with sequestration; CC/Seq = combined cycle with sequestration; RET Coal = coal plant retirements; Replacement CC = replacement combined cycle; DR = demand response.Figure 4-1. Capacity expansion by scenarioSITING OF CAPACITYThe resources forecast from the expansion model for each of the scenarios are a philosophy- and rule-based methodology, and industry expertise, to site the forecast generation. data (AWS Truewind 2009). The thermal capacity was locally sited at various 102energy production) but others require support from wind located in external regions.Areas that meet the target energy on a regional basis, by scenario, are as follows:Areas with less than the target amounts by scenario include the following:RESULTSScenarios 1 through 4, respectively. With the same 20% wind penetration level, Scenarios 1, 2, and 3 have exactly the same thermal generation capacity and siting locations. With the increased 30% wind energy penetration in Scenario 4, 103Figure 4-2. Scenario 1 installed capacity sitesFigure 4-3. Scenario 2 installed capacity sites 104 Figure 4-4. Scenario 3 installed capacity sites 4-5. Scenario 4 installed capacity sites 105TRANSMISSION OVERLAY DEVELOPMENT The following sections describe the interim steps of the transmission expansion methodology along with results for the study scenarios.
Line 699: Line 699:
121Figure 4-20. An example showing 800-kV HVDC lines (black) tied by 765-kV lines (green) and underlying 345-kV lines (red) the initial 5,700 MW because there are fewer lines to distribute the contingency. disturbances or generator outages. The overlay has ample capacity to back up disturbances, the severity of the AC disturbances in the area of the contingency and elsewhere would be considerably reduced compared to the case with no largest generator. that would tap the lines in the middle of the line. Three terminal lines would reduce the area affected by a contingency and reduce the impact of a contingency 122contingency. The AC system would not have to deliver power over such long lines at the third terminal. The conceptual design of the overlay would be more robust than that of the example (an example of an overlay with three terminal  Figure 4-21. A postcontingency example showing ve 800-kV HVDC lines  (black) example tied by 765-kV lines (green) and underlying 345-kV lines (red)system for the example. The impact of a contingency is expected to reduce with distance from the area in which it occurs.  
121Figure 4-20. An example showing 800-kV HVDC lines (black) tied by 765-kV lines (green) and underlying 345-kV lines (red) the initial 5,700 MW because there are fewer lines to distribute the contingency. disturbances or generator outages. The overlay has ample capacity to back up disturbances, the severity of the AC disturbances in the area of the contingency and elsewhere would be considerably reduced compared to the case with no largest generator. that would tap the lines in the middle of the line. Three terminal lines would reduce the area affected by a contingency and reduce the impact of a contingency 122contingency. The AC system would not have to deliver power over such long lines at the third terminal. The conceptual design of the overlay would be more robust than that of the example (an example of an overlay with three terminal  Figure 4-21. A postcontingency example showing ve 800-kV HVDC lines  (black) example tied by 765-kV lines (green) and underlying 345-kV lines (red)system for the example. The impact of a contingency is expected to reduce with distance from the area in which it occurs.  


123Figure 4-22. An example of the assumed distribution of the ows on the underlying AC systemThe overlay is designed not to have an impact greater than 1,500 MW on any  
123Figure 4-22. An example of the assumed distribution of the ows on the underlying AC systemThe overlay is designed not to have an impact greater than 1,500 MW on any
: 1.  
: 1.
: 2. 3. The rating of the underlying AC systems to be able to withstand a contingency in its area. A rating of 1,500 MW is assumed for these examples.into a ready-for-construction transmission plan.
: 2. 3. The rating of the underlying AC systems to be able to withstand a contingency in its area. A rating of 1,500 MW is assumed for these examples.into a ready-for-construction transmission plan.
124SECTION 5: POWER SYSTEM REGULATION AND BALANCING WITH SIGNIFICANT WIND GENERATIONMatching the supply of electrical energy to the demand for electricity, over time frames ranging from seconds to decades, is a fundamental building block for maintaining resource adequacy in the bulk power system. Wind generation introduces additional variability and uncertainty that make the general task incrementally more challenging.POWER SYSTEM OPERATION AND CONTROLPower system operation is near the real-time end of the spectrum of the operating time horizon. To maintain system reliability in day-to-day operations, several functions are necessary. These functions have traditionally been performed by individual utility "control areas," and now can be performed by one or several entities in a balancing authority that have been approved by the North American Electric Reliability Corporation (NERC). These reliability functions can be categorized by different names and are sometimes broken down  
124SECTION 5: POWER SYSTEM REGULATION AND BALANCING WITH SIGNIFICANT WIND GENERATIONMatching the supply of electrical energy to the demand for electricity, over time frames ranging from seconds to decades, is a fundamental building block for maintaining resource adequacy in the bulk power system. Wind generation introduces additional variability and uncertainty that make the general task incrementally more challenging.POWER SYSTEM OPERATION AND CONTROLPower system operation is near the real-time end of the spectrum of the operating time horizon. To maintain system reliability in day-to-day operations, several functions are necessary. These functions have traditionally been performed by individual utility "control areas," and now can be performed by one or several entities in a balancing authority that have been approved by the North American Electric Reliability Corporation (NERC). These reliability functions can be categorized by different names and are sometimes broken down
: 1. Scheduling (unit commitment), system control, and dispatch
: 1. Scheduling (unit commitment), system control, and dispatch
: 2. Reactive supply and voltage control from generation
: 2. Reactive supply and voltage control from generation
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show that long-distance (and high-capacity) transmission can assist smaller balancing areas with wind integration, allowing penetration levels that would not otherwise be feasible. Furthermore, all scenarios, including the Reference Case, made use of major transmission upgrades  
show that long-distance (and high-capacity) transmission can assist smaller balancing areas with wind integration, allowing penetration levels that would not otherwise be feasible. Furthermore, all scenarios, including the Reference Case, made use of major transmission upgrades  


wind integration.  
wind integration.
: 3.
: 3.
large quantities of remote wind energy to urban markets? Long-distance  
large quantities of remote wind energy to urban markets? Long-distance  
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operations, contributes substantially to integrating large amounts of wind  
operations, contributes substantially to integrating large amounts of wind  


distance transmission has other value in terms of system robustness that  
distance transmission has other value in terms of system robustness that
: 4.  
: 4.  


Truewind 2009) shows that the higher quality winds in the Great Plains have capacity factors that are about 7%-9% higher than onshore wind resources near the high-load urban centers in the East. Offshore plants have capacity factors on par with Great Plains resources but the cost of energy is higher because capital costs are higher.  
Truewind 2009) shows that the higher quality winds in the Great Plains have capacity factors that are about 7%-9% higher than onshore wind resources near the high-load urban centers in the East. Offshore plants have capacity factors on par with Great Plains resources but the cost of energy is higher because capital costs are higher.
: 5. How much does geographical diversity, or spreading the wind out substantially.  
: 5. How much does geographical diversity, or spreading the wind out substantially.
: 6. wind generation, forecasting will play a key role in keeping energy while maintaining system security.
: 6. wind generation, forecasting will play a key role in keeping energy while maintaining system security.
226 7. wind variability and uncertainty management? This and other recent studies (see Bibliography) reinforce the concept that large operating with adequate transmission, are the most effective measures for managing wind generation.  
226 7. wind variability and uncertainty management? This and other recent studies (see Bibliography) reinforce the concept that large operating with adequate transmission, are the most effective measures for managing wind generation.
: 8. How does wind generation capacity value affect supply resource adequacy? Wind generation can contribute to system adequacy, and additional transmission can enhance that contribution.
: 8. How does wind generation capacity value affect supply resource adequacy? Wind generation can contribute to system adequacy, and additional transmission can enhance that contribution.
The scenarios developed for this study do not in any way constitute a plan; instead, they give a top-down, high-level view of four different 2024 futures. The transition over time from the current state of the bulk power system to any one of the scenarios would require much more technical and economic evaluation, underlying transmission systems. A more thorough evaluation of the sensitivity of the results from this study to changes in assumptions or scenarios would also RECOMMENDATIONS FOR FUTURE STUDYwas driven primarily through economics-based transmission expansion planning, resource adequacy studies, and hourly modeling simulations. were not studied or were represented approximately or by means of best engineering judgments.
The scenarios developed for this study do not in any way constitute a plan; instead, they give a top-down, high-level view of four different 2024 futures. The transition over time from the current state of the bulk power system to any one of the scenarios would require much more technical and economic evaluation, underlying transmission systems. A more thorough evaluation of the sensitivity of the results from this study to changes in assumptions or scenarios would also RECOMMENDATIONS FOR FUTURE STUDYwas driven primarily through economics-based transmission expansion planning, resource adequacy studies, and hourly modeling simulations. were not studied or were represented approximately or by means of best engineering judgments.
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: 14. ABSTRACT (Maximum 200 Words)
: 14. ABSTRACT (Maximum 200 Words)
The Eastern Wind Integration and Transmission Study was designed to answer questions posed by a variety of stakeholders about a range of important and contemporary technical issues related to a 20% wind energy scenario for the large portion of the electric demand in the Eastern Interconnection.
The Eastern Wind Integration and Transmission Study was designed to answer questions posed by a variety of stakeholders about a range of important and contemporary technical issues related to a 20% wind energy scenario for the large portion of the electric demand in the Eastern Interconnection.
: 15. SUBJECT TERMS wind energy integration: wind energy interconnection; transmission study; utility grid;  
: 15. SUBJECT TERMS wind energy integration: wind energy interconnection; transmission study; utility grid;
: 16. SECURITY CLASSIFICATION OF:
: 16. SECURITY CLASSIFICATION OF:
: 17. LIMITATION OF ABSTRACT UL 18. NUMBER OF PAG ES  19a. NAME OF RESPONSIBLE PERSON
: 17. LIMITATION OF ABSTRACT UL 18. NUMBER OF PAG ES  19a. NAME OF RESPONSIBLE PERSON

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PREPARED FOR:The National Renewable Energy LaboratoryA national laboratory of the U.S. Department of EnergyPREPARED BY:EnerNex CorporationJANUARY 2010 EASTERN WIND INTEGRATION AND TRANSMISSION STUDYPIX #16204 2

3EASTERN WIND INTEGRATION AND TRANSMISSION STUDYJanuary 2010Prepared for NREL by: EnerNex Corporation Knoxville, TennesseeNREL Technical Monitor: David CorbusPrepared under Subcontract No. AAM-8-88513-01Subcontract Report NREL/SR-550-47078National Renewable Energy Laboratory1617 Cole Boulevard, Golden, Colorado 80401NREL is a national laboratory of the U.S. Department of EnergyO~ce of Energy E~ciency and Renewable EnergyOperated by the Alliance for Sustainable Energy, LLCContract No. DE-AC36-08GO28308 4NOTICEThis report was prepared as an account of work sponsored by an agency of the United States government. Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately owned rights. Reference trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation, or favoring by the United States government or any agency thereof. The views and opinions of authors expressed herein agency thereof.Available electronically at http://www.osti.gov/bridgeAvailable for a processing fee to U.S. Department of Energyand its contractors, in paper, from:U.S. Department of EnergyP.O. Box 62 Oak Ridge, TN 37831-0062 phone: 865.576.8401 fax: 865.576.5728email: mailto:reports@adonis.osti.govAvailable for sale to the public, in paper, from:U.S. Department of Commerce5285 Port Royal Roadphone: 800.553.6847 fax: 703.605.6900email: orders@ntis.fedworld.govonline ordering: http://www.ntis.gov/ordering.htmPrinted on paper containing at least 50% wastepaper, including 20%

postconsumer waste 5TABLE OF CONTENTSPREFACE .........................................................................................................................

15 ACKNOWLEDGEMENTS

............................................................................................

16 .......................................................................

18 ..............................................................................................

22 About the Study

...............................................................................................

22 Scenario Development and Analysis

............................................................

24 Key Study Findings

.........................................................................................

27 Scenario Costs

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29 Study Methodology

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31 ...................................................................................................

35 Transmission Requirements

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35 .............................................................................

40 Reserve Requirements

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41 Production-Cost Modeling Results

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44 ...................................................................................

45 Carbon Sensitivity Analysis

...........................................................................

47 Contributions to Resource Adequacy

...........................................................

51 ............................................................................

55 ..............................................................................................

61 Project Objectives

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62

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62 Overview of Project Tasks

..............................................................................

63 Organization of this Report

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64 Wind Scenario Development

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66 ......................................................................................................

66 Description of Mesoscale Database

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66 Scenario Development Process

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70 Scenario Descriptions

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70 Regional wind Capacity and Energy

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71 Scenario Details

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73 Analytical Methodology: Data, Models, and Tools

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81 Model Development

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83 Tools ...................................................................................................................

87 Assumptions

.....................................................................................................

90 Developing Economic Transmission Overlays

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98 Background

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98 Application of Expansion Methodology

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100 Analysis ...........................................................................................................117 ....................................................

120 Power System Regulation and Balancing

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124 6 Power System Operation and Control

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124 Effects of Wind Generation on Power System Control

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134 .........137 Summary .........................................................................................................

153

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156 ..........................................................

156 Analytical Approach

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157 Results .............................................................................................................

159 Scenario Analysis

...........................................................................................

164 Sensitivity Analysis

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180Wind Generation Contributions to Resource

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194 Adequacy and Planning Margin

Background

.....................................................................................................

194 Analytical Approach

.....................................................................................

194 Results .............................................................................................................

202 Analysis ...........................................................................................................

205

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207 Notes on the Analytical Methodology

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207 Total Costs

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209

...........................................................................211 Findings, Conclusions, and Recommendations

................................

214 Key Findings and Conclusions

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214 Summary .........................................................................................................

225 Recommendations for Future Study

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226...................................................................................................................

231

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238LIST OF FIGURESFigure 1. NERC synchronous interconnections

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23Figure 2. Summary of installed wind generation

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26 capacity by operating region for each scenarioFigure 3. Comparison of scenario costs

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30Figure 4. Study process

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32Figure 5. Assumed operational structure for the

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34Figure 6. Scenario2, annual generation differences between

...................................

36 unconstrained case and constrained caseFigure 7. Scenario 2, annual generation weighted locational

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37 marginal price (LMP) for constrained case ..............................................

38 each study scenarioFigure 9. Regulating reserve requirements by region and scenario

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42Figure 10. Annual production-cost comparison

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45 7 .......................................................

46Figure 12. Generation expansion for the Scenario 2

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48 carbon sensitivity caseFigure13. Generation expansion by scenario, including

..........................................

49 the carbon sensitivity caseFigure 14. Carbon emissions for different scenarios

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49Figure 15. Generation utilization by unit and fuel

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50 type for Scenario 2 and carbon sensitivity caseFigure 16. Comparison of generation-weighted LMP by

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50 region for Scenario 2 and carbon sensitivity caseFigure 17. Present value of accumulated costs for base

...........................................

51 scenarios and carbon sensitivityFigure 18. LOLE/ELCC results for high penetration

...............................................

54 scenarios, with and without transmission overlaysFigure 2-1. LCOE for all wind facilities in database

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68Figure 2-2. Capacity factor for all wind in database

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69Figure 2-3. Aggregate capacity factor for all wind in database

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69 ..........................................................................

71Figure 2-5. Annual energy production by region

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72Figure 2-6. Nameplate capacity by region

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73Figure 2-7. Wind plant size and location for Reference Case

..................................

74 ..................................................................

75 .......................................

75 ................................................................

76 ......................................

77 ................................................................

78 .....................................

78 ...............................................................

79 .....................................

80Figure 3-1. Overview of the study process

.................................................................

82Figure 3-2. Study area

....................................................................................................

93Figure 4-1. Capacity expansion by scenario

.............................................................

101Figure 4-2. Scenario 1 installed capacity sites

..........................................................

103Figure 4-3. Scenario 2 installed capacity sites

..........................................................

103Figure 4-4. Scenario 3 installed capacity sites

..........................................................

104Figure 4-5. Scenario 4 installed capacity sites

..........................................................

104Figure 4-6. Scenario 2 annual generation-weighted

................................................

105 LMP for Scenario 2Figure 4-7. Scenario 2 generation difference between

............................................

106 unconstrained case and constrained caseFigure 4-8. Scenario 2 interface annual energy difference

.....................................

107 between unconstrained case and constrained caseFigure 4-9. Transmission and substation costs per megawatt-mile

......................

109 .............................................110 8 ...........................................111...............................112...............................112...............................113...............................113Figure 4-16. Scenario 1 annual generation-weighted

..............................................118 LMP comparison118Figure 4-17. Scenario 2 annual generation-weighted

..............................................118 LMP comparisonFigure 4-18. Scenario 3 annual generation-weighted

..............................................119 LMP comparisonFigure 4-19. Scenario 3 annual generation-weighted

..............................................119 LMP comparison .........................................

121 .............................

122Figure 4-22. An example of the assumed distribution of

.......................................

123Figure 5-1. NERC reliability regions and balancing authorities

...........................

127 as of January 2005 and August 2007Figure 5-2. U.S. RTOs

...................................................................................................

128Figure 5-3. Depiction of regulation and load-following

.........................................

130 characteristics of demand ...........................

135 groups of wind generation. The 500MW scenario is part of the 5,000-MW scenario, which is part of the 15,000-MW scenario, and so onFigure 5-5. Ten-minute variability of load and net load for

...................................

136 .....................................

138 ............................

144 forecasts of load and wind generationFigure 5-8. Errors in short-term forecasts of load and wind

..................................

144 generation; load error is assumed to be zero in the mathematical procedure .............................

145 generation forecast errors as a function of average hourly productionFigure 5-10. Standard deviation of 1-hour persistence forecast

............................

147 error for PJM in Reference CaseFigure 5-11. Distributions of hourly regulating reserve requirements

.................

152 for PJM Scenario 3, for load only (ideal wind generation) and load net of wind generation 9Figure 6-1. Wind energy penetration levels by region using

.................................

160Figure 6-2. Annual average variable spinning reserve using

................................

161 ..............................................

162Figure 6-4. Annual steam turbine coal generation

..................................................

163Figure 6-5. Annual combined cycle and combustion

..............................................

164Figure 6-6. Annual APC comparison for actual cases

.............................................

165 Figure 6-7. Wind integration costs

.............................................................................

166Figure 6-8. Wind integration costs ($/MWh of annual

.........................................

167 wind energy in 2024)Figure 6-9. Annual wind energy input summary for Scenario 1

..........................

168 Figure 6-10. Annual wind curtailment summary using

.........................................

169Figure 6-11. Annual wind curtailment summary using

.........................................

169Figure 6-12. Annual wind curtailment summary using

.........................................

170Figure 6-13. Annual generation energy by fuel type using

....................................

171Figure 6-14. Annual generation energy by fuel type using

....................................

171Figure 6-15. Annual generation energy by fuel type using

....................................

172Figure 6-16. Annual generation energy by fuel type using

....................................

172Figure 6-17. Annual generation energy by fuel type using

....................................

173Figure 6-18. Annual generation energy by fuel type using

....................................

173Figure 6-19. Change in annual generation from ideal to

........................................

174Figure 6-20. Change in annual generation from ideal to

........................................

175Figure 6-21. Change in annual generation from ideal to

........................................

175Figure 6-22. Annual generation-weighted LMPs using

.........................................

176Figure 6-23. Annual generation-weighted LMPs using

.........................................

177Figure 6-24. Annual generation-weighted LMPs using

.........................................

177 10Figure 6-25. Annual regional transaction energy using

.........................................

178Figure 6-26. Annual regional transaction energy using

.........................................

179Figure 6-27. Annual regional transaction energy using

.........................................

179 .................................................

181Figure 6-29. APCs with 2005 hourly wind and load patterns

...............................

184 ......................................

185 wind and load patternsFigure 6-31. Annual generation-weighted LMPs with

...........................................

185 2005 hourly wind and load patternsFigure 6-32. Annual generation-weighted LMP comparison

................................

187Figure 6-33. Capacity expansion by scenario including

.........................................

188 carbon sensitivity, 2008-2024Figure 6-34. Carbon impact of modeled scenarios

..................................................

189Figure 6-35. Cost impact of modeled scenarios

.......................................................

189Figure 6-36. Forecast generation locations for sensitivity

......................................

190 to scenario 2Figure 6-37. Annual generation production by fuel type with

..............................

191 2005 hourly wind and load patternsFigure 6-38. Annual generation energy changes from ideal

..................................

192 case to actual caseFigure 6-39. Annual generation-weighted LMP comparison

................................

192Figure 7-1. ELCC example system with and without resource

.............................

195Figure 7-2. ELCC example system at the same LOLE

............................................

195Figure 7-3. Scenario 1, existing transmission system

..............................................

198 August interface limits (MW)Figure 7-4. Scenario 2, existing transmission system

..............................................

198 August interface limits (MW)Figure 7-5. Scenario 3, existing transmission system

..............................................

199 August interface limits (MW)Figure 7-6. Scenario 4, existing transmission system

..............................................

199 August interface limits (MW)Figure 7-7. Scenario 1, conceptual transmission overlay

.......................................

200 August interface limits (MW)Figure 7-8. Scenario 2, conceptual transmission overlay

.......................................

200 August interface limits (MW)Figure 7-9. Scenario 3, conceptual transmission overlay

.......................................

201 August interface limits (MW)Figure 7-10. Scenario 4, conceptual transmission overlay

.....................................

201 August interface limits (MW)Figure 7-11. ELCC results for existing and overlay transmission

.........................

203 .................

204 11Figure 8-1. Flow diagram for study analytical methodology

................................

208Figure 8-2. Costs by scenario

......................................................................................211Figure 9-1. Comparison of production simulation results

.....................................

219 (integration cost) for base unit-commitment algorithm and more sophisticated "bid-logic" approachFigure 9-2. Comparison of LMPs for hurdle rate sensitivity

.................................

220Figure 9-3. Scenario 2, carbon case generation expansion

.....................................

221Figure 9-4. Generation expansion by scenario, 2008-2024

.....................................

221Figure 9-5. Carbon emission comparison

.................................................................

222Figure 9-6. Change in generation for carbon sensitivity

........................................

223Figure 9-7. Change in LMP for carbon sensitivity

...................................................

223Figure 9-8. Scenario cost comparisons

......................................................................

224LIST OF TABLESTable 1. Total and Offshore Wind in the Scenarios

....................................................

26Table 2. Transmission Cost Assumptions

...................................................................

39Table 3. Estimated Line Mileage by Scenario

.............................................................

39Table 4. Estimated Costs by Scenario (US $2009, millions)

......................................

39 ........................

53 Generation Capacity (Nameplate Wind in Megawatts)Table 2-1. LCOE Economic Assumptions (US $2009)

...............................................

67Table 2-2. Summary of Energy by Region for Scenarios

...........................................

72Table 2-3. Reference Case, 6% of 2024 Load Requirements

......................................

73 ....................................

74 ....................................................

76 ..................................

77 ...............................

79Table 3-1. Summary of Demand and Energy Assumptions Used

...........................

84 Table 3-2. Status Categories Applied to all Units Within the Database

.................

85Table 3-3. Summary of Active Generation Capacity by Region

..............................

86 (Nameplate Capacities in Megawatts)Table 3-4. Summary of Planned Generation Capacity by Region

...........................

86 through 2024 (Nameplate Capacities in MegawattsTable 3-5. Summary of Generator Retirement Capacity by

.....................................

87 Region through 2024 (Nameplate Capacities in Megawatts)Table 3-6. Average Fuel Costs Modeled by System

...................................................

94Table 3-7. Target Reserve Margins by Region

............................................................

96 ........................

97Table 3-9. Modeled Penetration of Demand Response by Region

.........................

97 (as percent age of peak) .............................................

108 Energy DifferenceTable 4-2. Annual APC Savings for Each Scenario (US $2024, millions)

..............

109 12Table 4-3. Cost per Mile Assumption (US $2024, millions)

....................................114Table 4-4. Estimated Line Mileage Summary (Miles)

.............................................115Table 4-5. Estimated Cost Summary (US $2024, millions)

.....................................115...............................115 ..................................116 for Scenario 1 ..................................116 for Scenario 2 ..................................117 for Scenario 3 ................................117 for Scenario 4Table 4-11. Wind Curtailment Summary

..................................................................

120Table 5-1. Excerpts from NERC Glossary of Terms Related to

..............................

129 Operating ReservesTable 5-2. 2009 CPS2 bounds for Selected Eastern

..................................................

132Table 5-3. Mapping of Reserve Components in Categories for

.............................

140 Production SimulationsTable 5-4. Contingency Reserve Requirements by Operation

...............................

141 Region for 2024Table 5-5. Summary of Reserve Methodology for Study Scenarios

......................

147Table 5-6. Example Application of Reserve Methodology

.....................................

149 to Hourly DataTable 5-7. Adjustment of Spinning Reserve for Reduction

....................................

151 in Wind GenerationTable 5-8. Spinning Reserve Requirements by for the

............................................

152 Reference CaseTable 5-9. Spinning Reserve Requirements for Scenario 1

.....................................

152Table 5-10. Spinning Reserve Requirements for Scenario 2

...................................

153Table 5-11. Spinning Reserve Requirements for Scenario 3

...................................

153Table 5-12. Spinning Reserve Requirements for Scenario 4

...................................

153 ....................

162 .......................................................................

163Table 6-3. Wind Curtailment Comparison for Sensitivity

......................................

182 Case 1, Non Must-RunTable 6-4. Wind Curtailment Comparison for Sensitivity

......................................

182 Case 2, Copper SheetTable 6-5. Wind Curtailment Comparison for Sensitivity

......................................

183 Case 3, Wind Energy CreditTable 6-6. Annual Generation Energy Summary by Fuel Type

.............................

183Table 6-7. Scenario 1, Hurdle Rate Sensitivity Results

............................................

186Table 6-8. Scenario 3, Hurdle Rate Sensitivity Results

............................................

186 .................

193 13Table 7-1. Reliability zones for LOLE analysis with installed wind

.....................

197 Generation Capacity (Nameplate wind in megawatts) ............................................

202Table 7-3. ELCC Results for Existing Transmission System

...................................

202Table 7-4. ELCC Results for Overlay Transmission System

...................................

203 ................................................................................

205Table 8-1. Assumptions used in Scenario Cost Calculations

.................................

209 14 15PREFACEeffort that spanned two and one-half years. The study team began by modeling wind detailed wind integration study and top-down transmission analysis. The study resulted in information that can be used to guide future work. A number of other studies have already examined similar wind integration issues, but the breadth and depth of the which looked at considerably smaller geographic footprints, focused almost exclusively expanding the study area and including conceptual transmission overlays.Just a few years ago, 5% wind energy penetration was a lofty goal, and to some the idea of integrating 20% wind by 2024 might seem a bit optimistic. And yet, we know from and that planning for that change is critically important. Because building transmission capacity takes much longer than installing wind plants, there is a sense of urgency to the future, but to be as objective as possible while conducting a technical study of future Energy's (DOE) National Renewable Energy Laboratory (NREL) convened a Technical Review Committee (TRC) composed of regional electric reliability council representatives, expert reviewers, transmission planners, utility administrators, and wind industry representatives. Over a period of 14 months while the study was in progress, the TRC held 6 full-day meetings along with numerous Webinars and conference calls to review study progress; comment on study inputs, methods, and assumptions; assist with collecting data; and review drafts of the study report.Planning for the expansion of the electrical grid is a process that requires an immense amount of study, dialogue among regional organizations, development of technical methodologies, and communication and coordination among a multitude of important stakeholders. Keeping abreast of the changes is challenging because there are so many be helpful to all those involved in the planning of the future electrical grid and form a foundation for future studies.David CorbusSenior Engineer, NREL 16ACKNOWLEDGMENTSThe National Renewable Energy Laboratory (NREL) thanks the U.S. Department (TRC) for such great participation and input; and the study team of EnerNex along with Michael Brower of AWS Truewind, who conducted the wind modeling study. PROJECT MANAGER David Corbus NRELSTUDY TEAM EnerNex Corporation:

Jack King, Tom Mousseau, and Robert Zavadil John Lawhorn, Dale Osborn, and JT SmithTECHNICAL REVIEW COMMITTEE Mark Ahlstrom WindLogicsJared Alholinna CapX 2020 (Great River Energy)

Steve Beuning Xcel Energy Clifton Black Southern Company Jay Caspary Southwest Power Pool (SPP)

Charlton Clark DOE Cathy Cole Michigan Public Service Commission David Corbus (co-chair)

NRELDan Fredrickson Mid-Continent Area Power Pool (MAPP)

Michael Goggin American Wind Energy Association (AWEA)

Sasan Jalai (observer)

Federal Energy Regulatory Commission (FERC)Brendan Kirby NRELMichael Milligan NRELJeff Mitchell North American Electric Reliability Corporation (NERC; ReliabilityFirst)

Nathan Mitchell American Public Power Association (APPA) (Wisconsin Public Service Commission) 17 Mark O'Malley University College Dublin Dick Piwko GE EnergyMatt Schuerger (co-chair)

NREL Richard Sedano Regulatory Assistance ProjectJason Smith SPPand Mark Schroder (Purple Sage Design) for designing this Executive Summary and Project Overview and the full report.

18ABBREVIATIONS AND ACRONYMS ACE area control error AGC automatic generation control AMRN Ameren APC adjusted production costATC American Transmission Company BAA balancing authority area BAAL balancing authority ACE limit CAPX 2020 (GRE)

Capital Expansion Plan for 2020 (Great River Energy)CC combined cycle CC/Seq combined cycle with sequestration CO2 carbon dioxide CPM control performance measure CPS1 Control Performance Standard 1 CPS2 Control Performance Standard 2 CT combustion turbine DCS Disturbance Control Standard DOE U.S. Department of Energy DR demand response ECAR East Central Area Reliability Coordinating Agreement (a former NERC reliability region)EGEAS Electric Generation Expansion Analysis System ELCC effective load-carrying capability EMS energy management system EPJM East PJM 19 Group Multi-Regional Modeling Working Group ERCOT Electric Reliability Council of Texas ERO Electric Reliability Organization ES&D electricity supply and demandEUE expected unserved energy FERC Federal Energy Regulatory Commission FRCC Florida Reliability Coordinating Council GE-MARS GE Energy's Multi-Area Reliability Simulation program GW gigawatt GWh gigawatt-hour

Hz hertz sequestration JCSP Joint Coordinated System Plan (2008) km kilometer kW kilowatt kWh kilowatt-hour LCOE levelized cost of energy LMP locational marginal price LOLE loss of load expectation LOLP loss of load probability m meter 20 MACC Mid-Atlantic Area Council (a former NERC reliability region)MAPP Mid-Continent Area Power Pool MHEB Manitoba Hydro Electric Board MBtu million British thermal units MRO Midwest Reliability Organization MW megawatt MWh megawatt-hour NBSO New Brunswick Security Operator NERC North American Electric Reliability Corporation NPCC Northeast Power Coordinating Council NREL National Renewable Energy Laboratory NWP numerical weather predictionO&M operations and maintenance PTC production tax credit REC renewable energy credit RET Coal coal plant retirement RFC ReliabilityFirst Corporation RGOS Regional Generation Outlet Study RPS renewable portfolio standardRTO regional transmission organization or regional transmission operator SCUC security-constrained unit commitment SERC Southeastern Electric Reliability Council SLH single largest hazard SPP Southwest Power Pool SPS Southwestern Public Service ST steam turbine TRC Technical Review Committee TRE Texas Regional Entity 21TWh terawatt-hours UCTE Union for the Co-ordination of Transmission of Electricity W wattWAPA Western Area Power Administration WECC Western Electricity Coordinating Council 22EXECUTIVE

SUMMARY

The total installed capacity of wind generation in the United States surpassed projected for the foreseeable future. generation providing 20% of the electrical energy consumed in the United States by 2030 (EERE 2008). Developed through the collaborative efforts of a wide-ranging Report) takes a broad view of the electric power and wind energy industries. The 20% Report evaluates the requirements and outcomes in the areas of technology, manufacturing, transmission and integration, environmental impacts, and markets that would be necessary for reaching the 20% by 2030 target. scenario is unlikely to be realized with a business-as-usual approach, and that a major national commitment to clean, domestic energy sources with desirable environmental attributes would be required. The growth of domestic wind generation over the past decade has sharpened the focus on two questions: Can the electrical grid accommodate very high amounts of wind energy without jeopardizing security or degrading reliability? And, given that the nation's current transmission infrastructure is already constraining further amounts of wind energy be developed? The answers to these questions could hold the keys to determining how much of a role wind generation can play in the U.S. electrical energy supply mix.

ABOUT THE STUDYscale, and process. The study was designed to answer questions posed by a variety of stakeholders about a range of important and contemporary technical issues related to a 20% wind scenario for the large portion of the electric load 23through to the Atlantic coast, excluding most of the state of Texas.Figure 1. NERC synchronous interconnectionsNotes: NERC = North American Electric Reliability Corporation; WECC = Western Electricity Coordinating Council; TRE = Texas Regional Entity; ERCOT = Electric Reliability Council of Texas; MRO = Midwest Reliability Organization; SPP = Southwest Power Pool; NPCC = Northeast Power Coordinating Council; RFC = ReliabilityFirst Corporation; SERC = Southeastern Electric Reliability Council; FRCC = Florida Reliability Coordinating Counciloperational implications of adding up to 35% wind and solar energy penetration transmission system operators in collaboration with the European Commission. in Europe and at identifying arrangements that will make best use of the pan-delivered across Europe.

24in 2007, the study was designed to examine the operational impact of up to 20% to 30% wind energy penetration on the bulk power system in the Eastern SCENARIO DEVELOPMENT AND ANALYSIS To set an appropriate backdrop for addressing the key study questions, the high-penetration scenarios to represent different wind generation development wind energy equivalent to 20% of the projected annual electrical energy requirements in 2024; the fourth scenario increased the amount of wind energy to 30%. plants from the Eastern Wind Data Study database (see sidebar) were selected to reach the target energy level. The wind data consisted of hourly and 10-minute wind plant data for each of three years: 2004, 2005, and 2006. Wind plants were available in all geographic locations within the Eastern of the southeastern United States and Canada (because of limitations on the scope of work for the wind modeling). Approximately 4 GW of new Canadian renewable generation was modeled to cover imports of new Canadian wind and hydro to the northeast. A brief description of each scenario follows:Scenario 1, 20% penetration - High Capacity Factor, Onshore: Utilizes high-quality wind resources in the Great Plains, with other development in the eastern United States where good wind resources exist. Scenario 2, 20% penetration - Hybrid with Offshore: Some wind generation in the Great Plains is moved east. Some East Coast offshore development is included. Scenario 3, 20% penetration - Local with Aggressive Offshore: More wind generation is moved east toward load centers, necessitating broader use of offshore resources. The offshore wind assumptions represent an uppermost limit of what could be developed by 2024 under an aggressive technology-push scenario.The Eastern Wind Data StudyA precursor to EWITS known as the Eastern Wind Data Study (AWS Truewind 2009) identied more than 700 GW of potential future wind plant sites for the eastern United States. All the major analytical elements of EWITS relied on the time series wind generation production data synthesized in this earlier eort. The data cover three historical years-2004, 2005, and 2006-at high spatial (2-kilometer [km]) and temporal (10-minute) resolution. On- and oshore resources are included, along with wind resources for all states.

25What Are ISOs and RTOs?In the mid-1990s, independent system operators (ISOs) and regional transmission operators (RTOs) began forming to support the introduction of competition in wholesale power markets. Today, two-thirds of the population of the United States and more than one-half of the population of Canada obtain their electricity from transmission systems and organized wholesale electricity markets run by ISOs or RTOs. These entities ensure that the wholesale power markets in their regions operate e~ciently, treat all market participants fairly, give all transmission customers open access to the regional electric transmission system, and support the reliability of the bulk power system.Source: Adapted from IRC (2009).Scenario 4, 30% penetration - Aggressive On- and Offshore: Meeting the 30% energy penetration level uses a substantial amount of the higher quality wind resource in the NREL database. A large amount of offshore generation is needed to reach the target energy level.

The study team also developed a Reference Scenario to approximate the current state of wind development plus some expected level of near-term development guided by interconnection queues and state renewable portfolio standards (RPS). This scenario totaled about 6% of the total 2024 projected load requirements for Figure 2 depicts the installed capacity by regional entity (either or regional transmission operators Table 1 shows the contribution of total and offshore wind to the scenarios.

Supplying 20% of the electric energy requirements of the U.S. portion of the Eastern approximately 225,000 megawatts (MW) of wind generation capacity, which is about a tenfold increase above today's levels. To reach 30% energy from wind, the installed capacity would have to rise to 330,000 MW.

26Figure 2. Summary of installed wind generation capacity by operating region for each scenario (Notes: ISO-NE = New England Independent System Operator, MISO = Midwest ISO, NYISO = New York ISO, PJM = PJM Interconnection, SERC = Southeastern Electric Reliability Council, SPP = Southwest Power Pool, TVA - Tennessee Valley Authority)TABLE 1. TOTAL AND OFFSHORE WIND IN THE SCENARIOSRegionScenario 120% High Capacity Factor, OnshoreScenario 2 20% Hybrid with O~shore Scenario 3 20% Local, Aggressive O~shore Scenario 4 30% Aggressive On- and O~shore TOTAL ~MW Oshore (MW)Total (MW) Oshore (MW)Total (MW)Oshore (MW)Total (MW)Oshore (MW)MISO/MAPP a 94,808 69,444 46,255 95,046 SPP 91,843 86,666 50,958 94,576TVA 1,247 1,247 1,247 1,247 SERC 1,009 5,009 4,000 5,009 4,000 5,009 4,000PJM 22,669 33,192 5,000 78,736 39,780 93,736 54,780NYISO 7,742 16,507 2,620 23,167 9,280 23,167 9.280ISO-NE 4,291 13,837 5,000 24,927 11,040 24,927 11,040TOTAL 223,609 0 225,902 16,620 230,299 64,100 337,708 79,100 a MAPP stands for Mid-Continent Area Power Pool.

27KEY STUDY FINDINGS Wind generation required to produce 20% of the projected electrical in 2024Transmission concepts for delivering energy economically for each scenario (new transmission for each scenario is based on economic performance for the conditions outlined in that scenario) Economic sensitivity simulations of the hourly operation of the power transmission overlayThe contribution made by wind generation to resource adequacy and planning capacity margin. developed in close coordination with the TRC. Changes in the assumptions, such as the cost of various fuels, the impact of regulation and policy, or the technologies, or the very nature of the load itself (as in an aggressive plug-in impact on the results.New transmission will be required for all the future wind scenarios in this transmission, then, is imperative because it takes longer to build new transmission capacity than it does to build new wind plants.Without transmission enhancements, substantial curtailment (shutting down) of wind generation would be required for all the 20% scenarios.generation are manageable with large regional operating pools and Transmission helps reduce the impacts of the variability of the wind, which reduces wind integration costs, increases reliability of the electrical resources. Although costs for aggressive expansions of the existing annualized costs in any of the scenarios studied.

28Carbon emission reductions in the three 20% wind scenarios do not vary by much, indicating that wind displaces coal in all scenarios and that

eastern United States; carbon emissions are reduced at an increased rate in the 30% wind scenario as more gas generation is used to accommodate wind variability. Wind generation displaces carbon-based fuels, directly reducing carbon dioxide (CO

2) emissions. Emissions continue to decline in the analysis results in higher total production costs.

they should be seen as an initial perspective on a top-down, high-level view of four different 2024 futures. The transition over time from the current state of the bulk power system to any one of the scenarios would require additional technical and economic evaluation, including detailed modeling of power

of assumptions made would also be required to guide the development of any

answers the questions posed at the outset of the project:

1. What impacts and costs do

wind generation variability

and uncertainty impose on

system operations? With large balancing areas and

fully developed regional

markets, the cost of

integration for all scenarios

is about $5 (US$ 2009) per

megawatt-hour (MWh) of wind, or about $0.005 per kilowatt-hour (kWh) of electricity used by customers.

2. multiple and geographically diverse wind resources?

The study results

show that long-distance (and high-capacity) transmission can assist

smaller balancing areas with wind integration, allowing penetration

levels that would not otherwise be feasible. Furthermore, all scenarios, including the Reference Case, made use of major transmission

upgrades to better interlock Eastern Interconnection markets for

assisting with wind integration.

Why Regional Markets?Because they span large geographic areas, regional markets optimize the power grid by promoting e~ciency through

resource sharing. These organized markets are designed so

that an area with surplus electricity can benet by sharing megawatts with another region in the open market. This allows participants and operators to see the big picture when it comes to dispatching electricity in the most e~cient manner. Source: Adapted from www.isorto.org. Accessed November 2009.

29 3. large quantities of remote wind energy to urban markets?

Long-distance operations, contributes substantially to integrating large amounts of long-distance transmission has other value in terms of system robustness that was not completely evaluated in EWITS.

4. How do remote wind resources compare to local wind resources?

In the Eastern Interconnection, the Eastern Wind Data Study database (AWS Truewind 2009) shows that the higher quality winds in the Great Plains have capacity factors that are about 7%-9% higher than onshore wind resources near the high-load urban centers in the East. Offshore plants have capacity factors on par with Great Plains resources but the cost of energy is higher because capital costs are higher.

5. How much does geographical diversity, or spreading the wind out across a large area, help reduce system variability and uncertainty?

Quite substantially.

6. What is the role and value of wind forecasting? generation, forecasting will play a key role in keeping energy markets system security.
7. wind variability and uncertainty management?

This and other recent studies (see Bibliography) reinforce the concept that large operating areas-in terms of load, generating units, and geography-combined with adequate transmission, are the most effective measures for managing wind generation.

8. How does wind generation capacity value affect reliability (i.e., supply resource adequacy)? Wind generation can contribute to system adequacy, and additional transmission can enhance that contribution.

SCENARIO COSTS the degree suggested by the study scenarios would result if many capital investments were made from the present through 2024. Consequently, economic analysis of the scenarios brings to light complicated questions that cannot be answered precisely without a detailed timeline of capital expenditures.

30Because the study scenarios need to be compared on an economic basis, total and variable (production) cost components. These costs are then summed, allowing the study team to view some measure of economic performance for each scenario side by side. Study analysts calculated costs for each scenario as the sum of production-related costs (e.g., fuel costs) plus annualized amounts for capital investments in new conventional generation, wind plants, and transmission. The results for the Reference Case and the four high-penetration scenarios (Figure 3, in millions of US$2009) show that Scenario 1 is the least costly of the 20% scenarios, and that the increased cost of offshore wind adds to the costs in Scenarios 3 and 4.Figure 3. Comparison of scenario costsAlthough production-related costs constitute a large fraction of the total costs scenarios 3 and 4, capital costs for wind generation increase because of slightly lower capacity factors and the much higher capital cost of offshore construction. Transmission costs are a relatively small fraction for all scenarios, with only a small absolute difference seen across the 20% cases. Wind integration costs are measurable but very small relative to the other factors.

None of the initial scenarios include any costs associated with carbon, which sensitivity analysis for Scenario 2, as described later in this Executive Summary and Project Overview.

31STUDY METHODOLOGY development, (2) transmission requirements analysis, and (3) wind integration wind data to capture the correlations between load and wind (i.e., weather; see sidebar). The project team developed the quantitative information through a multistage analytical process, shown graphically in Figure 4. Methods integration studies formed the basis for the technical analysis, but were necessarily extended because the scope and size of this effort surpassed that of earlier studies. Focus on transmission requirements for the substantial amount of wind generation required to meet the 20% and 30% energy targets was a new and The Role of Weather and Wind ForecastingUsing numerical weather prediction models, also known as mesoscale models, is an accepted method for producing a time series of wind plant output data. Essentially, physics-based, numerical simulations on supercomputers, integrated with observational data sets, re-create the weather of historical years and generate a four-dimensional gridded wind-speed data set. A wind speed time series data set can be extracted and converted to wind power output. This approach produces a temporally, spatially, and physically consistent wind data set. For EWITS, this was done for hundreds of wind plants and the study team used these data sets in the modeling of the dierent scenarios. Wind forecast data modeling is an increasingly common tool used by utilities and ISOs to schedule generation units. Wind integration studies typically include the eect of wind forecast errors on integration costs.

32 Notes: A copper sheet simulation assumes no transmission constraints or congestion.LOLE = loss of load expectation and ELCC = e~ective load-carrying capabilityFigure 4. Study processCurrent transmission expansion planning is based on a decision-making process that starts with the present and looks forward through time. The existing bulk power grid in the United States is the result of such a bottom-up approach. conceptual transmission capacity needed to deliver energy to load. These top-down methods tend to create designs with more transmission than bottom-up methods. The primary reason is that the total economic potential of increasing combination of capturing the economic potential of both nonwind and wind being used for wind). The transmission requirements are mainly off peak for the wind generation and on peak for the nonwind generation. Although the study assumptions were touched on previously, a more detailed look is helpful at this point. Peak demand and energy for all study regions was based on 2004-2006 Federal Energy Regulatory Commission (FERC) data 33previous study, and were reviewed by all stakeholders at that time. To preserve 2005, and 2006.Because of the very large amount of wind generation studied, it was important to establish a framework for the day-to-day operations of the Eastern which wind generation is integrated. Small balancing areas, which were the original building blocks of today's major interconnections, large amounts of wind generation. Large effective balancing areas (see sidebar) have more supply resources from diversity in both load and wind generation. Extrapolating from trends that have been seen for the past that by 2024 operations in the conducted under the auspices of seven large balancing areas, which are shown in Figure 5. The structure as it existed in August 2007 was used for comparison. Five of the seven correspond to existing RTOs in the The project team also assumed that operations in each area would conform to the same structure. For example, on the day before the operating day, all generating units bid competitively to serve load, and after market clearing, operators perform a security-constrained unit commitment to ensure that adequate capacity will be available to meet forecast load. During the operating day, generators are dispatched frequently to follow short-term demand trends under a fast, subhourly market structure. A competitive ancillary services market Operating the GridBalancing AuthorityA balancing authority is the responsible entity that maintains load resource balance within a given, predened area (the balancing authority area). The authority develops integrated resource plans, matches generation with load, maintains scheduled interchanges with other balancing authority areas, and supports interconnection frequency of the electric power systems in real time.Reliability-Related Services In the NERC Functional Model, which denes the set of functions that must be performed to ensure the reliability of the bulk electric system, these include the range of services, other than the supply of energy for load, that are physically provided by generators, transmitters, and loads in order to maintain reliability. In wholesale energy markets, they are commonly described as "ancillary services." Source: Adapted from http://www.nerc.com/~les/opman_12-13Mar08.pdf.Accessed November 2009.

34supplies regulation, balancing, and unused generation capacity to cover large events such as the loss of major generating facilities. mechanisms, however, are actually in use today, albeit not uniformly, and have been shown in previous studies to be of substantial value for wind integration. There is some probability that developments in market operation over the next decade could further enhance the ability to integrate wind energy.

Figure 5. Assumed operational structure for the Eastern Interconnection in 2024 (white circles represent balancing authorities; Entergy is operated as part of SERC) 35PROJECT OVERVIEWThis section describes the transmission requirements, wind operational impacts, production-cost modeling results, wind integration costs, carbon sensitivity TRANSMISSION REQUIREMENTS)1 for evaluating transmission requirements. The study process began with locating wind generation across the interconnection, and then determining what additional nonwind capacity would be required in each region to maintain reliability for the projected energy demand in the study year. No new transmission was considered at this stage. This step allowed the study analysts to identify the locations of electrical energy supply and locate the loads or demand for the energy. To develop the transmission overlays, then, the project team used economic signals to connect the "sources" (supply) to the "sinks" (loads).

The study team used an economics-based expansion planning methodology to develop transmission requirements for each scenario based on the output of the different production simulations. Before each set of simulations, the additional nonwind capacity required to reliably serve the projected load was determined using traditional generation expansion methodologies. Wind generation was conventional expansion were added to the production-cost model that contained the existing transmission network. After simulating system operation over an entire year of hourly data, study analysts then compared the results of this modeling simulation to those from a similar simulation in which constraints on the transmission system were removed. The comparison indicates how regional or interconnection-wide production costs increase because of transmission congestion, or put another way, what value could be achieved by eliminating or reducing transmission constraints. Differences between the "constrained" case and the "unconstrained" case yield the following information:The areas of economic energy sources and sinks capacity needspotential budget for building transmission to relieve constraints and reduce congestion costs 1 PROMOD IV (developed by Ventyx) is an integrated electric generation and transmission market simulation system that incorporates extensive details of generating unit operating characteristics and constraints, transmis

-sion constraints, generation analysis, unit commitment/operating conditions, and market system operations. PROMOD IV performs an 8,760-hour commitment and dispatch recognizing both generation and transmission impacts at the bus-bar level. (Bus-bar refers to the point at which power is available for transmission.)

36differences between production simulations using a "copper sheet" (i.e., no transmission constraints, no congestion) versus the existing transmission system. Figure 6 shows the annual generation differences between the unconstrained and areas and gives insight into the optimal locations for potential transmission lines sink areas. As Figure 7 illustrates, the price signal drives energy from low-cost source areas to high-cost sink areas if the transmission system is not constrained across the study footprint.

Figure 6. Scenario 2, annual generation di~erences between unconstrained case and constrained case (Note: Because price contours developed from dened pricing hubs, they do not correspond exactly to geography.)

37Figure 7. Scenario 2, annual generation weighted locational marginal price (LMP) for constrained caseUsing these comparative results as a guide, and with input from the TRC, the study team developed transmission overlays for each scenario. The conceptual transmission overlays, shown in Figure 8, consist of multiple for all four scenarios. Tapping the most high-quality wind resources for all three 20% scenarios, the project team arrived at a transmission overlay for For Scenario 2, analysts moved some wind generation eastward, resulting in offshore resources are used in Scenario 3, the resulting transmission overlay has

38Figure 8. Conceptual EHV transmission overlays for each study scenarioTables 2 through 4 summarize the transmission and construction cost-per-mile assumptions by voltage level, the estimated total line miles by voltage level, and the estimated cost in US$2024 for the four wind scenario conceptual Costs associated with an offshore wind collector system and those for some necessary regional transmission upgrades are not included in the total estimated cost and would increase total transmission costs. With approximately 22,697 the highest estimated total cost at $93 billion (US$2009).

39for each scenario include the following: of the volumes of energy that must be transported across and around the interconnection, as well as the distances involved. Similar levels of new transmission are needed across the four scenarios, and certain major facilities appear in all the scenarios. This commonality generation in each scenario. The study focuses on four possible 2024 "futures." Determining a path for realizing one or more of those futures was outside the study scope. Large amounts of transmission are also required in the Reference Case.accommodated as long as adequate transmission capacity is available and market/operational rules facilitate close cooperation among the operating regions. TABLE 3. ESTIMATED LINE MILEAGE BY SCENARIOESTIMATED LINE MILEAGE

SUMMARY

VOLTAGE LEVEL345 KV345 KV AC ~DOUBLE CIRCUIT500 KV500 KV AC ~DOUBLE CIRCUIT765 KV400 KV DC 800 KV DCTOTALREFERENCE3,1062925934942,6244702,4009,979SCENARIO 11,9772471,2642437,30456011,10222,697SCENARIO 21,9772471,2642437,3045608,35219,947SCENARIO 31,9772471,2647427,3047694,74717,050SCENARIO 41,9772471,2647427,30456010,57322,667TABLE 4. ESTIMATED COSTS BY SCENARIO ~US$2009, MILLIONSEstimated Cost Summary (US $2024, millions)VOLTAGE LEVEL345 KV 345 KV AC ~DOUBLE CIRCUIT500 KV500 KV AC ~DOUBLE CIRCUIT765 KV400 KV DC800 KV DCTOTALReference 5,607 880 1,367 1,900 10,790 1,383 9,243 31,170Scenario 1 3,569 743 2,916 935 30,033 1,539 53,445 93,179Scenario 2 3,569 743 2,916 935 30,033 1,539 40,206 79,941Scenario 3 3,569 743 2,916 935 30,033 1,898 22,852 64,865Scenario 4 3,569 743 2,916 935 30,033 1,539 50,898 92,551TABLE 2. TRANSMISSION COST ASSUMPTIONSCOSTPERMILE ASSUMPTIONVOLTAGE LEVEL345 KV345 KV AC ~DOUBLE UNIT500 KV500 KV AC ~DOUBLE CIRCUIT765 KV400 KV DC800 KV DCUS$2024 ~MILLIONS2,250,0003,750,0002,875,004,792,005,125,0003,800,0006,000,000US$2009 ~MILLIONS1,440,0002,410,0001,850,003,080,003,290,0002,440,0003,850,000 40amount.here. For example, it would be possible to schedule reserves from one area to another, effectively transporting variability resulting from wind and load to areas that might be better equipped to handle it. And the transfer capability of the underlying AC network could be enhanced by using the DC terminals to mitigate limitations caused by transient stability issues. WIND OPERATIONAL IMPACTSReliable delivery of electrical energy to load centers entails a continuous process of scheduling and adjusting electric generation in response to constantly changing uncertainty in demand that power system operators face from day to day or even each of the study scenarios would affect daily operations of the bulk system and Using detailed chronological production simulations for each scenario, the study team assessed impacts on power system operation. The objective of these simulations was to mimic how day-to-day operations of the Eastern generation in each scenario, new conventional generation per the expansion study, and the transmission overlays the study team developed. Ways to manage the increased variability and uncertainty attributable to wind generation, along with the resulting effect on operational costs, were of primary interest. operational simulations using the transmission overlays for each scenario and the wind plant outputs and actual load data for 2004, 2005, and 2006. The model takes the wind generation at each "injection bus" (i.e., the closest transmission connection to the wind plant) and dispatches nonwind generation units accordingly for each market region while solving at the model node for the LMP. commitment problem (i.e., what conventional generators will be dispatched to the units based on the actual modeled wind and load data. Obtaining realistic results is necessary because unit-commitment decisions must actually be made to the grid. A hurdle rate accounts for hourly transactions among eight different market regions. The simulation is done over the entire study region and the wind plant and load time series data capture geographic diversity.

41RESERVE REQUIREMENTSWith large amounts of wind generation, additional operating reserves (see sidebar) are needed to support interconnection frequency and maintain balance between generation and load. Because the amounts of wind generation in any of the operating areas, for any of the scenarios, dramatically exceed the levels for which appreciable operating experience exists, the study team conducted data to estimate the additional requirements. These were used as inputs to the production-cost modeling. The analysis focused on the major categories of operating reserves, which included needs for regulation, load following, and contingencies.for each scenario, study analysts took into account the additional uncertainty and variability resulting from wind generation by operating reserves as constraints on the commitment and dispatch of generating resources in each operating areaCommitting generating units for operation based on forecasts of load and wind generation, then dispatching the available units against actual quantities.

The levels of wind generation the amount of operating reserves required to support interconnection frequency and balance the system in real time. Contingency reserves are not directly affected, but the amount of spinning reserves assigned to regulation duty must increase because of the additional variability and short-term uncertainty of the balancing area demand.Types of ReservesIn bulk electric system operations, dierent types of generation reserves are maintained to support the delivery of capacity and energy from resources to loads in accordance with good utility practice.

Contingency ReservesReserves to mitigate a "contingency," which is dened as the unexpected failure or outage of a system component, such as a generator, a transmission line, a circuit breaker, a switch, or another electrical element. In the formal NERC denition, this term refers to the provision of capacity deployed by the balancing authority to meet the disturbance control standard (DCS) and other NERC and regional reliability organization contingency requirements.Operating ReservesThat capability above rm system demand required to provide for regulation, load forecasting error, forced and scheduled equipment outages, and local area protection. This type of reserve consists of both generation synchronized to the grid and generation that can be synchronized and made capable of serving load within a specied period of time. Regulating ReservesAn amount of reserve that is responsive to automatic generation control (AGC) and is su~cient to provide normal regulating margin. Regulating reserves are the primary tool for maintaining the frequency of the bulk electric system at 60 Hz. Spinning ReservesThe portion of operating reserve consisting of (1) generation synchronized to the system and fully available to serve load within the disturbance recovery period that follows a contingency event; or (2) load fully removable from the system within the disturbance recovery period after a contingency event.

42The assumption of large balancing areas does reduce the requirement, however. amount of regulation that would need to be carried would be dramatically higher. regulating reserve requirements for each region and each scenario from hourly that varies with both the amount of load and the level of wind generation. The calculations account for important characteristics of the wind generation scenario, short-term variability. Figure 9 summarizes the regulating reserve requirements for each region and each The load-only case is a reference for calculating the incremental requirement resulting from wind generation.

Figure 9. Regulating reserve requirements by region and scenario.

The incremental amount resulting from wind generation is the di~erence between the scenario number and the load-only value.Current operating experience offers little guidance on managing the incremental variability and uncertainty associated with large amounts of wind generation in on the time series data from the scenarios, however, forms a highly reasonable 43analytical foundation for the assumptions and reserve requirement results that the study team carried forward to the production simulations. The team's analysis of reserve requirements with substantial amounts of wind operated in 2024 played an important role in minimizing the additional amounts of spinning reserve that would be required to manage the variability of large amounts of wind generation.The large size of the market areas assumed in the study allows substantial The pooling of larger amounts of load and discrete generating resources via load declines as the amount of load increases; larger markets also have more discrete generating units of diverse fuel types and capabilities for meeting load and managing variability.With real-time energy markets, changes in load and wind that can be forecast generating units. Because load changes over 10-minute intervals can be accurately forecast, they can be cleared in a subhourly market.wind generation. short-term (e.g., 10 to 20 minutes ahead) wind generation forecasts. Data from the Eastern Wind Data Study can be used to characterize both over a larger geographic area, percentages of aggregate wind variability and uncertainty decrease. These quantitative characterizations are useful for estimating incremental reserve requirements.Current energy market performance shows that, on average, subhourly market prices do not command a premium over prices in the day-ahead market. Consequently, the hourly production simulation will capture most of the costs associated with units moving in subhourly markets, and the spinning reserve requirements for regulation and contingency will appropriately constrain the unit commitment and dispatch.marginal conventional generation, those nonwind resources deliver less energy and thus realize less revenue. With large amounts of wind generation such as those marginal units that are not captured in the production modeling.

44PRODUCTIONCOST MODELING RESULTSThe project team ran annual production simulations for all three wind and load years and all scenarios. The raw results included hourly operations and costs for of the sheer volume of data generated, the project team had to analyze summary information. The detailed production modeling of a system of such size and scope reduces the number of assumptions and approximations required. Although the large volume of results is a disadvantage, the results do contain information from which conclusions Generation displacement depends on the location and amount of wind generation.Because of its low dispatch price, wind generation will reduce LMPs. The effect in a particular region is greater with local wind resources.The addition of overlay transmission works to equalize LMPs across the footprint. Because of transfer limits, there are still price differences across the footprint, but the magnitude of the difference is reduced with the overlays. Offshore wind has more effect on LMPs in eastern load centers because of its proximity to large load centers otherwise served by generation with higher costs.Figure 10 shows total production costs for each of the high-penetration wind scenarios and for the Reference Case. The primary effect of wind generation is to displace production from conventional sources; as the amount of wind generation increases, so does the magnitude of the displacement. The location of wind generation, however, prices are higher in the East and lower in the western portion of the interconnection. Consequently, production costs are reduced more by wind in areas with higher costs; the production costs shown in Figure 10 do not account for the capital costs of the wind or infrastructure required to deliver wind energy to load.

45Figure 10. Annual production-cost comparison (US$2009, millions)WIND INTEGRATION COSTSAssessing the costs for integrating large amounts of wind generation was another employed in earlier integration studies as their starting point. As interim results became available, nuances in and challenges to applying that methodology to a bolstered the knowledge base and perspective on the components of the total cost associated with managing wind energy delivery.The study team computed the cost of managing the delivery of wind energy (i.e., the integration cost) by running a set of comparative production simulations. additional regulating reserves for managing variability and short-term uncertainty. They also assumed that the hourly wind energy delivery was known perfectly in the unit-commitment step of the simulation. The differences in production costs among these cases and the corresponding cases where wind generation is not ideal can be attributed to the incremental variability and uncertainty introduced by the wind resource. Figure 11 shows the calculated integration cost for each scenario. Costs vary by scenario and by year, but all are less than 10% of the bus-bar cost of the wind energy itself.

46 Figure 11. Integration cost by scenario and year (US$2009) Salient points from the integration impacts and costs analysis include the following:Because the production simulation model contains multiple operating areas, and because transactions between and among these areas are determined on an economic basis, variability from wind in a given area is carried through impacts were isolated in the subject area by restricting transactions to Costs for integrating wind across the interconnection vary by scenario. For the 20% cases, Scenario 1 shows the highest cost at $5.13/MWh (US$2009) of wind energy; Scenario 3 shows the lowest integration cost at $3.10/MWh (US$2009).is roughly a combination of scenarios 1 and 3.Results for the 20% scenarios show that spreading the wind more evenly over the footprint reduces integration costs. This is particularly noticeable in the East, where there is more load and a larger number of resources to manage variability.The project team also analyzed production simulation results to assess curtailment of wind generation resulting from transmission congestion or other binding constraints. Such constraints include excess electricity supply relative to demand and must-run generation ("minimum generation" limits), limitations in ramping capability, or availability of adequate operating reserves.

47 production simulation results. Findings include the following:Wind generation was assigned a very low dispatch price in the to relieve congestion. Even so, study analysts observed a modest amount of curtailment in some operating areas. This is likely the result of local or subregional transmission congestion. After conducting a sensitivity analysis consisting of additional production simulation runs, the study team determined that transmission congestion levels, reserve constraints, and ramp limitations accounted for less than 1% of the curtailed energy.

transmission overlays, facilities were sized to accommodate a from the unconstrained production simulation case.

Consequently, a certain amount of wind generation curtailment was a likely outcome.CARBON SENSITIVITY ANALYSISThe entire analytical methodology, except for the loss of load expectation (LOLE) analysis (see the next section for more information on LOLE), was run for a scenario that considered a carbon price of $100/metric ton.

The study team determined that the high price was necessary to bring analysis, choosing a high price helped to illustrate sensitivities. Figure 12 shows the results of the expansion, and Figure 13 compares the expansion for the carbon sensitivity case to the base scenarios and the existing Results from the production simulations show that the impact on carbon emissions is substantial. Even though the carbon sensitivity case was based on Scenario 2, in which wind generation provides 20% of the those from Scenario 4, in which wind generation delivers 30% of that energy (Figure 14).Ramp RatesFor a generator, the ramp rate (typically expressed in megawatts per minute) is the rate at which a generator changes its output. For an interchange, the ramp rate or ramp schedule is the rate, also expressed in megawatts per minute, at which the interchange schedule is attained during the ramp period.Because wind is variable and results in ramping, it is important to understand these ramp rates and maintain reserves to cover them as needed.

48 Little impact was observed on wind generation curtailment or integration cost. Relative to the original Scenario 2 (Figure 15), fossil-fuel generation is reduced; nuclear generation increases because the nuclear share of the new generation expansion is larger. Energy from combined-cycle plants also increases because it became the preferred resource for managing variability. With the high cost of carbon, energy prices increase across the footprint (Figure 16). The present value of the accumulated costs more than doubles from the base scenarios (Figure 17).

Figure 12. Generation expansion for the Scenario 2 carbon sensitivity case

49CC = combined cycle; CT = combustion turbine; DR = demand response; IGCC = integrated gas combined cycle; IGCC/Seq = integrated gas combined cycle with sequestration; CC/Seq = combined cycle with sequestration; RET Coal = coal plant retirements; Replacement CC = replacement combined cycleFigure13. Generation expansion by scenario, including the carbon sensitivity caseFigure 14. Carbon emissions for di~erent scenarios (carbon price applies only to carbon scenario) 50 Figure 15. Generation utilization by unit and fuel type for Scenario 2 and carbon sensitivity case Figure 16. Comparison of generation-weighted LMP by region for Scenario 2 and carbon sensitivity case 51Figure 17. Present value of accumulated costs for base scenarios and carbon sensitivity (US$2009)CONTRIBUTIONS TO RESOURCE ADEQUACYof bulk power system reliability.

Although wind generation cannot be dispatched to meet peak loads, that wind generation would be available during periods of system stress (i.e., it needs additional energy to meet demand). Unlike conventional generating units, only a small fraction of the nameplate capacity rating of a wind plant can be counted on to be available for serving peak loads. With the amounts of wind generation the small fraction in quantitative detail is important because it equates to billions of dollars of capital investment. Reliability of the GridEWITS: The EWITS results represent a rst detailed look at several "snapshots" of the Eastern Interconnection as it could exist in 2024 and is therefore not intended to provide a complete analysis of the reliability impacts to the present bulk power system. EWITS is aimed at characterizing the operational impacts for future scenarios, primarily through economics-based transmission expansion planning, resource adequacy studies, and hourly modeling simulations. Important technical aspects in the study related to Bulk-Power System reliability were not studied or were represented approximately or by means of best engineering judgments. A variety of comprehensive power system engineering analyses and studies still need to be conducted (see Summary and Future Work Section) to determine what additional situations should be addressed to maintain system reliability from the present to the 2024 study year when integrating large quantities of renewable generation.

52 The fraction of the nameplate rating of a wind plant that can be counted expressed as a percentage, is known as the capacity value.To estimate a 2024 capacity value for wind, the study analysts used the 2004, 2005, and 2006 effective load-carrying capability (ELCC) of wind at the future penetration level.

The team analyzed each of the high-penetration wind scenarios that were explored in the operational analysis.different levels of transmission sensitivities. The level of transmission being modeled varied from no ties between areas to the different transmission levels of each existing and conceptual overlay scenario. These transmission sensitivities wereExisting transmission system (constrained case and interface limits)Conceptual transmission overlay (increased zone-to-zone interface limits and new ties).Data from the operational simulations were conditioned into the correct format for implementation into the LOLE model. Because that model uses a transportation representation for the transmission network, the study team ran a large number of additional production simulations to estimate the import were used as the modeling zones. Table 5 lists these zones along with the total Reliability of the Grid (continued)

Federal Energy Regulatory Commission (FERC) Study: FERC is conducting a new study with Lawrence Berkeley National Laboratory that is intended to validate whether frequency response is an appropriate metric for gauging the impacts on reliability of integrating increasing amounts of variable-output generation capacity into the three electrical interconnections. The study will do this by using today's transmission networks and generating facilities-including facilities under construction-as the basis for the models and studies in contrast to the alternative scenarios for 2024 used in EWITS. The new study is intended to investigate the frequency metric as an approach to identifying critical factors when integrating large amounts of variable generation into the bulk power system.

53TABLE 5. RELIABILITY ZONES FOR LOLE ANALYSIS WITH INSTALLED WIND GENERATION CAPACITY NAMEPLATE WIND IN MEGAWATTSZoneScenario 1Scenario 2Scenario 3Scenario 4MISO West 59,260 39,953 23,656 59,260MISO Central 12,193 11,380 11,380 12,193 MISO East 9.091 6,456 4,284 9,091 MAPP USA 13,809 11,655 6,935 14,047SPP North 48,243 40,394 24,961 50,326SPP Central 44,055 46,272 25,997 44,705PJM 22,669 33,192 78,736 93,736TVA 1,247 1,247 1,247 1,247SERC 1,009 5,009 5,009 5,009NYISO 7,742 16,507 23,167 23,167ISO-NE 4,291 13,837 24,927 24,927Entergy 0 0 0 0 IESO a 0 0 0 0MAPP Canada 0 0 0 0FULL STUDY SYSTEM 223,609 225,902 230,299 337,708a Independent Electricity System OperatorResults of the ELCC analysis, shown graphically in Figure 18 for the cases with existing and overlay transmission, indicate that the transmission network has calendar year with the smallest contribution, the aggregate capacity value of wind generation by scenario ranges from 53 GW to almost 65 GW. is intuitive, the magnitude of the contribution is striking. Considering both aggregate wind generation capacity value is increased by more than 20 GW in the 20% cases, and by nearly 30 GW at 30% wind penetration. The capacity value results vary depending on the year, which is consistent with observations in previous studies (see Bibliography). The magnitude of the interannual variation is actually smaller than that seen in some of the earlier results. This could be a consequence of both the scale of the model and the large volume of wind generation. Assessing the capacity value of wind generation has been a staple of most of the integration studies conducted over the past several years. The approach taken in date because of the size and scope of the model, the process by which area transfer limits were determined, and the sensitivities evaluated. The wind capacity values 54 studies. The study team recognizes that the results represent a macro view, in which some important intraregional transmission constraints are not considered. Because the project focuses on transmission, though, the results represent a target resource adequacy contribution that could be achieved for the wind generation scenarios studied.Figure 18. LOLE/ELCC results for high penetration scenarios, with and without transmission overlays the transmission overlays shows that the ELCC of the wind generation ranges from 24.1% to 32.8% of the rated installed capacity. The transmission overlays increase the ELCC of wind generation anywhere from a few to almost 10 percentage points (e.g., 18% to 28%). The ELCC of wind generation can vary greatly by geographic region depend

-interannual variations were observed, they are much smaller than those seen in previous studies (see, for example, EnerNex be the same across all four scenarios.

55

SUMMARY

AND FUTURE WORK primarily by economic considerations, with important technical aspects related to bulk power system reliability represented approximately or through engineering judgments. because it addresses questions such as feasibility and total ultimate costs, and begins to uncover important additional questions that will require answers. the study team also recognizes that additional key stakeholders must be involved to further develop an interconnection-wide view of transmission system plans.of additional technical analysis. The framework established by the scenario for conducting conventional power system planning to further evaluate the feasibility of these high-penetration scenarios and to improve the cost estimates. binding constraints or other periods of interest, such as large changes in wind generation would raise questions about the security of the system. The state of appropriate AC power system model. A variety of power system engineering analyses could then be conducted to determine what additional equipment or operating limitations would be necessary to maintain system reliability. These analyses would include the following.An AC analysis that examines in more detail the power transfer consider the wide range of issues associated with voltage control and look at voltage and reactive compensation issues, dynamic and transient needs could then be analyzed in much greater detail. Longer term dynamic analysis, where the actions of AGC, load tap changing on transformers, and capacitor or reactor switching for voltage control can be simulated and analyzed in much greater detail.

Such dynamic analysis could examine subhourly market operation and 56the response of generation to either AGC or market dispatch instructions while considering the limitations caused by prime mover or governor analysis could be used to zoom in on system operation in real time, requirements and policies needed to maintain performance and reliability.require that many entities across the interconnection participate and collaborate. Personnel engaged in running similar studies with a regional focus would need to be involved, at a minimum, in a review capacity and for interpreting results.

National entities such as NERC would also need to be engaged to oversee the development of the data sets and models. And because the size and scope of the system models might also require computational power beyond what is used today in the power industry, these suggested analyses could involve universities or national laboratories with appropriate resources.engineering analysis. The analysis would paint a more accurate picture of the total transmission investment necessary, and illuminate measures necessary to would be beyond the scope of previous attempts, and would require cooperation and coordination at many levels to succeed. results pose some interesting policy and technology development questions:

Could the levels of transmission, including the Reference Case, ever be permitted and built, and if so, what is a realistic time frame?Could the level of offshore wind energy infrastructure be ramped up fast scenarios?bottom-up process with more stakeholders involved?How can states and the federal government best work together on regional transmission expansion and the massive development of onshore and offshore wind infrastructure?What is the best way for regional entities to collaborate to make sure wind is optimally and reliably integrated into the bulk electrical grid? What is the difference between applying a carbon price instead of mandating and giving incentives for additional wind?

57 As is expected in a study of this type, especially when a wide variety of technical experts and stakeholders are giving ongoing input, a number of several technical areas in the study present opportunities for further technical investigation that could deepen understanding or reveal new insights:Further analysis of production-cost simulation results: The output from the on every generator and monitored transmission interface in the Eastern analysis was necessarily limited to summary results. Further analysis of these output data would likely generate additional valuable insights on detailed analyses that could be conducted in the future.Smart grid implications and demand response sensitivities: The Eastern out to the study year (2024). For the most part, load was considered "static." Major industry initiatives are currently exploring means by which at least a portion of the load might respond like a supply resource, thereby relaxing the constraints on scheduling and dispatch of conventional generating units. is why alternative 2024 scenarios that consider the range of smart grid implications for the bulk electric system merit further consideration (scope Nighttime charging of PHEVs: Widespread adoption of electric vehicles has the potential to alter the familiar diurnal shape of electric demand. Because the wind resource is abundant at night and during the low-load seasons, increases in electric demand during these times could ease some of the issues associated with integration. Commitment/optimization with high amounts of wind: The approach for scheduling and dispatching generating resources used in the production practices and energy market structures might be implemented that take advantage of the fact that uncertainty declines as the forecast horizon is that allow reoptimization of the supply resources more frequently could offer some advantage for accommodating large amounts of variable and uncertain wind energy. Fuel sensitivity: future for prices of other fuels used for electric generation. As history attests, there is much uncertainty and volatility inherent in some fuel markets, especially for natural gas. Alternate scenarios that explore the impacts of other fuel price scenarios on integration impacts and overall costs would be valuable.

58The role and value of electrical energy storage: With the substantial transmission overlays and the assumption of large regional markets, accommodated without deploying additional energy storage resources. The ability to store large amounts of electrical energy, though, could potentially obviate the need for some of the transmission and reduce wind integration impacts. Analysis of bulk energy storage scenarios with generic storage technologies of varying capabilities would quantify the renewable energy. Transmission overlay enhancement: As described earlier, the analytical methodology was based on a single pass through what is considered to be an iterative process. Further analysis of the existing results could be additional production simulations and LOLE analyses, along with AC of the overlay and bolster the view of the required regional transmission expansion that would be needed to deliver the large amounts of wind energy to load. Sequencing of overlay development: 2024 scenario using a top-down perspective. The resulting transmission overlays and substantial amounts of wind generation would be feasibility and costs of an aggressive transmission development future. Wind generation curtailment:

Using wind generation curtailment selectively and appropriately could have high operational value. spilling wind generation, downward movement is easily accomplished with today's wind generation technology. This could have very high economic value under certain circumstances. Wind generation is very capable of "regulating down"; for example, in an ancillary services market where the regulation service is bifurcated (i.e., regulation up and regulation down are separate services). Additional analysis of the would be worth to wind plant operators.The current installed capacity of wind generation in many areas of the United States, coupled with prospective development over the next several years, requires that assessments of the bulk electric power system take a much broader view generation as an electrical energy supply resource are leading the power industry to new approaches for planning and analyzing the bulk electric power system.

59in other large-scale wind integration analyses. The data sets compiled for the study represent the most detailed view to date of high-penetration wind energy futures more to follow.

60 61SECTION 1: INTRODUCTION As the penetration of bulk wind energy continues to grow, evaluating its effect on the operation of regional electrical systems becomes increasingly important. Evaluating wind energy's interaction with the utility grid allows for a better understanding of how to manage the wind resource during both planning and regional electrical systems will produce information critical to transmission planning.Many states are adopting regional mandates on renewable energy penetration into the electrical grid, and these mandates could be adopted for the nation as a whole. Meeting the growing need for wind power in the United States will require careful analysis and modeling of how large amounts of wind power will be integrated into the electrical grid. Analyzing future scenarios of wind power penetration using state-of-the-art production-cost models, transmission power step in the energy and transmission planning process. Because new transmission will most likely be necessary for much of the future wind power that will be installed in the United States, it is imperative to plan for this transmission. The building wind plants.The U.S. Department of Energy (DOE) commissioned the work described in this report through its National Renewable Energy Laboratory (NREL). Known as designed to consider a range of important and contemporary questions about integrating high-penetration wind energy into the grid. The technical work conducted for this study produced detailed quantitative information on the following:Wind scenarios that reach wind energy penetrations equivalent to 20% of Transmission concepts for delivering energy economically for each scenar

-io (new transmission for each scenario is based on economic performance for the conditions of the generation scenario) Economic sensitivity simulations of the hourly operation of the power transmission overlayThe contribution made by wind generation to resource adequacy and plan

-ning capacity margin.As part of this study, NREL convened a Technical Review Committee (TRC) with representation from regional electric reliability councils, expert reviewers, 62the study subcontractor, transmission planners, utilities, and wind industry representatives. The TRC met 6 times over 14 months during the project to review study progress; comment on study inputs, methods, and assumptions; assist with collecting data; and review drafts of the study report.The Eastern Wind Data Study (AWS Truewind 2009), a precursor to this study, for the eastern United States. The hourly time series data produced in that focuses on the integration of wind power into the majority of the Eastern PROJECT OBJECTIVESFor this project, the study team evaluated the power system impacts, costs, and conceptual transmission overlays attendant with increasing wind generation capacity to 20% and 30% of retail electric energy sales in 2024 for the study area, the following entities:Portions of the Southeastern Electric Reliability Council (SERC)Southwest Power Pool (SPP)Mid-Continent Area Power Pool (MAPP)and related technical work (see Bibliography), and coordinates with ongoing objective was to produce meaningful, broadly supported results through a technically rigorous and inclusive study process. KEY ISSUES AND QUESTIONShad been conducted for individual or regional entities (see Bibliography). When many of those studies were performed, the "outside world" (i.e., the operation from operating history. Now that the installed capacity of wind generation is approaching 30 GW in the United States (and concentrated in certain areas), and many states have passed legislation mandating that an appreciable fraction of electrical energy be produced by certain renewable resources, interest has grown 63Many of the key questions to be answered in this study are similar to those posed in previous wind integration studies, but the scope and scale are entirely has a limited capacity for accommodating additional wind generation; transmission congestion is already an issue in some areas, including those with the potential for tenfold or greater development in wind capacity. Consequently, evaluating transmission needs was also a major aspect of this study.Key questions posed at the outset of the project include the following:

1. What impacts and costs do wind generation variability and uncertainty impose on system operations?
2. multiple and geographically diverse wind resources?
3. large quantities of remote wind energy to urban markets?
4. How do remote wind resources compare to local wind resources?
5. How much does geographical diversity, or spreading the wind out across a large area, help reduce system variability and uncertainty?
6. What is the role and value of wind forecasting?
7. wind variability and uncertainty management?
8. How does wind generation capacity value affect supply resource adequacy? OVERVIEW OF PROJECT TASKSEvaluating the impacts of large-scale wind generation development across developing wind plant power outputs, conducting transmission analysis, and studying the implications of high-penetration wind integration. The last two tasks are formally part of the study documented in this report. A reasonably accurate, physically consistent depiction of what wind generation would look like to power system operators has been the critical input to all previous integration studies. Expanding the area of interest to include nearly challenge in this respect. The precursor effort (AWS Truewind 2009), though, resulted in an extensive database of synthesized, high-resolution, correlated 64quality control process was applied to the raw data, followed by construction of more than 700 GW of wind power plant temporal data, down to a resolution of 10 minutes for a consecutive 3-year period (2004, 2005, and 2006).wind generation scenarios for 2024, three with 20% of the projected electrical wind generation penetration to 30%. represents the leading edge of engineering and economic methods combined with computational horsepower. With the tools and analytical methods described in this report, the study team designed extensive top-down transmission overlays that span the interconnection, and then rigorously analyzed the operating and planning reserve impacts. and operational implications of adding up to 30% penetration by energy of wind generation and solar energy to the WestConnect footprint in the Western aimed at developing, where possible and appropriate, common solutions to wind integration challenges in Europe. The study also seeks to identify arrangements that will make the best use of the pan-European transmission network, allowing And as the amount of wind generation continues to increase, these studies are unlikely to be the last. ORGANIZATION OF THIS REPORT Section 2 describes the data the team used and the process employed to create the four wind generation scenarios. Characteristics of the wind resource on a regional basis are also described.

Section 3 discusses the data and analysis methods used to develop the scenarios for the 2024 study year. The section also describes the tools used for the detailed assessments of wind generation operational impacts and resource adequacy contributions.

Section 4 explains how the transmission overlays were developed for each scenario. The large amounts of wind generation considered here will increase the 65variability of the net demand and introduce some heightened uncertainty into operational considerations.

Section 5 discusses the approach for assessing how operating reserves would be affected by these large amounts of wind generation, and presents results by scenario and operating region.

Section 6 presents the range of operational impacts as determined from chronological hourly production simulations of the entire Eastern Section 7 summarizes the analytical effort to determine how wind generation contributes to resource adequacy, an important element of power system reliability.

Section 8Section 9continued. These recommendations are drawn from comments and discussions among members of the project team and the TRC, along with project sponsors.

66SECTION 2: WIND SCENARIO DEVELOPMENT INTRODUCTIONmultiple scenarios was to examine the effect of different geographic positioning of the wind resources at 20% of the expected load for 2024 and to understand the effect of increasing the wind penetration to 30%.describes the resulting four wind scenarios. Because of the large volume of data, both for the entire database and for the scenarios used in this study, the documentation in this report is necessarily in the form of summary charts, graphs, and tables that depict relevant characteristics of the time series data. DESCRIPTION OF MESOSCALE DATABASEThe mesoscale database, which now resides at the National Renewable Energy Laboratory (NREL), contains 1,325 separate wind production plants, most hypothetical and others corresponding to the locations of existing operating wind plants. These plants are aggregations of the 2-kilometer (km) wind simulation grid data from meteorological simulations done by AWS Truewind (2009). The nameplate capacity of these plants varies from 100 megawatts (MW) to greater than 1,400 MW. The total installed nameplate capacity is approximately 700 gigawatts (GW).The project that produced these data modeled the atmosphere over the study area using mesoscale modeling tools. Mesoscale refers to atmospheric phenomena (temperature, pressure, precipitation, and wind, for example) on scales of several kilometers to several hundred kilometers. By using known meteorological measurement data for historical years, the model can be guided to reproduce what the wind speeds and air density would have been at many points, both on the ground and at wind turbine hub height. Those wind speeds are used, along with local geographic information (e.g., mountains, lakes, and ridgelines), to estimate an area's wind power production over the time frame of the numerical weather simulation.and 2006. For each plant, the data span 3 years of 10-minute power production data. For each site, several hourly resolution forecast vectors were calculated, including a day-ahead horizon (18 to 42 hours4.861111e-4 days <br />0.0117 hours <br />6.944444e-5 weeks <br />1.5981e-5 months <br />), a 6-hour-ahead forecast, and a 4-hour-ahead forecast.

67The wind data calculated for this study are roughly distributed according to the geographic quality of the wind resources across the eastern United States.

Some heavier weighting was given to eastern states because high-capacity wind resources are concentrated in the western states. States like Nebraska and Minnesota have large amounts of high-quality wind; states like New Jersey, an effort to represent wind resources from all states with any reasonable wind resources in the data set.One important measure of wind-resource quality is the levelized cost of energy (LCOE) for each facility in the database and for the wind database as a whole. Table 2-1 gives the economic parameters used for the calculations, and Figure 2-1 shows the LCOE for all the wind plants in the database (plotted by increasing cost against the accumulating nameplate capacity of the plants). TABLE 21. LCOE ECONOMIC ASSUMPTIONS ~US $2009ASSUMPTIONONSHOREOFFSHOREFixed Charge Rate (%)

11.92 11.92Capital Cost ($/kW) 1,875 3,700Fixed O&M ($/kW/yr) 11.50 15.00Variable O&M ($/MWh) 4.79 14.50Notes: kW = kilowatt; O&M = operations and maintenance; MWh = megawatt-hour.Source: 2008 data from NREL and Lawrence Berkeley National Laboratory. See also Wiser and Bolinger (2009). Note that these costs are not the same as those used for the resource expansion planning documented later in this report. The values here reect updated information that was not available when the study team explored expansion planning.

68Figure 2-1. LCOE for all wind facilities in databaseThe plants with the lowest costs typically have the highest capacity factor. Offshore wind plants tend to have the highest LCOE because of their high capital nameplate capacity and about 100 GW of offshore wind nameplate capacity in the Great Lakes and off the eastern seaboard. Offshore wind is located in waters up to 30 meters (m) deep.

Another useful way to look at the overall data is in terms of capacity factor versus cumulative nameplate capacity. Capacity factor can be seen as a reasonable proxy for return on construction, carrying, and operations costs. Figure 2-2 shows the incremental capacity factor for all plants in the database with and without considering offshore plants. The horizontal axis shows the total capacity in the database having a capacity factor equal to or greater than the capacity factor indicated on each curve. Capacity factors shown on this graph (and on Figure 2-3) are net, but curtailment (shutting down wind) is not taken into account.

69 Figure 2-2. Capacity factor for all wind in databaseFigure 2-3 shows the same data as Figure 2-2 except that the capacity factor is an aggregate of all units less than a selected capacity value. Figure 2-3 shows the total effective capacity factor for the total capacity selected on the capacity axis.

Figure 2-3. Aggregate capacity factor for all wind in database 70SCENARIO DEVELOPMENT PROCESSthe wind scenarios from the database described earlier. Four scenarios were For each of the scenarios, the study team took state renewable portfolio standard (RPS) goals and existing interconnection wind queues into account, and gave some consideration to distributing wind to all states with usable resources.To yield data required for selection among all the resources calculated and stored in the database, some calculations were required. The study team analyzed the production time series data to determine annual and average capacity factor and energy production for 2004, 2005, and 2006 for each of the 1,325 sites.Once the regional allocations were decided, the project team worked with NREL to segregate wind resources from the database by geographic region. Next, the team analyzed these data, and made the appropriate selections. Because this the annual wind production target. For regionally focused scenarios (all except Scenario 1), selections were based decreasing capacity factor until the target annual energy penetration was reached. The study team used 3-year average capacity factor and energy production values for this process.After the quantitative process was complete, the allocations were manually checked to ensure that diversity and local siting goals were met. For instance, some adjustments were made to the scenarios to site some wind generation in all states with RPS in place or pending.SCENARIO DESCRIPTIONSanalyses on the scenario data. This section gives an overview of these analyses.

71 1 Because of space considerations, Midwest ISO is shortened to MISO in tables and gures. Similarly, PJM Inter

-connection is shortened to PJM.

2 Entergy is operated as part of SERC.REGIONAL WIND CAPACITY AND ENERGYThe scenario data were tabulated based on the independent system operator follow:

1 and Mid-Continent Area Power PoolSPP: Southwest Power Pool (includes Nebraska Power Association and Entergy 2 loads) Figure 2-4.Study regional denitionsThe scenarios were developed for the different energy targets. Table 2-2 and by region for each of the four energy scenarios and for the Reference Scenario. The 20% energy scenarios (1 through 3) vary slightly because of the different resources used to achieve the targets.

72TABLE 22.

SUMMARY

OF ENERGY BY REGION FOR SCENARIOSREGIONANNUAL ENERGY ~TWHREFERENCE SCENARIOSCENARIO 1SCENARIO 2SCENARIO 3SCENARIO 4ISONE 33 13 46 82 82MISO + MAPP 63 404 288 189 405NYISO 20 22 48 71 71PJM 65 64 97 244 295SERC 13 3 16 16 16SPP 26 234 245 139 243TVA 4 4 4 4 4TOTAL 224 744 745 746 1116Figure 2-5. Annual energy production by regionFigure 2-6 shows the allocation of nameplate capacity for each region by scenario.

73Figure 2-6. Nameplate capacity by regionSCENARIO DETAILSinstalled capacity by operating region, sizes and locations of plants, and state-by-state capacity. REFERENCE CASEThis scenario is designed to approximate the current state of wind development plus some expected near-term development guided by interconnection queues and state RPS. This scenario totaled about 6% of the total 2024 projected load Table 2-3 lists capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-7. TABLE 23. REFERENCE CASE, 6% OF 2024 LOAD REQUIREMENTSREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 8,310 3,000 11,310 33MISO + SAPP 19,732 19,732 63NYISO 4,932 3,000 7,932 20PJM 19,402 1,620 21,022 65SERC 1,009 2,000 3,009 13SPP 7,419 7,419 26TVA 1,247 1,247 4TOTAL 62,051 9,620 71,671 224 74Figure 2-7. Wind plant size and location for Reference Case SCENARIO 1factors across the interconnection. Consequently, it has the largest Great Plains wind capacity of the three 20% scenarios and takes advantage of the best onshore resources in the East. Table 2-4 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-8. Figure 2-9 is a better visual illustration of state-by-state installed capacity.TABLE 24. SCENARIO 120% HIGH CAPACITY FACTOR, ONSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 4,291 0 4,291 13MISO + SAPP 94,808 0 94,808 404NYISO 7,742 0 7,742 22PJM 22,669 0 22,669 64SERC 1,009 0 1,009 3SPP 91,843 0 91,843 234TVA 1,247 0 1,247 4TOTAL 223,609 0 223,609 744 75Figure 2-8. Installed capacity-Scenario 1 Figure 2-9. State map of nameplate capacity-Scenario 1 76SCENARIO 2the East Coast. This scenario corresponds most closely to a 20% scenario studied in a recent collaborative planning effort (JCSP 2008).Table 2-5 shows capacity by operating region. Figure 2-10 shows locations and sizes of individual plants, and Figure 2-11 shows state-by-state installed capacity.TABLE 25. SCENARIO 220% HYBRID WITH OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 8,837 5,000 13,837 46MISO + SAPP 69,444 0 69,444 288NYISO 13,887 2,620 16,507 48PJM 28,192 5,000 33,192 97SERC 1,009 4,000 5,009 16SPP 86,666 0 86,666 245TVA 1,247 0 1,247 4TOTAL 209,282 16,620 225,902 745 Figure 2-10. Installed capacity-Scenario 2 77 Figure 2-11. State map of nameplate capacity-Scenario 2SCENARIO 3To create a contrast with Scenario 1, a large amount of wind generation is moved from the Great Plains nearer to the East Coast load centers. To bring about this shift, a large amount of offshore wind generation is required. Table 2-6 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-12, with the state-by-state illustration in Figure 2-13.TABLE 26. SCENARIO 320% LOCAL, WITH AGGRESSIVE OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 13,887 11,040 24,927 82MISO + SAPP 46,255 0 46,255 189NYISO 13,887 9,280 23,167 71PJM 38,956 39,780 78,736 244SERC 1,009 4,000 5,009 16SPP 50,958 0 50,958 139TVA 1,247 0 1,247 4TOTAL 166,199 64,100 230,299 746 78Figure 2-12. Installed capacity-Scenario 3 Figure 2-13. State map of nameplate capacity-Scenario 3 79SCENARIO 4Reaching 30% energy penetration requires more than 300 GW of wind resources in the NREL database. A large amount of offshore wind is required, and the amounts in the Great Plains are comparable to Scenario 1. Table 2-7 shows capacity by operating region. Locations and sizes of individual plants are shown in Figure 2-14, with the state-by-state illustration in Figure 2-15.TABLE 27. SCENARIO 430% AGGRESSIVE ONSHORE AND OFFSHOREREGION ONSHORE ~MWOFFSHORE~MWTOTAL~MWANNUAL ENERGY ~TWHISONE 13,887 11,040 24,927 82MISO + SAPP 95,046 0 95,046 405NYISO 13,887 9,280 23,167 71PJM 38,956 54,780 93,736 295SERC 1,009 4,000 5,009 16SPP 94,576 0 94,576 243TVA 1,247 0 1,247 4TOTAL 258,608 79,100 337,708 1,116 Figure 2-14. Installed capacity -Scenario 4 80 Figure 2-15. State map of nameplate capacity-Scenario 4 81SECTION 3: ANALYTICAL METHODOLOGY: DATA, MODELS, AND TOOLSThe study analysis focused on three major areas:

1. Developing conceptual transmission to accommodate the levels of wind
2. Assessing the impacts of the wind generation in each scenario on grid 3. Determining the level to which wind generation in each scenario contributes to resource adequacy (i.e., its capacity value).The analytical methods used in this study build on those established in previous integration studies conducted over the past 10 years (see, for example, EnerNex Corporation and Wind Logics 2004; Bai et al. 2007; GE Energy 2008). A chronological data set of wind generation and load data is the critical input for Hourly load data from across the interconnection for years corresponding to the National Renewable Energy Laboratory's (NREL) mesoscale data were obtained loads in 2024. The basic resolution for both load and wind data is 1 hour1.157407e-5 days <br />2.777778e-4 hours <br />1.653439e-6 weeks <br />3.805e-7 months <br />, although higher resolution (10-minute average) data are available from the mesoscale data for wind generation, and samples of higher resolution data were analytical methods use the chronological wind generation and load data over a 3-year period as inputs. Brief descriptions of the methods follow:Statistical analysis of wind generation and load data, separately and in combination, to assess impacts on operating reservesChronological production simulations, which, if correctly structured, are used to simulate scheduling and operation of the power systemMonte Carlo-based chronological resource adequacy assessment, which uses annual or multiannual hourly data for load and wind generation to determine the probability that available supply resources would not be able to meet demand.

82Notes: LOLE = loss of load expectation; ELCC = e~ective load-carrying capability.Figure 3-1. Overview of the study processThe consensus approach for assessing wind integration impacts is to simulate the scheduling and operation of the power system with wind generation over and load and their respective variability and uncertainty characteristics are represented in the input data. This prevents focusing only on severe events, like major wind ramps, that would be expected infrequently. the analysis:Development of the transmission overlays, where production simulations for the current system determine both the locations and the economic value of new transmission

83Evaluation of operational impacts (where scheduling and real-time system operations are mimicked as closely as possible and details such as incremental operating reserves required to manage wind generation and increased uncertainty caused by wind generation forecast errors are considered directly)Determination of new area import limits with expanded transmission for evaluation of wind generation and new transmission contributions to resource adequacy.The chronological wind and load data also support the preferred approach for assessing wind generation contributions to resource adequacy. With a chronological, Monte Carlo-based, probilistic resource adequacy assessment tool, the effects of wind generation on planning capacity margin can be calculated directly. Comparing these results to one without wind generation determines the MODEL DEVELOPMENTELECTRICAL REPRESENTATION OF THE EASTERN INTERCONNECTIONand generation. The Joint Coordinated System Plan (JCSP 2008) was an earlier effort to produce analytics of the electrical infrastructure that ignored regional transmission organization (RTO) and balancing authority seams while working under a common set of assumptions. The JCSP process included open stakeholder involvement in developing assumptions and verifying the base data. This open process gave all stakeholders the opportunity to review system. This reviewed data set consists of forecast loads, along with planned and existing generation, and incorporates existing transmission development plans (or concepts) to supplement the existing transmission infrastructure.Because a large amount of effort went into compiling and reviewing these data, the data were an appropriate starting point for this study. LOADDemand for all study regions was originally based on the information within Federal Energy Regulatory Commission (FERC) Form 714, 2006 North American Electric Reliability Corporation (NERC) electricity supply and demand (ES&D) 84benchmarked against various reporting entities within each region and were also given to all stakeholders during the JCSP participant review process. Through Most areas, however, deemed the PowerBase data to be suitable for the study.

The PowerBase database contains annual peak demand for each company however, because the Electric Generation Expansion Analysis System (EGEAS; company's peak demand and determine a peak coincident factor for each study region. This was accomplished by summing the individual hourly peaks for maximum sum of the individual hourly peaks is the coincident peak for the year.

Dividing the coincident peak by the sum of the individual company peaks gives a regional coincident factor, as seen in Table 3-1. Just like the demand assumptions, annual energy values are also available from TABLE 31.

SUMMARY

OF DEMAND AND ENERGY ASSUMPTIONS USED WITHIN THE SCENARIO DEFINITIONSREGION STUDY REGION COINCIDENCE FACTOR 2008NONCOINCIDENT PEAK DEMAND~MW2008COINCIDENTPEAK DEMAND~MW2008ANNUAL ENERGY~GWHREFERENCEANNUAL DEMAND ESCALATION~%REFERENCE ANNUAL ENERGY ESCALATION~%ENTERGY 0.9947 27,712 27,565 142,362 1.80 1.66ISONE 0.9892 28,227 27,923 1335,776 2.27 1.69MISO 0.9343 115,862 108,254 590,662 1.28 1.50MAPP 0.9608 9,915 9,526 49,941 1.20 1.61NYISO 0.9428 35,064 33,057 171,054 0.92 0.77PJM 0.9497 142,826 135,637 717,468 1.90 1.65SERC 0.9828 96,071 94,421 472,752 2.37 2.04SPP 0.9798 47,478 46,519 220,543 1.34 1.77TVA 0.9858 47,633 46,955 257,337 2.27 0.89Notes: MW = megawatts; GWh = gigawatt-hours; ISO-NE = New England ISO; NYISO = New York ISO; MISO = Midwest ISO (shortened to MISO in gures and tables because of space considerations); MAPP = Mid-Continent Area Power Pool; PJM = PJM Interconnection (shortened to PJM because of space considerations); SERC = Southeastern Electric Reliability Council; SPP = Southwest Power Pool; TVA = Tennessee Valley Authority.

85TABLE 32. STATUS CATEGORIES APPLIED TO ALL UNITS WITHIN THE DATABASESTATUSGENERATOR STATUS DESCRIPTIONACTIVEExisting online generation, including committed and uncommitted units. Does not include generation that has been mothballed or decommissioned.PLANNEDA generator that is not online, has a future in-service date, is not suspended or postponed, and has proceeded to a point where construction is almost certain. Examples would include generators for which an Interconnection Agreement has been signed, all permits have been approved, all study work has been completed, state or administrative law approval has been obtained, and so on. One exception to this rule is the inclusion of recently proposed nuclear expansions throughout the Eastern Interconnection. Although the units do not qualify as "planned" units, JCSP participants felt strongly that the units be considered as part of the planned generation eet. These units are used in the model to meet future demand requirements before the economic expansions. All units coming online between August 2007 and July 2008 show up as newly installed in 2008.FUTUREGenerators with a future online date that do not meet the criteria of the planned status. Generators with a future status typically fall under one of the following categories: proposed, feasibility studies in progress, applications for permits submitted, and so on. These generators are not used in the models but are considered in the siting of future generation.CANCELEDGenerators that have been suspended, canceled, retired, or mothballed.NETWORKduring the participant review process for a previous study of the Eastern Group Multi-Regional Modeling Working Group (ERAG MMWG) 2007 Series development. To better represent the latest and most accurate transmission stakeholders to incorporate planned or proposed transmission projects. GENERATION some asset owners supplied additional information, and the study team added the information to the database before model population. Stakeholders gave comments on adjusted capacities, ownership, in-service dates, and unit operational status. Building on the generators in the default PowerBase database, generator in the database or in a queue was assigned a status of active, planned, future, or canceled, as described in Table 3-2.Table 3-3 summarizes active generation with the nameplate capacity described by region and generator unit type. Table 3-4 gives a summary of capacity additions planned between 2010 and 2024. Finally, Table 3-5 summarizes the generation that is planned to retire before 2024.

86TABLE 33.

SUMMARY

OF ACTIVE GENERATION CAPACITY BY REGION ~NAMEPLATE CAPACITIES IN MEGAWATTSFUEL TYPE COMBINED CYCLE ~CCSTEAM T URBINE ~ST; COALCOMBUSTION TURBINE ~CTHYDROIGCC aNUCLEARBIOMASSSTEAM TURBINE ~ST; GASSTORAGEWINDTOTALENTERGY 13,422 5,919 3,446 691 5,182 95 15,760 28 44,543ISONE 12,610 3,195 2,614 1,734 4,389 935 6,932 1,675 70 34,154MISO 10,709 69,843 27,398 1,408 282 10,282 286 3,918 2,475 2,862 129,462MAPP 1,077 7,319 2,142 2,449 891 24 498 14,400NYISO 8,699 3,092 5,994 4,356 5,069 289 11,898 1,280 42 40,716PJM 24,484 70,656 30,974 2,647 30,769 669 9,253 3,625 514 173,590SERC 17,501 44,948 23,638 6,261 17,151 147 1,169 3,844 114,659SPP 11,059 23,426 8,932 2,570 2,056 56 12,943 296 2,001 63,338TVA 7,761 27,189 11,641 5,074 7,117 1,712 79 60,574TOTAL 197,322 255,587 116,779 27,191 282 82,906 2,473 61,896 14,935 6,065 675,435TABLE 34.

SUMMARY

OF PLANNED GENERATION CAPACITY BY REGION THROUGH 2024 ~NAMEPLATE CAPACITIES IN MEGAWATTSFUEL TYPE CCST ~COALCTHYDROIGCCNUCLEARBIOMASSST ~GASSTORAGEWINDTOTALENTERGY 18,00 1,585 3,000 6,385ISONE 110 70 50 607 837MISO 2,235 3,972 374 4,163 597 11,340MAPP 90 90NYISO 640 1,100 1,600 460 3,800PJM 1,127 92 2,071 4,706 862 8,858SERC 1,652 500 280 8,848 11,280SPP 500 2,995 1,407 2,748 7,380TVA 660 549 340 677 3,460 50 5,736TOTAL 8,724 9,692 3,571 2,748 25,777 50 5,144 55,705 aIGCC = integrated gasication combined cycle 87TABLE 35.

SUMMARY

OF GENERATOR RETIREMENT CAPACITY BY REGION THROUGH 2024 ~NAMEPLATE CAPACITIES IN MEGAWATTSFUEL TYPE CCST ~COALCTHYDROIGCCNUCLEARBIOMASSST ~GASSTORAGEWINDTOTALENTERGY 0ISONE 0MISO 561 561MAPP 0NYISO 177 825 1,002PJM 615 868 1,483SERC 62 62SPP 0TVA 0TOTAL 0 1,353 1,755 0 0 0 0 0 0 0 3,108TOOLSThe project team used three software tools to support the various analytical elements of the study. All are established tools in the electrical energy industry; their basic purpose and functions are described next. PROMOD IV OVERVIEW simulation system that incorporates extensive details of generating unit operating characteristics and constraints, transmission constraints, generation analysis, unit commitment/operating conditions, and market system operations. hourly energy prices, unit generation, fuel consumption, bus-bar energy market simultaneously adhering to a variety of operating constraints, including generating unit characteristics, transmission limits, fuel and environmental considerations, spinning reserve requirements, and customer demand.TRANSMISSION SYSTEM REPRESENTATIONand load buses, and transmission lines with reactance and resistance inputs. The transmission topology data are fully integrated with the commitment and dispatch algorithm so that generators are scheduled, started, and cycled while transmission generation commitment, and unit dispatch for all 8,760 hours0.0088 days <br />0.211 hours <br />0.00126 weeks <br />2.8918e-4 months <br /> under security-88also models transmission interfaces, enforcing bidirectional limits on groups of lines. constraints, so that the dispatch will still be feasible if the system experiences contingency can represent multiple transmission lines or generator outages (e.g., N-1, N-2, and more contingencies). Emergency ratings (summer and winter) on limitations on the number of contingencies or monitored lines. An iterative approach is used to include the effects of marginal transmission losses in markets that have loss components in their locational marginal price using a nonlinear solution, generators are penalized based on their incremental contribution to losses, and the simulation is repeated until the convergence incorporates that marginal loss component into the bus LMP. UNIT DISPATCHits variable costs, which include fuel (commodity, handling, and transportation); emissions; and operations and maintenance (O&M). Based on the reactance of the connected transmission lines, shift factors are calculated for each bus, so factors, and ramp rate limits into a linear program to optimize the dispatch across the entire system for each hour, honoring transmission constraints within a full security-constrained economic dispatch.UNIT COMMITMENTA multipass process is employed to establish day-ahead unit commitment for each generator based on forecast energy prices at the generator injection bus. Unit characteristics captured in the commitment and dispatch include multisegment operation, minimum capacity, ramp-up and ramp-down limits, start-up costs, minimum runtime and downtime constraints, and operating reserve contribution. The unit-commitment process also captures system operational effects, including transmission congestion, marginal losses, phase angle regulators, DC line operation, decisions within hourly dispatch. The following paragraphs describe the steps in the unit-commitment and dispatch solution.

89First, a preliminary unit dispatch is performed without enforcing unit runtime and downtime constraints, ramp rates, and start-up cost effects. This preliminary solution is designed to create a starting point for the price of energy in each hour that is not subject to multihour commitment constraints. This dispatch incorporates a full view of transmission congestion and other detailed operations. Wind units can be set up to be dispatched in the preliminary solution by designating them as "Firm" resources, or they can be excluded in the preliminary price formation by designating them as "Non-Firm." The preliminary dispatch is performed for a 7-day period, starting Monday at 1:00 a.m. and ending Sunday at 12:00 p.m. (midnight). This gives each generation injection site (bus) a unique 168-hour forecast for energy prices. The 168-hour look-ahead from Monday to Sunday is designed to be long enough to account for unit-commitment decisions based on multiday constraints (e.g., 48-hour minimum downtime). The second step in the unit-commitment process is to optimize the operation operating constraints and unit bid (or cost) inputs. A mixed-integer program a given hour, it is assumed the unit must be committed in that hour for load or reliability, and the program optimizes the run schedule for the surrounding is calculated by determining the price increase needed to allow the unit to break even over the given run period. This new bid is added to the unit cost from the preliminary unit dispatch for the next dispatch pass. Each unit is processed individually based on the forecast prices at its injection bus. The unit commitment is done for the entire week without knowing if any forced outages is re-optimized from the hour in which the unit returns to service. When all units have been processed, a second complete dispatch pass is done with all unit constraints in place and all commitment bid adders applied. The second dispatch results in a new forecast of bus prices and the commitment is re-resources that were not included in the preliminary passes. The dispatch process factors at each generation node, recognizes market import-export tariffs, and co-optimizes for spinning reserve requirements.

90as Firm resources, thus affecting the nodal prices used for unit commitment. This modeling creates the disconnect between day-ahead unit commitment and approach is used to model the effects of load uncertainty. GEMARSThe study team used GE Energy's Multi-Area Reliability Simulation (GE-MARS) program to calculate reliability indices. GE-MARS is a transportation-style model based on a sequential Monte Carlo simulation that steps through time chronologically and produces a detailed representation of the hourly loads and interfacing between the interconnected areas. GE-MARS calculates, by area or area group, the standard reliability indices of daily or hourly loss of load expectation (LOLE, in days per year or hours per year) and expected unserved energy (EUE, in megawatt-hours per year). For load-carrying capability (ELCC) of wind generation.The basic calculations are done at the area level, which is how much of the aggregated into the LOLE study areas.EGEASThe EGEAS software is used for long-term regional resource forecasting. EGEAS performs capacity expansions based on long-term, least-cost optimizations with multiple input variables and alternatives. Optimizations can be performed on a variety of constraints such as resource adequacy (loss-of-load hours), reserve minimizing the 20-year capital and production costs, with a reserve margin requirement indicating when new capacity is required. ASSUMPTIONSmake a number of assumptions. And because any assumptions about a scenario 15 years in the future would be subject to differences of opinion and debate, 91of discussion and interaction that took place in that earlier effort to reach some OPERATIONS IN THE EASTERN INTERCONNECTIONBecause the wind generation in the scenarios is not distributed evenly across the interconnection, either geographically or in a load-weighted sense, portions of the interconnection in the study scenarios have very high penetrations. These penetrations are well beyond the boundaries of actual experience with wind generation, resulting in effects on power system operation and control.Previous wind integration studies (see Bibliography) offer some important that the rules and processes that govern power system operations are an important factor in wind integration; structures that aggregate large amounts of load and allow wind generation to be more easily integrated. assumed that these market footprints would remain, and that the other portions of A further assumption was made about the details of scheduling and operations processes in each of the operating areas. For purposes of production modeling, the team assumed that operations in each area had three major elements:A day-ahead unit-commitment process, where forecasts of load and wind generation for the next operating day are assessed through a SCUC evaluation. Currently, this step is performed some time following the day-ahead energy market clearing.A real-time or subhourly energy market, where participating units are eco

-nomically dispatched on a frequent basis (5- to 10-minute intervals) based on short-term forecasts of net load (i.e., load minus wind generation).An ancillary services market that draws on many resources for the spinning and nonspinning reserves required for frequency support, balancing, and system security.At present, not all of the operating areas in the study footprint operate in this manner. With the study horizon of 2024 and the strong trends in the industry in these directions, however, the assumption is appropriate for this type of study.

92 3 The regions are MISO (which is divided into three regions), MAPP, SPP, TVA, PJM, SERC, Entergy, NYISO, and ISO-NE.CAPACITY EXPANSION MODEL ASSUMPTIONSExisting area generation queues do not typically have planned capacity beyond a 5- to 10-year window. This results in gaps for resource adequacy for modeling dispatched within the economic energy model.A regional resource forecast model estimates, on a consistent least-cost basis, the be incorporated into the planning models to maintain adequate reserves. For 3 The do not require capacity expansion analysis because they have adequate resource availability. Appropriate resource adequacy is needed for all of these areas to avoid generation biases from one region to another, which would skew economic energy movement. shown in Figure 3-2. All regions were studied as a whole with the exception EGEAS software has a limitation of 1,000 thermal generating units and the and West). The planning regions are the same as the existing regions. Each area is schedules and wind interchanges. From a capacity viewpoint, each area can supply its own needs.

93Figure 3-2. Study areaFUTURE FUEL COSTSregion as well as the 2024 average cost. The fuel costs are a primary driver in the dispatch of generation within the energy models. The fuel cost assumptions were vetted through an earlier participant process.

94TABLE 36. AVERAGE FUEL COSTS MODELED BY SYSTEMSYSTEMYEAR ~UNITCOALNATURAL GASURANIUMOIL HEAVYOIL LIGHTISONE2008 $/MBTU a 2.51 9.21 0.6 12.61 17.942024 $/MBTU 3.45 16.53 0.82 23.61 33.61ANNUAL GROWTH RATE %

2.01 3.72 1.97 4.00 4.00MISO2008 $/MBTU 1.61 8.35 0.66 12.61 17.942024 $/MBTU 2.2 15.67 0.91 23.61 33.61ANNUAL GROWTH RATE %

1.97 4.01 2.03 4.00 4.00MAPP2008 $/MBTU 1.13 8.06 0 12.61 17.942024 $/MBTU 1.55 15.38 0 23.61 33.61ANNUAL GROWTH RATE %

1.99 4.12 0 4.00 4.00NYISO2008 $/MBTU 2.19 8.98 0.52 12.61 17.942024 $/MBTU 3.01 16.3 0.71 23.61 33.61ANNUAL GROWTH RATE %

2.01 3.80 1.97 4.00 4.00TVA2008 $/MBTU 1.78 8.65 0.53 12.61 17.942024 $/MBTU 2.44 15.96 0.73 23.61 33.61ANNUAL GROWTH RATE %

1.99 3.90 2.02 4.00 4.00SERC2008 $/MBTU 2.14 8.5 0.5 12.61 17.942024 $/MBTU 2.94 15.81 0.68 23.61 33.61ANNUAL GROWTH RATE %

2.00 3.95 1.94 4.00 4.00SPP2008 $/MBTU 1.24 8.01 0 12.61 17.942024 $/MBTU 1.71 15.33 0 23.61 33.61ANNUAL GROWTH RATE %

2.03 4.14 0 4.00 4.00PJM2008 $/MBTU 2 8.81 0.54 12.61 17.942024 $/MBTU 2.74 16.12 0.74 23.61 33.61ANNUAL GROWTH RATE %

1.99 3.85 1.99 4.00 4.00ENTERGY2008 $/MBTU 1.47 8.29 0.55 12.61 17.942024 $/MBTU 2.02 15.54 0.76 23.61 33.61ANNUAL GROWTH RATE %

2.01 4.01 2.04 4.00 4.00 a Millions of British thermal unitsPRODUCTION COST AND OTHER ECONOMIC ASSUMPTIONSThe variable cost associated with supply of the energy to load is an important others. With a large model consisting of multiple operating areas, the balance of energy transfers (imports and exports) in a given region may not sum to zero over the year. Consequently, the net "position" for the area is a function of more than just the raw cost of production. Adjustments must be made to account for this transaction balance. Adjusted 95Annual APC =

where i represents each hour in a year of the study.

C ij is the production cost of generator j during hour i.

M is the number of total generators in the region.

Load_Weighted_LMP i is load-weighted LMP during hour i.

Generator_Weighted_LMP i is generator weighted LMP during hour i.

Purchase i is a company's purchase of megawatts during hour i.

Sale i is a company's sale of megawatts during hour i.rate of 3% was used to translate results into US$2009. Care must be taken with some of the results, however, because prices for individual fuels were escalated at different rates for different regions. For this reason, only some of the results in later sections are shown in US$2009. A 15% annualized revenue requirement is used to determine the annual cost of the conceptual transmission plan. This is obviously subject to change in actual construction, depending on the cost structure of the constructing transmission owners. RESOURCE ADEQUACY ASSESSMENTCapacity additions in the model are based on reserve margin requirements. Reserve margin is calculated as the difference in available capacity and peak coincident demand divided by the peak coincident demand.Reserve Margin

= Available Capacity - Peak Coincident Demand Peak Coincident Demand Peak demand is determined using the noncoincident annual peaks applied to load demand reaches its peak for the system (refer to Table 3-1 for demand assumptions). The available capacity is the maximum capacity available during generation. Firm generation is the percentage of a generator's maximum capacity that is counted toward calculation of the reserve margin. For example, wind units contribute 20% of their maximum capacity toward reserve margin calculations. Table 3-7 shows the modeled reserve targets.

96TABLE 37. TARGET RESERVE MARGINS BY REGIONREGION RESERVE TARGET ~%ENTERGY 15.0ISONE 15.0MAPP 15.0MISO 15.0NYISO 15.0PJM 15.5SERC 15.0SPP 15.0TVA 15.0GENERATION EXPANSION ALTERNATIVESturbines, nuclear facilities, and wind facilities. Before using the capacity expansion model, the project team eliminated other alternatives such as biomass, and hydro facilities as options because they were not economically competitive with the conventional resources under the assumptions applied to the analysis. that the capital costs for wind generation in this table are lower than what is assumed later in the report when total costs are tabulated. These lower values were artifacts of an earlier planning study. Because of the approach used here, Wind is given a 20% capacity credit against the required planning margin; all other units produce 100% of available capacity at peak system hours. Because of the wind modeling technique, resource adequacy calculations take into account need for additional capacity.

97TABLE 38. MODELED GENERATOR PROTOTYPE DATA VALUES IN US $2008 aALTERNATIVE2008 OVERNIGHTCONSTRUCTION COST~$/KW2008 VARIABLEO&M~$/MWH2008 FIXED O&M~$/KWBOOK LIFEOPERATING LIFECOAL 1,833 4.60 28.22 40 60CC 857 5.17 34.01 30 30CT 597 4.62 17.72 30 30WIND ~ONSHORE1,750 5.70 11.93 25 25WIND~OFFSHORE 2,440 18.67 15.55 25 25NUCLEAR 2,928 4.63 69.57 40 60 a All costs escalated at 3% annually during study period.Notes: kW = kilowatt; MWh = megawatt-hourenergy projections available through the PowerBase database. Demand response, however, is added to the individual areas to maintain existing penetration percentages through the study period, as shown in Table 3-9. The demand response units are modeled much like high-cost combustion turbines to limit capacity factors to values less than 1%.TABLE 39. MODELED PENETRATION OF DEMAND RESPONSE BY REGION ~AS PERCENTAGE OF PEAKSTUDY REGIONNONCOINCIDENT PEAK DEMAND ~MWMODELED DEMAND RESPONSE~MWRATIO OF DEMAND RESPONSE TO PEAK DEMAND ~%ENTERGY 22,712 50 0.18ISONE 28,227 2,400 8.50MISO + MAPP 125,777 4,362 3.47NYISO 35,064 2,014 5.74PJM 142,826 3,239 2.27SERC 96,071 1,745 1.82SPP 47,478 736 1.55TVA 47,633 2,309 4.85 98SECTION 4: DEVELOPING ECONOMIC TRANSMISSION OVERLAYS current installed capacity by nearly an order of magnitude. Transmission issues are already limiting wind energy development in some regions, so it is a near the much higher amounts of wind generation represented in the Eastern Wind This section describes the methodology and results of the transmission BACKGROUNDdeveloped through a planning process that had two basic objectives: (1) to reliability of the bulk power system in the face of growing demand. By building transmission facilities to interconnect with neighbors, capacity resources could be shared in emergencies, reducing the amount of excess capacity an individual utility must maintain to serve load reliably. Opportunities for economic Because it is primarily a source of energy, not capacity, wind generation does system with a major facility out of service. The status of conventional generating units during these periods is usually a given. With large amounts of wind generation, the disposition of other conventional generating units may not be so easily ascertained; in addition, high amounts of wind generation are likely in off-peak hours or seasons that might not be of special interest for reliability issues.A transmission planning method based on economics has been developed, transmission organization (RTO) for insight into transmission needs for 99to develop conceptual interregional transmission plans required for a 5% wind energy scenario and a 20% wind energy scenario. evaluate the transmission that would be needed to facilitate 20% and 30% wind delivery across a large geographical area, energy-based regional transmission planning is necessary to incorporate comprehensive economic assessment using production-cost simulations. By linking the markets across the entire Eastern regional transmission plan could outweigh its cost. Because the JCSP also focused on a 20% scenario, the results from that effort of the high-quality wind resources in the Great Plains and Upper Midwest. A similar level of regional detail was not available for other parts of the Eastern The transmission development methodology is a sequential process that focuses on a snapshot of a single future year. The steps in the process follow:

1. 2. Determining what generation capacity would be necessary to reliably This is accomplished through a formal generation expansion process that begins with the present and ends in the target year. Wind generation is accounted for by assigning an estimated capacity value, which is capacity for planning purposes. The expansion program then considers the new generation that must be built to meet regional planning margin requirements given the growth in loads and possible retirements of existing generators. Projected capital and operating costs over the planning horizon are used to optimize the expansion by minimizing total costs while maintaining resource adequacy.

100 3. Testing the result of the generation expansion step by running annual or multiannual hourly production simulations with the existing transmission network. Two cases are run:

a. A "copper sheet" case, where limits on all transmission facilities energy in any hour is the same across the entire system.
b. A constrained case, where transmission limits are applied. Congestion will result in unequal prices caused by less-than-generation.
4. Comparing results of the copper sheet and constrained cases. Costs of congestion across major transmission lines and interfaces are totaled for the annual period.
5. Using the accumulated congestion charges as a guide for developing new transmission.

generation scenarios, and this process is described in the following sections.APPLICATION OF EXPANSION METHODOLOGYTo begin the transmission development process, the study team used the Electric Generation Expansion Analysis System (EGEAS) tools described in Section 3 to conduct a regional capacity expansion analysis for each wind scenario. The objective was to maintain an approximate 15% reserve margin across the Eastern production and regional load data, wind generation was assigned a uniform capacity value of 20% in the EGEAS runs. EGEAS GENERATION EXPANSIONand will not depend on capacity in other regions for resource adequacy needs. served and no more, whether located internally or not.Figure 4-1 shows the nameplate capacity expansions required to meet the resource adequacy needs for each region. The information is, however, for 101The effect of wind on the capacity expansion model can be seen by comparing the three 20% wind energy scenarios to the 30% wind energy scenario. The added energy produced from the wind resources tends to be more competitive with the base-load generation in the off-peak hours. As a result, when increased wind resources are forced into the expansion model, the economic result is to remove base-load capacity (e.g., coal and nuclear) from the expansion and leave the more Notes: CC = combined cycle; CT = combustion turbine; IGCC = integrated gas combined cycle; IGCC/Seq = integrated gas combined cycle with sequestration; CC/Seq = combined cycle with sequestration; RET Coal = coal plant retirements; Replacement CC = replacement combined cycle; DR = demand response.Figure 4-1. Capacity expansion by scenarioSITING OF CAPACITYThe resources forecast from the expansion model for each of the scenarios are a philosophy- and rule-based methodology, and industry expertise, to site the forecast generation. data (AWS Truewind 2009). The thermal capacity was locally sited at various 102energy production) but others require support from wind located in external regions.Areas that meet the target energy on a regional basis, by scenario, are as follows:Areas with less than the target amounts by scenario include the following:RESULTSScenarios 1 through 4, respectively. With the same 20% wind penetration level, Scenarios 1, 2, and 3 have exactly the same thermal generation capacity and siting locations. With the increased 30% wind energy penetration in Scenario 4, 103Figure 4-2. Scenario 1 installed capacity sitesFigure 4-3. Scenario 2 installed capacity sites 104 Figure 4-4. Scenario 3 installed capacity sites 4-5. Scenario 4 installed capacity sites 105TRANSMISSION OVERLAY DEVELOPMENT The following sections describe the interim steps of the transmission expansion methodology along with results for the study scenarios.

PREOVERLAY ECONOMIC ANALYSISThe pre-overlay economic analysis results are the input information necessary for constrained base case and the unconstrained case assuming no transmission constraints. Examining the differences between the two cases reveals the following:The areas of economic energy sources and sinks needsbudget.Figure 4-6 shows the annual generation-weighted locational marginal pricing (LMP) contour map across the system for the constrained case in the 20% wind Scenario 2. The highest prices are on the East Coast. The price differentials are driven by a combination of transmission constraints and the cost of natural areas to high-cost areas. The LMP contour map shows the direction where the transmission should link the lower cost areas to the higher cost areas and relieve the transmission constraints. The more areas that are linked with the appropriately sized transmission, the greater the value the transmission can achieve.

Figure 4-6. Scenario 2 annual generation-weighted LMP for Scenario 2 106Figure 4-7 shows the annual generation difference between the unconstrained (red) and energy sink (blue) areas and gives insight into where the potential transmission lines and substations should be located. As seen in Figure 4-7, the price signal drives energy from low-cost source areas to high-cost sink areas if the transmission system is not constrained across the study footprint.

Figure 4-7. Scenario 2 generation di~erence between unconstrained case and constrained caseprice difference. Figure 4-8 shows the annual energy differences between the unconstrained and constrained cases on each interface for Scenario 2; it shows the candidate locations for overlay lines to increase power transfer.

107 Figure 4-8. Scenario 2 interface annual energy di~erence between unconstrained case and constrained caseTable 4-1 lists the top 24 interfaces with the largest annual energy differences between the unconstrained and constrained cases for each wind scenario; it essentially shows in tabular form the same information depicted in Figure 4-8. The additional transfer needs are calculated to deliver 80% of the annual energy differences on each interface between the unconstrained and constrained cases.

This information can be used to determine the type and size of the transmission lines and transformers.

108TABLE 41. TOP 24 INTERFACES WITH THE LARGEST ANNUAL ENERGY DIFFERENCEINTERFACE aJCSP 20% WINDEWITS SCENARIO 1EWITS SCENARIO 2EWITS SCENARIO 3EWITS SCENARIO 4ADDITIONAL TRANSFER NEEDS ~MEGAWATTS MWADDITIONAL TRANSFER NEEDS ~MWADDITIONAL TRANSFER NEEDS ~MWADDITIONAL TRANSFER NEEDS ~MWADDITIONAL TRANSFER NEEDS ~MWAMRN IN 26,878 28,856 22,901 13,223 24,609IN OH 19,334 20,843 16,594 9,633 17,081OH EPJM 16,126 18,662 14,378 8,715 12,928SPS SPP 7,174 13,482 13,983 7,743 13,399SPS EES 12,567 12,551 11,598 6,417 12,160IOWAAMRN 12,204 15,173 11,150 6,040 14,084EESTVA 9,472 10,173 9,039 4,879 9,205TVASOUTHERN 8,860 9,045 8,202 5,476 8,744SPPAECI 7,866 8,565 7,930 4,134 8,283NYISOISONE 10,331 9,128 7,405 6,202 6,967PJMNYISO 8,430 8,457 7,086 6,115 6,631WAPAMINN 1,865 9,243 6,633 2,443 9,164ATCAMRN 8,068 8,771 6,586 3,878 7,663MINNATC 6,575 8,647 6,260 3,186 8,148SPPIOWA 2,926 3,978 4,355 2,606 4,227AECIAMRN 4,618 4,585 4,300 2,142 4,512SPPAMRN 3,922 4,556 3,973 2,134 4,368AMRNTVA 4,691 4,571 3,925 2,367 4,224INMICH 4,568 4,937 3,846 2,254 3,990IESONYISO 8,678 4,971 3,817 2,630 3,499MICHIESO 4,184 4,070 3,238 2,349 2,801AECIEES 3,976 3,607 3,088 2,042 3,584MINNIOWA 3,424 4,102 2,866 1,425 3,931WAPAIOWA 2,392 4,343 2,847 994 4,262 a Interface and state names are abbreviated in this column because of space considerations. Please refer to the Abbreviations and Acronyms list.Adjusted production cost (APC) savings are calculated by taking the differences between the unconstrained and constrained cases for all four high-penetration wind scenarios. Table 4-2 lists the detailed APC savings for each region plus the proxy estimates of potential budgets available for transmission development, are 109TABLE 42. ANNUAL APC SAVINGS FOR EACH SCENARIO ~US$2024, MILLIONSREGION SCENARIO 1SCENARIO 2SCENARIO 3SCENARIO 4PJM 4,682 3,169 1,588 2,768MISO 2,529 1,832 1,288 2,883TVASUB 789 661 590 1,500MAPP 8,234 5,275 2,264 6,290SPP 6,534 6,765 4,071 5,691SERCNI 5,728 5,655 5,494 11,166E_CAN 2,652 2,306 2,389 1,823IMO 1,144 1,068 1,048 1,157ISONE 3,794 2,117 1,079 1,432MHEB 730 629 599 603NYISO 4,851 3,424 1,872 2,499ENTIRE EI 41,667 32,902 22,282 37,812Notes: SERCNI, E-CAN and TVASUB are monikers used in EWITS for subregions in the PROMOD IV model. IMO = the independent electricity market operator that covers Ontario; MHEB = Manitoba Hydro Electric Board; EI = Eastern Interconnection.TRANSMISSION DESIGN CONSIDERATIONS FOR EHV OVERLAYFigure 4-9 demonstrates simple decisions that enable the transmission lines to limit, the cost to deliver energy is lower with the higher voltage lines. For large Note: HSIL = high-surge impedance loadingFigure 4-9. Transmission and substation costs per megawatt-mile 110to collect energy from multiple wind resources that are geographically diverse, IDENTIFYING AND LOCATING NEW TRANSMISSION FACILITIES The study team developed the conceptual transmission overlays for all four wind scenarios using the pre-overlay economic analysis results and input from the Technical Review Committee (TRC). Local and regional knowledge represented by the TRC members was used to locate terminals for new transmission facilities Regional transmission plans in the western part of the interconnection were incorporated into the overlay design. Two existing conceptual plans with a regional focus were integrated into the overlays. Figure 4-10 shows a conceptual transmission portfolios required to meet the renewable portfolio standards (RPS) or goals, is depicted in Figure 4-11.

Figure 4-10. SPP EHV conceptual transmission plan 111Figure 4-11. Midwest ISO RGOS, Phase I, Preliminary Scenario T, 765-kV (green)Building on previous energy-driven transmission planning efforts, the inputs from the TRC were incorporated to create initial conceptual overlays for the resulting conceptual transmission overlays for the four wind scenarios are shown in Figures 4-12 through 4-15. All of the overlays are structured to allow a general west-to-east energy transfer. There are several reasons for such a bias. First, in all the scenarios, the western part of the interconnection has large amounts of wind generation and minimal system in roughly the geographical center of the interconnection favor west-to-east lines over more north-south orientations of long-distance facilities.A third major reason for the general west-to-east orientation of the overlays involves the representation of Canadian provinces in this study. No wind generation data were available for Canada, which precluded detailed study of energy transactions between border regions of the United States and the transaction, was the only consideration of wind generation outside the United States.

112resources, and its proximity to the northeastern U.S. load centers in particular offers the northeastern portion of the United States access to wind generation that is relatively local compared to wind generation in the Great Plains. The TRC recommended that such a scenario be considered in the future, if and when compatible wind data are available for those provinces.

Figure 4-12. EWITS Scenario 1 conceptual transmission plan Figure 4-13. EWITS Scenario 2 conceptual transmission plan 113 Figure 4-14. EWITS Scenario 3 conceptual transmission plan Figure 4-15. EWITS Scenario 4 conceptual transmission plan 114POSTOVERLAY ECONOMIC ASSESSMENTper-mile assumptions by voltage level and region, the estimated total line miles by voltage level, and the estimated cost in millions of US$2024 for the four a 25% adder to approximate the costs of substations and transformers; the total costs associated with an offshore wind collector system or some subregional transmission upgrades that would be required, which could be substantial, are not included in the total estimated cost. With approximately 21,666 miles of estimated total cost at $158 billion. wind resources in the western portion of the interconnection were used. The eastward). Because more wind is moved eastward and more offshore resources along the East Coast in Scenario 4, the overlay must be expanded to include ten wind resources in the Great Plains and Upper Midwest. Approximately 85% of wind scenarios.TABLE 43. COST PER MILE ASSUMPTION ~US $2024, MILLIONSREGION 345 kV 345 kV AC (double circuit)500 kV 500 kV AC (double circuit)765 kV 400 kV DC 800 kV DCMISO 3.2 3.9 5.6 9.4 7.1 3.8 6.0SPP 1.9 3.2 3.0 4.3 4.5 3.8 6.0PJM 7.2 12.0 9.2 15.4 16.0 3.8 6.0ISONE/NYISO 5.5 9.1 7.0 11.7 16.0 3.8 6.0 115TABLE 44. ESTIMATED LINE MILEAGE

SUMMARY

~MILESSCENARIO 345 kV 345 kV AC (double circuit)500 kV 500 kV AC (double circuit)765 kV 400 kV DC 800 kV DCTOTALREFERENCE 3,054 299 567 494 2,631 1,188 1,968 10,203SCENARIO 1 1,978 236 1,100 243 6,701 560 11,102 21,920SCENARIO 2 1,978 236 1,100 243 6,674 560 8,352 19,143SCENARIO 3 1,978 236 1,148 726 6,674 769 4,747 16,278SCENARIO 4 1,886 236 1,240 726 6,445 560 10,573 21,666 TABLE 45. ESTIMATED COST

SUMMARY

~US$2024, MILLIONSSCENARIO 345 kV 345 kV AC (double circuit)500 kV 500 kV AC (double circuit)765 kV 400 kV DC 800 kV DCTOTALREFERENCE 12,308 1,820 2,356 5,912 27,789 4,403 11,752 66,340SCENARIO 1 7,409 1,563 7,242 4,575 49,798 2,397 83,265 156,249SCENARIO 2 7,409 1,563 7,242 4,575 49,640 2,397 62,640 135,466SCENARIO 3 7,409 1,563 7,789 12,131 49,663 2,957 35,603 117,115SCENARIO 4 7,040 1,563 8,436 12,131 47,520 2,397 79,298 158,385An annual revenue requirement of 15% of the total overlay cost was used to calculate the annual cost of the overlay. All the dollar values represent the year 2024 only. Table 4-6 gives the annual transmission costs, APC savings, overlays and associated B/C ratios are indicative, and a further comprehensive B/C analysis of potential alternatives would be required before making any recommendations. TABLE 46. BENEFIT AND COST COMPARISON ~US $2024, MILLIONSSCENARIO2024 ANNUALTRANSMISSION COST2024 APC SAVINGS2024 B/C RATIO 1 23,437 28,648 1.22 2 20,320 22,194 1.09 3 17,567 13,095 0.75 4 23,758 18,676 0.79conceptual transmission overlays that meet both economic and resource adequacy of wind collector systems in the western portion of the interconnection and addresses more of the underlying system and the associated impacts. Because of time constraints, however, additional congestion problems remain to be tackled.

116Tables 4-7 through 4-10 give more details on the estimated investments needed to performed to ensure that the conceptual transmission overlays lower energy costs TABLE 47. ESTIMATED ADDITIONAL TRANSMISSION INVESTMENTS FOR SCENARIO 1VOLTAGETRANSMISSION OVERLAYEXISTING 500 KV ABOVETOTALNUMBER OF LINESLINE MILEAGENUMBER OF LINESLINE MILEAGEESTIMATED COST ~US$2024, MILLIONS345 kV 3 67 0 0 188345 kV AC (double circuit)2 49 0 0 230 500 kV 0 0 13 574 2,063500 kV AC (double circuit)0 0 0 0 0 765 kV 9 1,619 0 0 10,372TOTAL 14 1,735 13 574 12,853TABLE 48. ESTIMATED ADDITIONAL TRANSMISSION INVESTMENTS FOR SCENARIO 2VOLTAGETRANSMISSION OVERLAYEXISTING 500 KV ABOVETOTALNUMBER OF LINESLINE MILEAGENUMBER OF LINESLINE MILEAGEESTIMATED COST ~US$2024, MILLIONS345 kV 2 54 0 0 152345 kV AC (double circuit)2 49 0 0 230 500 kV 0 0 12 660 2,372500 kV AC (double circuit)0 0 0 0 0 765 kV 11 1,861 0 0 11,922TOTAL 15 1,964 12 660 14,676 117TABLE 49. ESTIMATED ADDITIONAL TRANSMISSION INVESTMENTS FOR SCENARIO 3VOLTAGETRANSMISSION OVERLAYEXISTING 500 KV ABOVETOTALNUMBER OF LINESLINE MILEAGENUMBER OF LINESLINE MILEAGEESTIMATED COST ~US$2024, MILLIONS345 kV 2 54 0 0 152345 kV AC (double circuit)2 49 0 0 231 500 kV 0 0 15 659 2,368500 kV AC (double circuit)1 142 0 0 849 765 kV 1 93 0 0 598TOTAL 6 339 15 659 4,198TABLE 410. ESTIMATED ADDITIONAL TRANSMISSION INVESTMENTS FOR SCENARIO 4VOLTAGETRANSMISSION OVERLAYEXISTING 500 KV ABOVETOTALNUMBER OF LINESLINE MILEAGENUMBER OF LINESLINE MILEAGEESTIMATED COST ~US$2024, MILLIONS345 kV 2 54 0 0 152345 kV AC (double circuit)1 20 0 0 94 500 kV 0 0 21 795 2,857500 kV AC (double circuit)3 295 0 0 1,767 765 kV 7 1,202 10 10 7,764TOTAL 13 1,571 31 805 12,634ANALYSISTransmission overlays were added to the production simulation model for each scenario to test their impact. REGIONAL GENERATIONWEIGHTED LMP CHANGESFigure 4-16 shows the comparison of the annual generation-weighted LMPs across the study footprint for Scenario 1. The diagram on the left represents the hub LMPs for the constrained base case, and the one on the right represents the hub LMPs for the overlay case.

118Figure 4-16. Scenario 1 annual generation-weighted LMP comparisonThe LMP change demonstrates the ability of the conceptual transmission overlay to the East Coast. With the conceptual transmission overlay, more low-cost energy in the western regions is available to energy markets and is economically transferred to the high-priced East Coast regions. As the result of the economic energy transfer, LMPs increase in the western regions with the increased base-load generation output, but decrease in the eastern regions because the output of the high-priced generation is displaced by the imported low-cost energy. back to end-use customers through regulatory mechanisms. Other mechanisms might have to be put in place to distribute the revenues back to load. Figures 4-17 through 4-19 illustrate the comparison of the annual generation-weighted LMPs across the study footprint for Scenarios 2, 3, and 4, respectively.

Figure 4-17. Scenario 2 annual generation-weighted LMP comparison

119Figure 4-18. Scenario 3 annual generation-weighted LMP comparison Figure 4-19. Scenario 3 annual generation-weighted LMP comparisonWIND CURTAILMENTTo accommodate increasingly high wind penetration levels, regional transmission infrastructure is needed to deliver substantial amounts of high-quality wind energy to remote load centers. Without new transmission corridors to access the wind resources, large amounts of wind curtailment would occur. To minimize the wind curtailment levels seen in the transmission overlay cases, the study team performed a sensitivity analysis to include negative wind dispatch price in the production-cost model. The renewable energy production tax credit (PTC) is the primary federal incentive to encourage wind power development.

For this sensitivity analysis, a negative $40/megawatt-hour (MWh) was assumed. This value includes the PTC along with renewable energy credits (RECs).

120Table 4-11 summarizes the wind curtailment for the base constrained case, the transmission overlay case, and the transmission overlay case modeling the negative $40/MWh wind dispatch price. With the transmission overlays to move the wind energy, the curtailment drops down, ranging from 3.61% to 10.04%. And the curtailment is further reduced to the range between 1.05% and 3.83%

with the negative $40/MWh wind dispatch price included. Section 6 discusses more detailed sensitivity analyses for this topic. TABLE 411. WIND CURTAILMENT

SUMMARY

SCENARIOCONSTRAINED CASE ~%TRASMISSION OVERLAY CASE ~%TRANSMISSION OVERLAY WITH 40$/MWh WIND DISPATCH PRICE (%)

1 47.55 7.11 3.53 2 37.78 6.73 3.83 3 18.94 3.61 1.05 4 36.39 10.04 2.83DESIGN OF HVDC OVERLAY TRANSMISSIONtransfer mostly from west to east and west to southeast for Scenarios 1 and 2. the eastern part of the interconnection to the southeast. The AC system is used to schedules and their associated AC systems. underlying system.

121Figure 4-20. An example showing 800-kV HVDC lines (black) tied by 765-kV lines (green) and underlying 345-kV lines (red) the initial 5,700 MW because there are fewer lines to distribute the contingency. disturbances or generator outages. The overlay has ample capacity to back up disturbances, the severity of the AC disturbances in the area of the contingency and elsewhere would be considerably reduced compared to the case with no largest generator. that would tap the lines in the middle of the line. Three terminal lines would reduce the area affected by a contingency and reduce the impact of a contingency 122contingency. The AC system would not have to deliver power over such long lines at the third terminal. The conceptual design of the overlay would be more robust than that of the example (an example of an overlay with three terminal Figure 4-21. A postcontingency example showing ve 800-kV HVDC lines (black) example tied by 765-kV lines (green) and underlying 345-kV lines (red)system for the example. The impact of a contingency is expected to reduce with distance from the area in which it occurs.

123Figure 4-22. An example of the assumed distribution of the ows on the underlying AC systemThe overlay is designed not to have an impact greater than 1,500 MW on any

1.
2. 3. The rating of the underlying AC systems to be able to withstand a contingency in its area. A rating of 1,500 MW is assumed for these examples.into a ready-for-construction transmission plan.

124SECTION 5: POWER SYSTEM REGULATION AND BALANCING WITH SIGNIFICANT WIND GENERATIONMatching the supply of electrical energy to the demand for electricity, over time frames ranging from seconds to decades, is a fundamental building block for maintaining resource adequacy in the bulk power system. Wind generation introduces additional variability and uncertainty that make the general task incrementally more challenging.POWER SYSTEM OPERATION AND CONTROLPower system operation is near the real-time end of the spectrum of the operating time horizon. To maintain system reliability in day-to-day operations, several functions are necessary. These functions have traditionally been performed by individual utility "control areas," and now can be performed by one or several entities in a balancing authority that have been approved by the North American Electric Reliability Corporation (NERC). These reliability functions can be categorized by different names and are sometimes broken down

1. Scheduling (unit commitment), system control, and dispatch
2. Reactive supply and voltage control from generation
3. Energy imbalance
4. Regulation and frequency response
5. 6. 7. Generator imbalanceAs a result of the Energy Policy Act of 2005, reliability standards are now mandatory in the United States, and NERC is the federally mandated Electric the list are called reliability-related services. These include the range of services, other than supplying energy for load, that are physically supplied by generators, transmitters, and loads to maintain reliability.OPERATIONAL STRUCTURE 125The four synchronous interconnections in the United States and Canada each were previously individual electric utility control areas. The restructuring of the electric power industry over the past two decades and the emergence of wholesale energy markets have reduced the number of both BAAs and balancing authorities (Figure 5-1). Further consolidation is expected regional transmission organizations (RTOs) are examples. Balancing authorities market, located in the Midwest Reliability Organization (MRO), Mid-American (SERC), and ReliabilityFirst Corporation (RFC) regional reliability organizations, market started up. The SPP RTO began market operations with an Energy supplant conventional individual balancing authority functions within its market footprint. reliability-related services that involve the control of generation to meet demand, facilitate the delivery of wind energy, and maintain the security of the bulk power system is of primary interest. All are covered in this section, which also focuses on the control of generation in real time in response to changes in wind generation and load. The generation capacity assigned to serve these roles is in the Union for the Co-ordination of Transmission of Electricity (UCTE) in Europe are different than those used in the United States. Even within the United not uniform across the country. Reliability Standards.

4include regulating reserve, and the general category of operating reserve 126 4 See http://www.nerc.com/les/Glossary_12Feb08.pdf. Accessed December 2009.does. Mapping each of these terms to the reliability-related services in the NERC Functional Model is also not straightforward.evaluated are as follows:Regulating reserve: Generation responsive to automatic generation control (AGC) that is adjusted to support the frequency of the interconnection and compensate for errors in short-term forecasts of balancing area demand.Contingency reserve: The unloaded capacity carried to guard against major system disruptions such as the sudden loss of a large generating unit or major transmission facility.that is synchronized to the system and fully available to serve load within reserve consisting of generation that is either synchronized to the system NERC DCS.

127Notes: WECC = Western Electricity Coordinating Council; FRCC = Florida Reliability Coordinating Council; NPCC = Northeast Power Coordinating Council; TRE = Texas Regional Entity. The Mid-Atlantic Area Council (MACC) and the East Central Area Reliability Coordinating Agreement (ECAR) are NERC reliability regions that no longer exist.Figure 5-1. NERC reliability regions and balancing authorities as of January 2005 (top) and August 2007 (bottom) 128 Figure 5-2. U.S. RTOs 129TABLE 51. EXCERPTS FROM NERC GLOSSARY OF TERMS RELATED TO OPERATING RESERVES aTERMDEFINITIONANCILLARY SERVICEThose services necessary to support the transmission of capacity and energy from resources to loads while maintain

-ing reliable operation of the transmission service provider's transmission system in accordance with good utility practice.

bCONTINGENCY RESERVEThe provision of capacity deployed by the balancing author

-ity to meet the DCS and other NERC and regional reliability organization contingency requirements.OPERATING RESERVEThat capability above rm system demand required to pro

-vide for regulation, load forecasting error, forced and sched

-uled equipment outages, and local area protection. Consists of spinning and nonspinning reserve.OPERATING RESERVESPINNINGThe portion of operating reserve that consists ofavailable to serve load within the disturbance recovery period that follows the contingency eventthe disturbance recovery period after the contingency event.OPERATING RESERVESUPPLEMENTALThe portion of operating reserve that consists of-nized to the system) that is fully available to serve load within the disturbance recovery period that follows the contingency eventthe disturbance recovery period after the contingency event.REGULATING RESERVEAn amount of reserve that is responsive to AGC, which is suf

-cient to provide normal regulating margin.SPINNING RESERVESynchronized unloaded generation that is ready to serve ad

-ditional demand.

aAdapted from http://www.nerc.com/les/Glossary_12Feb08.pdf. Accessed December 2009.

bFrom Federal Energy Regulatory Commission (FERC) Order 888-A. See http://www.ferc.gov/legal/maj-ord-reg/land-docs/order888.asp. Accessed December 2009.MANAGING VARIABILITYEach BAA must assist the larger interconnection with maintaining frequency with real power demand is the means by which frequency is maintained. Regulation and load following are mechanisms for achieving this control under normal operating conditions. Figure 5-3 illustrates the load characteristics that are continuous, and can be roughly separated into two components:Fast variations, nearly random in nature, that result from a great number

130Slower trends that are relatively predictable, such as the rising load in the morning and the falling load through the evening into nighttime.Figure 5-3. Depiction of regulation and load-following characteristics of demandGeneration units on regulation duty are adjusted to compensate for random or sudden changes in demand. These adjustments take place automatically through AGC and occur, depending on the characteristics of the balancing area, over tens of seconds to a minute. Regulation movements both up and down are required, and the amount of net energy over a period is small because the movements tend to cancel each other. To offer regulation, therefore, a generating unit must reserve capacity and operate below its maximum (to reserve room for upward movement) units that meet the balancing authority's requirements for providing regulation and frequency service can participate in the regulation market. The term "load following" does not appear in NERC's glossary, but is generally taken to mean the adjustment of generation over periods of several minutes to hours to compensate for changes in demand. Generation movement is in response to economic-dispatch commands from the balancing area energy points are determined from short-term forecasts of demand, and generating units participating in that market are instructed to move to the forecast clearing point. Subhourly market intervals as short as 5 minutes are used today, with the clearing points established two or three intervals prior. Subhourly markets are dispatched economically, meaning that the least costly units available (i.e., those participating in the subhourly market) that satisfy 131system security constraints are called on to follow the forecast change in demand. Regulation service requires a commitment on the part of generators to leave capacity both up and down and to allow their units to be moved automatically by the market operator. Consequently, analysis of current market operation reveals that regulation can be quite expensive (Kirby et al. 2009). Conversely, load following obtained in subhourly markets is not. Although prices within the hour can vary dramatically, on average prices in subhourly markets track day-ahead energy prices quite closely. This has important consequences for the method used to calculate incremental operating reserve requirements with large amounts of wind generation.MEASURING CONTROL PERFORMANCEA running evaluation of control performance is kept for each BAA. The primary measure of control performance is area control error (ACE). The equation for a BAA's ACE has interchange and frequency error terms:

where A = the sum of the actual interchange with other balancing areas S = the total scheduled interchange with other balancing areas

~ change with frequency F A = the actual frequency of the interconnection F S = the scheduled frequency of the interconnection; usually 60 Hz, although there are times when the scheduled frequency is slightly above or below the nominal value to effect what is known as "time error correction" ME = metering error, which is neglected for the purposes of this discussion.ACE is computed automatically by the balancing area EMS every few seconds. The adequacy of generation adjustments by the balancing area operators and Control Performance Standard 1 (CPS1), uses ACE values averaged over a contributing to this frequency depression. Conversely, if the ACE were positive during that period, overgeneration in the balancing area would help restore the interconnect frequency.

132The CPS1 score for balancing authorities is based on performance over a rolling 12-month period. This score must be greater than 100%, which is an artifact of the equations used to compute the compliance factor. Maintaining adequate capacity on AGC is a major factor in complying with CPS1. ACE over a 10-minute period. Over each period, the 10-minute average ACE for which are unique for each balancing area, are generally based on system size. TABLE 52. 2009 CPS2 BOUNDS FOR SELECTED EASTERN INTERCONNECTION BAA sBAAESTIMATED PEAK DEMAND ~MWFREQUENCYBIAS ~MW/0.1Hz)BIAS/LOAD

~%BIAS/TOTAL BIAS~%L10~MWNPCC 93,851975 1.04 14.98IESO 25,657 1.11 4.38 128.10ISONE 28,480285 1.00 4.38 128.10NBSO 5,54763 1.14 0.97 60.23NYISO 34,167342 1.00 5.25 140.33RFC 245,1752,480 1.01 38.10MISO 110,6251,106 1.00 16.99 252.36PJM 134,4281,344 1.00 20.65 278.19Note: IESO - Independent Electricity System Operator; NBSO = New Brunswick Security Operator. Source: Adapted from http://www.nerc.com/docs/oc/rs/CPS2Bounds_2009.9b.pdf. The CPS2 metric is tabulated monthly. To comply with CPS2 requirements, 90% or more of the 10-minute average ACE values must be within the designated L10 bounds for the balancing authority. Minimum performance allows 14.4 violations per day. Most balancing authorities maintain CPS2 scores in the mid-90% range. The equations for average ACE and CPS2 follow:combination of CPS1 and CPS2 scores:Out of compliance: CPS1 < 100% or CPS2 < 90%.Compliance is based solely on control performance relative to the required scores 133operating area. Each operating area must establish policies and practices to comply with the NERC standards.Field trials of a new reliability-based control standard (NERC draft standard would replace CPS1 and CPS2. The new metrics are designed to improve interconnection frequency support, reduce short-term frequency deviations caused by ramping associated with transaction schedules, and ensure timely transmission congestion relief. The effects of the new standards on the challenge studied quantitatively. MAINTAINING SYSTEM SECURITYTo achieve high levels of reliability, the bulk power system must be operated so that it can withstand the loss of major elements without cascading failure reliability-related services listed previously are intended to preserve bulk power system security. Contingency reserve is the conventional name for the spare generating capacity that can be called on in system emergencies. The spinning portion of the contingency reserve is synchronized with the grid and ready to respond immediately; off-line capacity that can be called on, started, and synchronized nonspinning or supplemental contingency reserve. Unlike reserves for regulation, which are for supporting normal system operations within applicable reliability criteria, contingency reserves that are spinning are not dispatched continuously by AGC in response to ACE and are held in reserves for system emergencies. They are also unidirectional, in that the Currently, the basis for the required contingency reserves varies across the two largest loss-of-source events, which could result from a single contingency. For example, in an operating region where the largest plant is a 900-MW nuclear unit, enough additional generation must be available to cover the sudden loss reliability regions, a substantial portion of this additional generation must be synchronized with the grid (i.e., spinning). The required fraction of contingency reserves that must be spinning is often about 50% of total contingency reserves.

134would begin to decline because the amount of load now exceeds the available supply. As frequency declines, however, governors on all generating units, whether they are regulating units, units participating in the energy market, increase the mechanical power inputs to the generators. The system operator would use the operating reserves to replace the loss of generation. The NERC DCS requires balancing authorities to rebalance their systems within 15 minutes of a major disturbance and to restore the deployed contingency reserves within 105 minutes. EFFECTS OF WIND GENERATION ON POWER SYSTEM CONTROLActions to support frequency and maintain scheduled interchanges in a BAA are driven by the variety of errors in the generation and load balance. As a result, the effects of wind generation's variability and uncertainty on the net variability of wind generation affects power system control. Measurable impacts would be manifested in increased requirements for regulation capacity and load-following capability. Wind plants typically do not affect contingency reserve requirements because the individual generators are relatively small.Previous integration studies (see, for example, EnerNex Corporation and WindLogics 2004; GE Energy Consulting 2007; and AWS Truewind 2009;) have shown that the net variability concept is extremely important and the effects of aggregation and diversity are quite powerful. With load alone, in practice, the much less than for smaller areas. The same phenomenon is observed with wind generation because of spatial and geographic diversity effects. As the number of turbines grows and the area over which they are installed expands, the aggregate variability declines. When these aggregations increase to span multiple balancing examining impacts on current operating protocols.Figure 5-4 illustrates the effects of diversity on the variability of wind generation generation over a 10-minute interval; the value plotted on the vertical axis is the standard deviation of all incremental changes over 3 years of data for hourly production levels (per unit) corresponding to the value on the horizontal axis. The curves illustrate that more variability can be expected when the wind generation is in the midrange of the aggregate nameplate production. Second, more wind is aggregated.

135 Figure 5-4. Normalized 10-minute variability for ve di~erent groups of wind generation. The 500MW scenario is part of the 5,000-MW scenario, which is part of the 15,000-MW scenario, and so on.The magnitude of the effects of diversity on the variability of the balancing authority net load will depend on the amount of wind generation relative to load, the variability of load alone, and the amount of diversity that characterizes the aggregate wind generation. Figure 5-5 uses actual load and wind data for 10-minute interval to the next, along with the variability of load net of wind variability because of the much higher amount of installed capacity. The converse Scenario 3, with much more installed wind generation capacity, the effect is much more pronounced.

136Figure 5-5. Ten-minute variability of load and net load for MISO (left) and PJM (right). Scenario 1 is shown at the top and Scenario 3 is depicted on the bottom.Changes in wind generation over other time frames must also be factored into operational practices. Large drops in wind energy production could be as large as the contingency for which operating reserves are carried, but there would be could be lost in an instant, producing 900 MW 1 minute and going off line the instead they can evolve over several hours. This is caused by the many individual turbines, the large geographic area over which they are installed, and the time it takes for major meteorological phenomena such as fronts to propagate. Smaller, but more frequent, changes in wind generation over 1 to 4 hours4.62963e-5 days <br />0.00111 hours <br />6.613757e-6 weeks <br />1.522e-6 months <br /> are also operationally important. On these time scales, uncertainty about how much wind generation will be available becomes more important than variability. Because of the short lead time, replacement capacity for forecast wind generation that does not materialize in this time frame must be found. This replacement capacity can come from units already committed, regulating reserves (until economic replacement energy can be committed), units with quick-start capability if Consequently, the expected error in wind generation forecasts over these horizons could play a role in the policy and practice for operating reserves. A centralized wind production forecast will assist balancing authorities in mitigating the impact of changes in wind generation; a level of operating reserves may, however, still be required to mitigate the remaining errors.

137MODELING AND ANALYSIS FOR ASSESSING WIND INTEGRATION IMPACTSjudgment and technical knowledge of power system operation and control to develop a methodology for estimating how wind generation in the study scenarios would be managed in real-time operations.Develop a process for mapping these requirements to the chronological production simulations that will be used to assess overall wind integration impacts.

The second bullet is very important to the overall analytical methodology wind generation are accounted for, at least approximately, in the production simulations by setting constraints on the unit-commitment and economic-aside and not used to serve load. in the production simulation at increments of 1 hour1.157407e-5 days <br />2.777778e-4 hours <br />1.653439e-6 weeks <br />3.805e-7 months <br /> or more. Additional output that can be changed over a single hour, maximum and minimum output, start-up and shutdown times, and minimum runtimes and downtimes. The unit-commitment and economic-dispatch steps must observe these constraints such as minimum load and minimum generation periods, are evaluated in the as dump energy (see Glossary) or load that is not served. ASSUMPTIONSThe U.S. electric power industry is trending toward larger effective operating pools, through either energy markets or interarea operating agreements. Previous wind integration studies (see Bibliography) concluded that larger operating areas are an effective means for managing wind integration because they take natural advantage of geographic diversity of load and wind and aggregate a larger set of discrete generating units to compensate for load variations; they also offer frequent economic dispatch of units with movement capability to follow slower variations in balancing area demand.

138interconnection.

Figure 5-6. Eastern Interconnection balancing authorities (existing on left; assumption for study on right)The study team further assumed that by 2024 all operating areas will have a uniform structure in terms of market products, unit dispatch, and real-time consists of the following:A day-ahead energy market followed by a security-constrained unit commitment (SCUC) later on the day before the operating day.A real-time energy market, cleared at frequent intervals during the operating hour. Each real-time market clearing point is based on short-term forecasts of load and wind generation. To align with the data the market clearing point based on information available at the previous 10-minute interval.An ancillary services market, where a large pool of resources competes to The areas modeled in this study currently operate according to these assumptions in varying degrees. Although the progress in consolidating and operation of the entire study footprint under these assumptions by 2024 is not a foregone conclusion. Additionally, the project team assumed that reserves could be shared across the entire operating area footprint; transmission congestion internal to a region does not create subregions with reserve requirements that must be met locally. There is a general recognition that wind generation, in the current operating and markets constructs, would face very real barriers to realizing these levels 139of wind energy penetration. This analysis, then, looks at wind impacts in a possible "future world" operating and market construct that might be able to accommodate high levels of wind. This study team also recognizes that considerable work remains to be done before this operating scenario could be realized.

The study analysts used existing practice as a starting point for estimating the amount of regulating reserve required for load alone. They assumed a value of 1% of the hourly load. Although that fraction of the forecast daily peak load practice and policies, 1% of hourly load is a reasonable working assumption. MAPPING RESERVE REQUIREMENTS FOR PRODUCTION SIMULATIONS The methodology used in this study for assessing the impacts and cost of integrating wind energy into a utility balancing area is based on chronological simulations of scheduling and real-time operations. The study team used production costing and other optimization tools to conduct these simulations. a result, many details of real-time operation cannot be simulated explicitly. the units on AGC used by the EMS for fast response to ACE and the capacity that is frequently economically redispatched to follow changes in balancing various tools. At this level of granularity, the reserve requirements for the system are constraints on optimization and dispatch. Supply resources are designated by their ability to contribute to system requirements in one or more reserve categories. During optimization or dispatch, the solution algorithm must honor system reserve needs, meaning that some capacity cannot be used to meet load The reserve requirements with wind generation for the study operating areas were computed on a technical basis from the functional considerations for system reliability and security. To allow their use in the production simulations, the various reserve components had to be translated into the reserve categories considered by the simulation tools. For the large-scale production simulations in this study, only two types of reserves could be considered: spinning and nonspinning. Note that synchronized and nonsynchronized would be clearer service. But because the simulation tools actually use the terms spinning and nonspinning, they are retained here.

140Table 5-3 shows the mapping for the reserve types discussed in this section. By compensate for changes in balancing area demand to help control the frequency two components that were assumed to be additive. And, per the NERC glossary team assigned forecast error as a regulation category as well. The rationale for this is explained in detail later in this section.

study scenarios as described in the next section. TABLE 53. MAPPING OF RESERVE COMPONENTS IN CATEGORIES FOR PRODUCTION SIMULATIONSRESERVE COMPONENTSPINNING ~%NONSPINNING ~%REGULATION~VARIABILITY 100 0REGULATION~FORECAST ERROR 100 0CONTINGENCY 5050CONTINGENCY RESERVESgenerator within a balancing authority or as part of a reserve-sharing group, it the same level of reliability at varying levels of wind penetration. Contingency reserves would need to be increased if it were determined that the total output current contingency and has the potential to trip off line within a few minutes. of wind generation in the operating area, but would instead be a function of conventional equipment and the network, as is the current practice. reliability regions and reserve-sharing groups. Consequently, it was necessary information was available from current practice, the total contingency reserve operating area. At least half of this requirement was required to be spinning.

141The transmission overlays developed in Section 4 result in large high-voltage individual terminal is 1,600 MW, which would mean that the total contingency assume a self-redundant design; the maximum transfer on any single DC element of the overlay is limited so that if it were to fail, the transfer could be to 1,500 MW. Consequently, the contingency reserve requirement is established by the existing AC equipment for each area, and does not vary between the Reference Scenario and the high-penetration scenarios. Table 5-4 lists the assumed contingency reserve requirements for each operating region and scenario. The requirement is split 50/50 between spinning and TABLE 54. CONTINGENCY RESERVE REQUIREMENTS BY OPERATION REGION FOR 2024REGIONCONTINGENCY RESERVE REQUIREMENTALL SCENARIOS~MWSPINNING/SUPPLEMENTAL SPLIT ~%ISONE 1,158 50/50NYISO 1,200 50/50PJM 3,350 100/0SPP 1,539 50/50MISO 2,271 100/0TVA 1,750 23/77SERC (partial)1,140 50/50Note: The spinning contingency reserve assumed for PJM is high by about 50%. Because of the size of the PJM market, e~ects of the error on the results were deemed to be minimal. REGULATION AND LOAD FOLLOWINGThe approach for calculating the incremental regulation and load-following capacity required to maintain control performance in each study BAA was based on observations from current market operations and experience from previous studies. The minute-to-minute variability of wind generation, relative to that of the aggregate load, is very small. Because the National Renewable Energy Laboratory's (NREL) mesoscale data only goes down to a 10-minute resolution, actual wind data collected by NREL (Wan 2004) and others was used for the analysis in the quicker time frames. Those measurement data show that the standard deviation of the minute-to-142plant, based on separating the fastest variations from longer term trends using a 20-minute rolling average window. (As the results show, the details of this critical to the results and conclusions.)Minute-to-minute variability is also uncorrelated between individual wind plants and between wind and load. Considering a BAA with 100 gigawatts (GW) of load and 60 GW of wind generation, the impact of wind generation on the fast variations of the net BAA demand can be estimated:Assume that the 60 GW of wind generation is made up of 100-MW plants MW plants exhibits a minute-to-minute variability of 1 MW (as measured by the standard deviation of these variations), and they are uncorrelated with similar variations from other wind plants in the sample, the standard deviation of the variability for all 60 GW would be as follows:

Assume that the 1% regulation amount carried for load alone (100 GW of load in this example) is three times 5 the standard deviation of the load variability on this same time scale:

The standard deviation of the load net of wind generation, which is a basis for the regulating reserve, can be computed assuming that the fast variations from load are not correlated with those from the aggregate wind generation:As the calculations show, the effect of the fast variations in aggregate wind production is negligible. becomes apparent that wind variability would most likely have larger impacts on time scales associated with the subhourly markets and economic dispatch of generating resources.

5 A multiple of 3 times the standard deviation encapsulates almost 99.9% of all samples in a normal distribution. There is precedent in the U.S. electric utility industry for using a multiplier of 3, although instances of higher multiples can be found. The multiplier assumed here is thought to be more appropriate for the very large balancing areas dened for the 2024 scenario. In smaller balancing areas, multipliers of up to 5 are used.

143Subhourly market clearing points are based on short-term forecasts of demand. on projections of demand made 15 to 20 minutes before the interval. Participating units are instructed to move to cover the projected change in load; any difference between the forecast load and the actual load for the interval (assuming that all generating units follow dispatch instructions precisely) will effectively "spill over" into the regulation bin. accurate. For wind generation, the variations over these same time periods are less so. Errors in the short-term forecast of wind generation will therefore increase the requirement for regulation. used to estimate this impact on regulation. Using a persistence forecast, where the average production for the last several intervals (six intervals in this case) is the forecast for the next 10-minute interval, the expected error in this simple, short-term wind generation forecast can be easily calculated and characterized. Persistence performs reasonably well as a forecast technique for limited horizons, but over all the intervals in a year, those techniques might not outperform simple persistence. The team's objective here was to employ a simple, yet reasonable, approximation to a more sophisticated approach that would be used in practice. Figure 5-7 illustrates the short-term forecast errors for load and wind generation with data from one of the scenarios and operating regions. Here the study team assumed that the subhourly market operates on 10-minute intervals (to match the resolution of data available for this study), and the load forecast is generated one interval prior. A simple regression-extrapolation technique performs very well for forecasting load; this is most likely caused by the smoothness of the for the expected load shape and other factors that would further improve performance near peak intervals.The persistence forecast for wind generation performs reasonably well, but the variations at 10-minute intervals for even this large amount of wind generation exhibit more volatility than is observed in the aggregate load. Consequently, the errors in wind generation forecasts dominate the net error, as Figure 5-8 shows.

144Figure 5-7. Illustration of short-term (next 10-minute interval) forecasts of load and wind generation Figure 5-8. Errors in short-term forecasts of load and wind generation; load error is assumed to be zero in the mathematical procedureThe high-resolution data available for the study allowed the expected errors in short-term wind generation forecasts to be statistically characterized. The errors for each interval forecast were sorted into deciles based on the average hourly 145production at the time of the forecast. The errors in each of the deciles appear to be normally distributed, so the standard deviation was calculated and used as a measure of the expected forecast error.Figure 5-9 shows the result for one scenario and operating region. The maximum expected error occurs in the midrange of the aggregate production, which is expected, because it would be where the largest number of turbines is operating on the steep part of their power curves. For low levels of production, the error is small because the output is small; at higher production levels, the error also declines, because in this region, many turbines are operating above rated wind production.Figure 5-9. Illustration of short-term (10-minute ahead) wind generation forecast errors as a function of average hourly productionThe empirical expected error characteristic can be approximated with a quadratic expression as shown in Figure 5-9. The input to this expression is the average hourly production, and the output is the standard deviation of the expected error in the short-term wind generation forecast for the current level of wind production. Fast variations in load are almost certainly uncorrelated with the short-term forecast errors for wind generation. Therefore, the regulation requirements for load alone and short-term wind generation forecast errors do not add arithmetically. To account for this, the individual requirements are combined as a root of the sum of the squares. regulation capacity carried is equal to 1% of the hourly load. The total spinning 146reserve carried forward to the production simulations is the regulation amount With wind generation, the regulation reserve is augmented to account for the short-term wind generation forecast errors using statistical characterizations like the one shown in Figure 5-10. The resulting regulation reserve requirement, using this characterization, follows:whereST operating area and wind generation scenario.The amount of regulation capacity is taken to be three times the standard deviation of the combined variability of load and wind, which accounts for the division of load regulation by three and the multiple of three on the radical in the previous equation. Again, a multiplier of 3 was selected because of the large size As described previously, movements of generators to follow trends in load were assumed to come from the subhourly energy market. Economic dispatch in the production simulation honors individual unit ramp rates on an hourly basis, and, to reiterate, the average price for energy in the subhourly market was assumed not to diverge from the day-ahead price. As a result, the movements of generation to follow trends in the aggregate load are reasonably captured in the production simulations. This is, of course, based on an additional assumption that Uncertainty in the amount of wind generation to be delivered in the next hour also has an effect on the reserve picture. A procedure similar to that the team employed to characterize the very-short-term forecast errors can characterize the expected hour-ahead error for wind generation in each operating area and scenario (Figure 5-10). The expected next-hour forecast errors exhibit characteristics similar to those of the very-short-term forecasts; the highest errors occur when the aggregate wind production is in the midrange of capability, and the errors decline for both lower and higher production levels.forecast assumption, is equivalent to the forecast being more than what actually 147TABLE 55.

SUMMARY

OF RESERVE METHODOLOGY FOR STUDY SCENARIOSRESERVE COMPONENTSPINNING ~MWNONSPINNING ~MWREGULATION ~VARIABILITY AND SHORTTERM WIND FORECAST ERROR 0REGULATION ~NEXTHOUR WIND FORECAST ERROR 0ADDITIONAL RESERVEnext hour wind forecast error CONTINGENCY 50% of 1.5 x SLH50% of 1.5 x SLHfraction)TOTAL ~USED IN PRODUCTION SIMULATIONSSUM OF ABOVESUM OF ABOVE xxwould be held to cover next-hour forecast errors, which are expected to be frequent (once or more per day). The amount of additional spinning reserve was set at one standard deviation of the expected error. Additional supplemental or nonspinning reserve was also allocated to cover the larger but less frequent forecast errors. An amount equivalent to twice the standard deviation of the expected next-hour wind generation forecast error was used here.

Figure 5-10. Standard deviation of 1-hour persistence forecast error for PJM in Reference CaseTable 5-5 summarizes the elements of the spinning and nonspinning reserves used in the production simulations. Because hourly wind and load are inputs number. Using the statistical characterizations of short-term and next-hour scenarios within the reserve determination.

148Load forecast errors, both in the very short term and for the next hour or hours, have similar effects on the regulating and load following reserves. Some of these errors were considered in the assumption of 1% regulation for load. With analysts used the process to assess whether requirements with wind generation could be applied to determine the regulation and load-following requirements for load. Table 5-6 gives an example of the calculations used to determine the hourly regulating and spinning reserve requirement for each operating area. Hourly load (column 1) and wind generation (column 2) are the key inputs, along with the equations from Figures 5-9 and 5-10. These equations were developed for each operating area for each scenario from the high-resolution and hourly production data.The regulation amount for load alone was assumed to be 1% of the hourly load (column 3). The standard deviation of the short-term wind generation forecast error was calculated using the appropriate equation and the hourly average wind production (column 4). The regulation for load net of wind generation was then computed by statistically combining the load regulation (assuming that it represents three times the standard deviation of load) with the standard deviation of the short-term wind generation forecast error (column 5).The spinning portion of the contingency reserve (column 6) is constant for each was computed using the appropriate equation and the previous hour's wind generation. The total spinning reserve requirement for the hour (column 8), then, is the sum of total regulation (column 5), the spinning portion of the contingency reserve (column 6), and the additional regulating reserve that was set aside during the previous hour to cover expected reductions in wind generation (column 7).

149Notes: The equation for column 3 is from Figure 5-9 and uses current hour wind generation from column 2: The column 5 value is computed from column 3 and column 4 using:The equation for column 7 is from Figure 5-10 and uses wind generation from the previous hour:

to be used if wind generation is less than was forecast in the previous hour. this capacity in the production simulation. This is accomplished by reducing the hourly spinning reserve constraint by the amount of the reduction in wind generation from the previous hour, up to the amount that was held. This TABLE 56. EXAMPLE APPLICATION OF RESERVE METHODOLOGY TO HOURLY DATACOLUMN NUMBER 1 2 3 4 5 6 7 8HOURACTUAL LOAD ~MWACTUAL WIND ~MWREGULATION FOR LOAD ~MWST ~MWTOTAL REGULATION ~MWCONTINGENCYRESERVESPINNING ~MWNext Hour Error ~MWTOTAL SPINNING RESERVE ~MW1 56,341 11,860 563 280 1,011 2,271 1,037 4,319 2 54,788 15,174 548 187 785 2,271 996 3,056 3 53,993 14,261 540 219 851 2,271 729 3,122 4 53,786 11,926 538 279 994 2,271 820 3,265 5 54,922 10,843 549 295 1,042 2,271 993 3,313 6 58,120 10,283 581 301 1,073 2,271 1,043 3,344 7 63,929 9,193 639 306 1,120 2,271 1,062 3,391 8 69,969 7,942 700 304 1,150 2,271 1,083 3,421 9 72,432 8,077 724 305 1,167 2,271 1,085 3,438 10 72,992 8,726 730 307 1,174 2,271 1,086 3,445 11 73,475 9,736 735 304 1,172 2,271 1,087 3,433 12 73,502 9,838 735 304 1,171 2,271 1,075 3,442 13 73,316 9,201 733 306 1,176 2,271 1,073 3,447 14 72,894 8,801 729 307 1,174 2,271 1,083 3,445 15 72,704 10,146 727 302 1,161 2,271 1,087 3,432 16 72,201 12,733 722 262 1,067 2,271 1,065 3,338 17 73,160 13,937 732 230 1,005 2,271 943 3,276 150being carried to cover hourly wind generation forecast error (column 3). Wind generation declined by 913 MW from the previous hour (column 5). All of the 729 MW was deployed to cover this drop, so the total spinning reserve constraint for that hour in the production simulation is reduced by that amount, from 3,122 MW to 2,392 MW. As can be seen in hour 2, if wind generation increases from the previous hour, there is no adjustment. regulation versus nonspinning (quick-start) generation was desired, the amount to one standard deviation of the next-hour persistence forecast error was held; increasing the amount to two standard deviations, which would be adequate to cover about 90% of the reductions in next-hour wind generation output, would more spinning reserve that is not actually dispatched to cover forecast errors, thus increasing the cost. The "cost" of releasing the spinning reserves is tabulated by the production simulation program; generation capacity that would have otherwise been unloaded is dispatched to cover the loss in wind, and associated production costs are accumulated.

151production simulation program as operating area requirements, which constrain the algorithms for optimization and economic dispatch. REGULATING RESERVE RESULTS FOR STUDY SCENARIOSTables 5-8 through 5-12 document the statistics of the regulation portion of the spinning reserves for each operating region and wind generation scenario. These amounts include the additional amount of spinning operating reserve for Figure 5-11 shows a more detailed view of the PJM requirements, showing distributions of the regulating requirement for load only and load net wind for Scenario 3.

TABLE 57. ADJUSTMENT OF SPINNING RESERVE FOR REDUCTION IN WIND GENERATIONCOLUMN NUMBER 1 2 3 4 5 6 7HOURACTUAL LOAD ~MWACTUAL WIND ~MWWind_1Hour

~MWTOTAL SPINNING RESERVE ~MWCHANGE IN WIND GENERATION ~MWADJUSTMENT ~MWADJUSTED SPINNING RESERVE ~MW1 56,341 11,860 1,037 4,319 50 0 4,319 2 54,788 15,174 996 3,056 3,314 0 3,056 3 53,993 14,261 729 3,122 729 2,393 4 53,786 11,926 820 3,265 820 2,445 5 54,922 10,843 993 3,3131,084993 2,320 6 58,120 10,283 1,043 3,344559559 2,785 7 63,929 9,193 1,062 3,3911,0901,062 2,329 8 69,969 7,942 1,083 3,4211,2521,083 2,337 9 72,432 8,077 1,085 3,438 135 0 3,438 10 72,992 8,726 1,086 3,445 649 0 3,445 11 73,475 9,736 1,087 3,443 1,010 0 3,443 12 73,502 9,838 1,075 3,442 101 0 3,442 13 73,316 9,201 1,073 3,447636636 2,810 14 72,894 8,801 1,083 3,445400400 3,045 15 72,704 10,146 1,087 3,432 1,346 0 3,432 16 72,201 12,733 1,065 3,338 2,586 0 3,338 17 73,160 13,937 943 3,276 1,204 0 3,276 152Figure 5-11. Distributions of hourly regulating reserve requirements for PJM Scenario 3, for load only (ideal wind generation) and load net of wind generationTABLE 58. SPINNING RESERVE REQUIREMENTS FOR THE REFERENCE CASEREGIONLOAD ONLYLOAD AND WINDCONTINGENCYSPINNING ~MWMAXIMUM~MWAVERAGE~MWMAXIMUM ~MWAVERAGE~MWMISO 1,480 924 2,235 1,635 2,271ISONE 399 202 1,040 762 579NYISO 390 219 738 531 600PJM 1,741 1,060 2,545 1,817 3,350SERC 1,343 744 1,549 886 570SPP 870 514 1,135 800 770TVA 721 365 769 419 403TABLE 59. SPINNING RESERVE REQUIREMENTS FOR SCENARIO 1REGIONLOAD ONLYLOAD AND WINDCONTINGENCYSPINNING ~MWMAXIMUM~MWAVERAGE~MWMAXIMUM ~MWAVERAGE~MWMISO 1,480 924 6,806 5,460 2,271ISONE 399 202 600 395 579NYISO 390 219 921 623 600PJM 1,741 1,060 2,966 2,006 3,350SERC 1,343 744 1,348 753 570SPP 870 514 8,154 6,245 770TVA 721 365 769 419 403 153TABLE 510. SPINNING RESERVE REQUIREMENTS FOR SCENARIO 2REGIONLOAD ONLYLOAD AND WINDCONTINGENCYSPINNING ~MWMAXIMUM~MWAVERAGE~MWMAXIMUM ~MWAVERAGE~MWMISO 1,480 924 5,131 4,094 2,271ISONE 399 202 1,392 1,041 579NYISO 390 219 1,565 1,124 600PJM 1,741 1,060 3,247 2,377 3,350SERC 1,343 744 1,665 954 570SPP 870 514 8,179 6,110 770TVA 721 365 769 419 403TABLE 511. SPINNING RESERVE REQUIREMENTS FOR SCENARIO 3REGIONLOAD ONLYLOAD AND WINDCONTINGENCYRESERVESPINNING ~MWMAXIMUM~MWAVERAGE~MWMAXIMUM ~MWAVERAGE~MWMISO 1,480 924 3,759 2,934 2,271ISONE 399 202 2,401 1,780 579NYISO 390 219 2,161 1,616 600PJM 1,741 1,060 5,690 4,467 3,350SERC 1,343 744 1,665 954 570SPP 870 514 4,837 3,658 770TVA 721 365 769 419 403TABLE 512. SPINNING RESERVE REQUIREMENTS FOR SCENARIO 4REGIONLOAD ONLYLOAD AND WINDCONTINGENCYRESERVESPINNING ~MWMAXIMUM~MWAVERAGE~MWMAXIMUM ~MWAVERAGE~MWMISO 1,480 924 6,832 5,478 2,271ISONE 399 202 2,401 1,780 579NYISO 390 219 2,161 1,616 600PJM 1,741 1,060 7,006 5,413 3,350SERC 1,343 744 1,665 954 570SPP 870 514 8,412 6,423 770TVA 721 365 769 419 403

SUMMARY

As mentioned previously, the algorithms in the production simulation program Generation must be committed and dispatched to meet load at minimum costs while honoring all constraints, including the hourly spinning reserve requirement. The reserve constraints have an impact only when they are binding on either 154regulating reserve requirements appear to be very large (e.g., SPP). Note that the generation mix does not change except for the introduction of wind, heavy penetration of wind generation frees up nonwind generation to supply the required regulating reserves that support frequency and balance generation with market structures, however, could affect the availability of these services in the market, as discussed further in this summary.to meet the spinning reserve requirement, the reserve constraint is binding and additional operating costs will be incurred. Even without a change in commitment, units might not be loaded to their maximums, and as a result, will regulation is a relatively expensive service compared to the provision of spinning operating reserve. and merit some additional discussion. First, although the philosophy behind the view of short-term forecast errors in wind generation as a contributor to needs for incremental regulation is sound, the persistence forecast technique short-term forecasts would reduce the impact on regulation requirements. The persistence assumption employed here has most likely led to conservative estimates of regulation requirements. Second, high penetrations of wind generation and the increased requirements for value. Moving up the supply curve for those services might reach into units that be another limitation. Alternatively, loads and storage are beginning to supply increase the supply of regulation by 2024. Third, if large amounts of wind energy displace conventional units and compensation in lieu of energy sales for those units and keeping them 155Finally, the importance of the assumptions about the structure for operations in this 2024 scenario must be reiterated. Functional subhourly markets are the most economic means to compensate for short-term changes in load and transmission take maximum advantage of diversity in both load and wind California, comprises smaller, less tightly interconnected balancing areas. Even modest penetrations of wind generation, much smaller than those considered additional requirements they bring for regulation and balancing. knowledge gained from operating experience around the country and the world as wind generation penetrations continue to grow will build an increasingly better foundation for technical insights into this important challenge.

156SECTION 6: ASSESSING IMPACTS ON POWER SYSTEM OPERATIONanalysts assessed wind generation impacts on the operation of the Eastern nodal model, in which all transmission is represented explicitly along with all committed and dispatched to serve load at each bus while honoring transmission constraints and recognizing the security needs of the system.primary inputs to the process. The power system model is built on the data and assumptions described in Section 3. Transmission overlays and new conventional four high-penetration wind generation scenarios.

The intent of these simulations is to mimic as closely as possible the assumed uncertainty introduced by wind generation in each scenario. WIND INTEGRATION IMPACTS AND COSTwind integration costs include those incremental costs incurred in the operational time frames that can be attributed to the variability and uncertainty introduced policies and practices are mapped as closely as possible to the production simulations. This is accomplished by mimicking the established (or desired) practices for unit commitment, transaction scheduling, and the maintenance of adequate reserves for system control and security. The increased uncertainty of wind generation is considered in the unit-commitment step, where forecasts of both load and wind generation are the basis for optimizing generating unit deployment. The economic-dispatch step of the 157require that additional operating reserves be carried. The basic process for assessing the impacts of wind generation on power system operations involves running a production simulation that uses forecasts of load and wind generation in the unit-commitment step and honors operating reserve constraints in the economic-dispatch step that are adequate for managing the increased variability. The production costs incurred over the simulation, then, Extracting or isolating that increased cost requires an additional step. With marginal units are displaced by what is usually considered to be a "must take" resource. Because of this displacement, the costs related to uncertainty in the optimization process and the requirements for carrying additional reserves will studies (see Bibliography) used the concept of a "proxy resource" to represent the energy provided by wind generation, but in a way that affects scheduling and real-time operations as little as possible (i.e., it neither helps nor hurts the scheduling and dispatch of other conventional resources and is therefore close to operational cost-neutrality. One conceptual energy resource that meets this used this type of proxy resource. The amounts of wind in several of the scenarios A cursory examination of the wind generation data for the 20% penetration introduces additional uncertainty into the commitment process and requires additional reserves in the economic-dispatch steps to the case with the proxy resource, where only load carries uncertainty and exhibits variability. Although previous studies focused on the costs of integrating wind generation, it must be noted that those costs are only one piece of the larger set of wind ANALYTICAL APPROACHThe general analytical approach for assessing operational impacts attributable to wind generation is quite straightforward, and was the basic method used in 158many previous integration studies. The size and extent of the model for this study, some new challenges. some issues related to using an extremely large production model to assess wind of the Reference Scenario:1. The effect of various approaches for calculating reserve requirements with wind generation variability and short-term uncertainty2. The approach for extracting the incremental production costs caused by wind generation variability and uncertainty (integration costs)The costs of carrying additional spinning reserves were also explored through several iterations of the Reference Scenario. The results revealed a strong correlation between cost and the amount of spinning reserve. Consequently, the study analysts carefully evaluated the calculation of the spinning reserve requirements, and the approach described in Section 5 was the result.Calculating the cost of wind generation variability and uncertainty involves running at least two annual production simulations for each scenario: 1. An ideal wind case, where the energy initially delivered on a daily basis was forecast error and no requirement for incremental reserves. Load is uncertain in the day-ahead commitment and requires a baseline of operating reserves.2. An actual wind case, where wind is delivered in the hourly shapes from the National Renewable Energy Laboratory's (NREL) mesoscale database, is uncertain in the day-ahead commitment (per the day-ahead forecasts in the data) and requires additional spinning reserves for regulation and load following. The difference between production costs for this case and the ideal wind case is the total integration cost. To estimate the effects of either the day-ahead forecast error or incremental regulating reserves individually, two additional cases can be run:1. A case where wind is known perfectly 1 day ahead, but more spinning reserve is carried because of the variability and short-term uncertainty of wind generation. Comparing this case to the actual wind case produces an estimate of the cost of the wind generation forecast error. 2. A case where wind imposes no additional burden in real time (i.e., additional spinning reserves), but the day-ahead forecast for unit 159commitment is imperfect. The uncertainty costs can then be computed as the difference between production costs for this case and for the actual wind case. Applying this approach to the Reference Scenario produced some results that were initially in contrast to conclusions from previous integration studies. After some generation had meaning only across the entire model; integration cost calculations associated with valuation of the hourly energy exchanges with other operating areas. transactions between operating areas are determined by the program algorithms and made on an economic basis (i.e., if surplus energy in one operating area is less expensive than native generation in that area, and transmission capacity is available, the energy will be sold). Consequently, incremental variability from wind generation can be exchanged with other areas if the appropriate economic signals are present. For this reason, the effects of wind generation variability and uncertainty in this operating areas. RESULTSHIGHPENETRATION WIND SCENARIOS 14 The primary inputs for evaluating the operational impacts of wind generation variability and uncertainty are simulated wind generation data sets synchronized with historical load over an extended time series. The study team used NREL mesoscale wind data for 2004 through 2006 for this analysis. This section gives this section.SCENARIO CHARACTERISTICSAfter considering various locations of wind resources and different wind penetration levels, four wind scenarios were developed: three 20% wind energy scenarios and one 30% wind energy scenario. Figure 6-1 summarizes the wind penetration levels by region and scenario. Among the three 20% wind scenarios, Scenario 1 has the highest penetration levels in the western regions because it uses the most high-quality wind resources in the Great Plains. Because wind is moved eastward and more offshore wind is used in Scenarios 2 and 3, the penetration 160across the footprint and offshore wind along the East Coast with the highest wind penetration levels in almost all the regions. Based on wind quality and Reliability Council (SERC) have very little installed wind capacity and are the primary wind import regions. Conversely, the Southwest Power Pool (SPP) has very high wind penetration levels for all four scenarios. Because of the unique characteristics of the wind resource, additional reserve requirements are required to regulate the wind and maintain system reliability. with the amount of wind generation at that particular hour. Figure 6-2 shows the shows, the level of required operating reserves increases with wind penetration levels, as expected.

Figure 6-1. Wind energy penetration levels by region using 2004 hourly proles 161 Figure 6-2. Annual average variable spinning reserve using 2004 hourly prolesOPERATIONAL IMPACTTo calculate the costs of operational impact associated with wind variability the actual wind case. Given the assumption that the day-ahead wind forecast is perfect, no wind forecast error and incremental variable reserves for wind were modeled in the ideal wind case. Considering wind uncertainty and variability, the actual wind case modeled both the day-ahead wind forecast error and additional variable reserves driven by wind. Load uncertainty was accounted for in both cases by including hourly load forecast error and that portion of the additional reserves that resulted from the load uncertainty. The cost difference between the actual and ideal wind cases is the total integration cost. To separate the individual operational effects of the day-ahead wind forecast error and the variable reserve requirement, an intermediate case ignored the incremental reserve requirements driven by wind. Comparing this case to the ideal wind case gives the day-ahead wind forecast error cost; comparing this case to the actual wind case gives the cost of carrying the incremental reserves associated with wind. Adjusted production cost (APC) was used to calculate the integration cost with regional interchanges and associated costs captured as described in Section 4.2.

162Figure 6-3 shows APCs of the ideal, intermediate, and actual cases for each scenario. With the increased 30% wind energy penetration offsetting base-load steam generation, Scenario 4 has the lowest APCs of the four wind scenarios.

Figure 6-3. Annual APCs using 2004 hourly prolesTable 6-1 summarizes the integration costs for each scenario in US$2024 per megawatt-hour (MWh) of wind energy. Carrying additional reserves has a much larger effect on total integration costs than the day-ahead wind forecast error, which could be caused by the resulting total forecast error reduction of aggregating many individual wind plants over a very large geographical area.TABLE 61. INTEGRATION COSTS ~$/MWh of wind energy in US$2024)SCENARIODAYAHEAD FORECAST ERROR ~

$/MWh)VARIABLE RESERVE

~$/MWh)TOTAL INTEGRATION COST~$/MWh)1 2.26 5.74 8.00 2 2.61 4.59 7.21 3 2.84 2.93 5.77 4 2.51 4.56 7.07Table 6-2 lists the integration costs for each scenario from different perspectives, in dollars per megawatt-hour ($/MWh) normalized over total wind energy ($/MWh), as a percentage of total APCs, and in dollars normalized over the total load amount ($/MWh). With 20% to 30% wind energy penetration levels for the 163wind variability and uncertainty range from $5.77/MWh to $8.00/MWh of wind energy (in US$2024).TABLE 62. INTEGRATION COSTS US$2024SCENARIO 1 2 3 4INTEGRATION COST ~$5,290,351,725 4,795,114,783 3,988,497,258 6,915,311,563APC s ($)104,125,330,202 100,302,223,283 99,350,363,256 98,493,233,640INTEGRATION COST ~$/MW h of WIND)8.00 7.21 5.77 7.07INTEGRATION COST ~% of APC5.08 4.78 4.01 7.02INTEGRATION COST ~$/MW h of LOAD)1.52 1.37 1.14 1.98Figures 6-4 and 6-5 show the detailed annual generation production for the ideal, intermediate, and actual cases by fuel type and scenario. With day-ahead wind forecast error modeled in the intermediate case, the base-load coal units are displaced to some degree. And as a result of carrying additional reserves to accommodate wind variability and uncertainty in the actual wind case, the coal cycle (CC) and combustion turbine (CT) units.

Figure 6-4. Annual steam turbine coal generation summary with 2004 hourly proles 164 Figure 6-5. Annual combined cycle and combustion turbine gas generation with 2004 hourly prolesSCENARIO ANALYSISproduced a substantial amount of information on what could be expected in terms of operational impacts and the associated costs of wind variability and uncertainty. Study analysts completed the production-cost simulations with 1652005 and 2006 wind and load patterns as a follow-up. The 3-year results for all scenarios, summarized here, offer more detailed analysis on integration cost, wind curtailment, generation production by fuel type, locational marginal prices (LMPs), and regional interchanges. All costs in this section are in US$2024.Wind penetration levels, geographical locations of wind, and additional variable reserve amounts for wind are a few of the key elements driving the total APC and integration cost for each scenario. As Figure 6-6 illustrates, Scenario 4 has the lowest APC because the least amount of conventional generation resources are committed to accommodate the aggressive 30% wind penetration. Among the three 20% wind scenarios, Scenario 1 has the highest APC, with wind resources concentrated in the western regions and the largest variable reserve amount carried because of wind on the whole study footprint. The Reference Scenario, with the least amount of wind energy and thus the most amount of conventional generation, has the highest APCs.

Figure 6-6. Annual APC comparison for actual cases INTEGRATION COSTSFigures 6-7 and 6-8 summarize 3-year wind integration costs by scenario in millions of dollars and dollars per megawatt-hour of wind, respectively. The 166 As expected, the total integration cost of Scenario 4 is the highest among all the scenarios because it has the highest wind penetration level. The integration costs are reduced as wind moves toward load centers from Scenario 1 to 3. By normalizing over wind generation for each scenario, the integration cost for Scenario 1 is the highest, up to $8/MWh of wind energy. For Scenario 3, a low of approximately $5/MWh integration cost is obtained. These costs show very good consistency between the study results with 3-year wind and load patterns. As with production costs, the Reference Scenario integration costs are much lower because of the lower wind penetrations and associated reserves and forecast errors.

Figure 6-7. Wind integration costs (US$2024, millions)

167Figure 6-8. Wind integration costs ($/MWh of annual wind energy in 2024)WIND ENERGY AND CURTAILMENTpattern corresponding to the historical hourly load shape of that year. Using realistic wind patterns is critical to ensure that an assessment of the operational impacts of wind variability and uncertainty on the system is credible. To account real-time dispatch wind, forecast errors are included to adjust the amount of wind energy to be essentially the actual wind data. Figure 6-9 shows the annual energy inputs for forecast wind and actual wind using 2004, 2005, and 2006 wind pattern compared to the other 2 years and is applied in some of the further sensitivity analyses in Section 6.5.

168Figure 6-9. Annual wind energy input summary for Scenario 1Figures 6-10 through 6-12 summarize the wind curtailment levels by region and scenario with 2004, 2005, and 2006 wind and load patterns, respectively. Roughly 2% to 10% wind curtailment occurs across the study footprint. SPP has the highest wind curtailment levels in all scenarios except Scenario 3. The lowest wind curtailment level occurs in Scenario 3, with wind spreading more evenly over the footprint. With their intertwined nature, transmission constraints and minimum generation events are clearly the main drivers for wind curtailment. To further investigate wind curtailment, the study team conducted detailed sensitivity analyses, and the results are discussed in Section 6.5.1.

169Figure 6-10. Annual wind curtailment summary using 2004 hourly load and wind proles Figure 6-11. Annual wind curtailment summary using 2005 hourly load and wind proles 170 Figure 6-12. Annual wind curtailment summary using 2006 hourly load and wind prolesGENERATION BY FUEL TYPE The operating costs associated with wind variability and uncertainty depend on the nature of the generation mix developed for each scenario. For example, fuel prices, carbon emission regulations, variable operations and maintenance (O&M) costs, and renewable energy mandates all feed into operating costs. Examining the generation dispatch pattern can offer valuable insights into understanding the effect of wind on system operations. illustrated in Figures 6-13 through 6-15, and with the same information, Figures 6-16 through 6-18 show a different way to look at the dispatchable generation cycle (CC) units are dispatched in Scenario 1 to manage the largest variable Scenario 4, the increased off-peak energy contribution of the 30% wind mandate results in an approximately 16% reduction of steam turbine (ST) coal generation compared to the three 20% wind scenarios, whereas a fairly comparable level of penetration), wind becomes the second largest energy producer, behind only 171Note: IGCC = integrated gas combined cycle Figure 6-13. Annual generation energy by fuel type using 2004 hourly load and wind proles Figure 6-14. Annual generation energy by fuel type using 2005 hourly load and wind proles

172 Figure 6-15. Annual generation energy by fuel type using 2006 hourly load and wind proles Figure 6-16. Annual generation energy by fuel type using 2004 hourly proles

173Figure 6-17. Annual generation energy by fuel type using 2005 hourly proles Figure 6-18. Annual generation energy by fuel type using 2006 hourly proles 174Figures 6-19 through 6-21 summarize the annual generation changes between the ideal and actual wind cases by fuel type and scenario for the 3-year wind and load patterns. As described in Section 6.2, the day-ahead wind forecast error and additional reserve carried because of wind are used to differentiate the actual changes between these two cases. All the scenarios follow the same trend, with combustion turbines. To deal with the highest wind penetration and associated observed in Scenario 4. The generation shift in Scenario 3 is the most modest of the high-penetration wind scenarios because it has the least additional reserve requirement (as a result of moving wind close to load centers). Consistent with low wind penetration and integration costs, the Reference Scenario shows the least generation shift from the ideal to actual wind cases.

Figure 6-19. Change in annual generation from ideal to actual cases using 2004 hourly proles

175Figure 6-20. Change in annual generation from ideal to actual cases using 2005 hourly prolesFigure 6-21. Change in annual generation from ideal to actual cases using 2006 hourly proles 176LOCATIONAL MARGINAL PRICESFigures 6-22 through 6-24 illustrate the effect of wind on annual generation-weighted LMPs by region and scenario for the 3-year wind and load patterns. The LMP is the marginal cost of serving the next megawatt of demand and depends on the system transmission constraints and the performance characteristics of generation resources. Because there is less congestion with wind moving toward load centers, it is intuitive to expect that the regional generation-weighted LMPs decrease from Scenario 1 to 3.

Figure 6-22. Annual generation-weighted LMPs using 2004 hourly proles

177Figure 6-23. Annual generation-weighted LMPs using 2005 hourly proles Figure 6-24. Annual generation-weighted LMPs using 2006 hourly proles 178Scenario 4 elevates the effect on energy market prices because it has the most aggressive wind penetration level. Regionally, the results are as follows:Even though both Scenarios 3 and 4 use a substantial amount of offshore wind along the East Coast with approximately the same installed wind Scenario 4 because more wind resources are accessible in the western regions (MAPP), however, the LMPs in Scenario 4 are actually higher than those in wind capacity and the resulting variable reserve requirement driven by wind between these two scenarios. -mitted or available to accommodate the larger amount of imported vari

-ability and uncertainty in Scenario 4. As a result, Scenario 4 has higher LMPs than Scenario 3. REGIONAL TRANSACTIONSThe conceptual transmission overlay enables wind and base-load steam energy in the western regions to reach a wider footprint and results in a different unit commitment and dispatch across the entire study footprint. The associated regional transaction costs have a great impact on the APC calculation for each region. Figures 6-25 through 6-27 show the annual transaction energy by region Figure 6-25. Annual regional transaction energy using 2004 hourly proles

179Figure 6-26. Annual regional transaction energy using 2005 hourly proles Figure 6-27. Annual regional transaction energy using 2006 hourly proles 180The effects of wind generation on regional transactions can be summarized as follows:predominant import region because of low wind availability in all the scenarios. The total transaction amount decreases from Scenario 1 to 3 as wind resources move toward the East Coast. Scenario 3 has the least regional transaction energy among the high-penetration wind scenarios.As a result of using aggressive amounts of offshore wind capacity in eastern regions, Scenario 4 has smaller amounts of transactions on the total study footprint compared to Scenario 1. The import amounts are roughly the same in SERC for the three 20% wind penetration scenarios, and there is an approximate 40% increase in Scenario 4 because the eastern regions need to import less.toward load centers. This results in a large increase of installed wind capacity in the eastern regions for export. With a different transmission overlay, different thermal expansion plan, and lower wind penetration with different siting, the Reference Scenario shows much lower total energy interchange and some different net positions for regions. SENSITIVITY ANALYSISWIND CURTAILMENTContinuing the study effort described in Section 4.4.2, more detailed sensitivity analyses were performed to further investigate wind curtailment in the high-penetration wind scenarios. Wind curtailment ranges from approximately 3% to the study team conducted three sensitivity analyses:Sensitivity Case 1, Non Must-Run: The must-run constraint is removed from coal units (i.e., the program is allowed to actually shut them down). Sensitivity Case 2, Copper Sheet: There are no transmission constraints in the system.Sensitivity Case 3, Wind Energy Credit: The wind curtailment price is set at negative $40/MWh, twice as much as the current production tax credit (PTC).interface. As described in Section 4, the transmission line across the interface preliminary economic transmission requirement. For very short periods of time, some wind energy curtailment would be expected for the four high-penetration wind scenarios.

181 Figure 6-28. Interface ow duration curve sampleTables 6-3 through 6-5 summarize the annual wind curtailment results by region status from coal units has the least effect on wind curtailment, with only 0.27% curtailment reduction compared to the original actual wind case. With a wind curtailment price at negative $40/MWh, approximately 3.51% wind curtailment is achieved for the whole study footprint as opposed to the original 6.38%. The majority of wind curtailment is caused by transmission constraints because system. Only 0.12% wind curtailment is left, which is most likely caused by the minimum generation events.

182TABLE 63. WIND CURTAILMENT COMPARISON FOR SENSITIVITY CASE 1, NON MUSTRUNREGIONSENSITIVITY NON MUSTRUNORIGINAL ACTUAL CASEWIND INPUT DATATOTALCURTAILMENT ~%TOTALCURTAILMENT ~%TOTALE_CAN 648,088 0.03 648,088 0.03 648,283ISONE 10,514,801 0.03 10,514,801 0.03 10,517,477MAPP 173,422,697 4.33 172,292,204 4.95 181,271,613MHEB 2,598,952 1.01 2,600,815 0.93 2,625,348MISO 129,742,596 2.90 129,844,931 2.83 133,622,952NYISO 21,326,536 3.72 21,216,681 4.22 22,151,455PJM 70,241,841 2.52 70,206,710 2.56 72,054,440SERCNI 3,171,641 0.03 3,171,218 0.04 3,172,523SPP 251,687,576 10.28 251,004,870 10.52 280,512,355TVASUB 3,369,879 4.86 3,297,972 6.89 3,542,176TOTAL 666,724,607 6.11 664,798,290 6.38 710,118,621Notes: SERCNI, E-CAN, and TVASUB are monikers used in EWITS for subregions in the PROMOD IV model. MHEB = Manitoba Hydro Electric Board.TABLE 64. WIND CURTAILMENT COMPARISON FOR SENSITIVITY CASE 2, COPPER SHEETREGIONSENSITIVITY COPPER SHEETORIGINAL ACTUAL CASEWIND INPUT DATATOTALCURTAILMENT ~%TOTALCURTAILMENT ~%TOTALE_CAN648,0240.04648,0880.03648,283 ISONE10,514,8010.0310,514,8010.0310,517,477MAPPCOR180,775,9200.27172,292,2044.95181,271,613 MHEB2,625,2000.012,600,8150.932,625,348 MISO133,617,0700.00129,844,9312.83133,622,952NYISO22,147,6450.0221,216,6814.2222,151,455PJM72,047,6860.0170,206,7102.5672,054,440SERCNI3,172,0460.023,171,2180.043,172,523 SPP280,148,1340.13251,004,87010.52280,512,355TVASUB3,541,9860.013,297,9726.893,542,176TOTAL709,238,5120.12664,798,2906.38710,118,621Note: MAPPCOR is the service provider to MAPP.

183TABLE 65. WIND CURTAILMENT COMPARISON FOR SENSITIVITY CASE 3, WIND ENERGY CREDITREGIONSENSITIVITY NON MUSTRUNORIGINAL ACTUAL CASEWIND INPUT DATATOTALCURTAILMENT

~%TOTALCURTAILMENT

~%TOTALE_CAN 648,088 0.03 648,088 0.03 648,283ISONE 10,514,801 0.03 10,514,801 0.03 10,517,477MAPPCOR 179,024,767 1.24 172,292,204 4.95 181,271,613MHEB 2,625,069 0.01 2,600,815 0.93 2,625,348MISO 130,820,220 2.10 129,844,931 2.83 133,622,952NYISO 22,014,540 0.62 21,216,681 4.22 22,151,455PJM 71,400,322 0.91 70,206,710 2.56 72,054,440SERCNI 3,171,925 0.02 3,171,218 0.04 3,172,523SPP 261,480,631 6.78 251,004,870 10.52 280,512,355TVASUB 3,463,917 2.21 3,297,972 6.89 3,542,176TOTAL 685,164,280 3.51 664,798,290 6.38 710,118,621 As illustrated in Table 6-6, by setting coal units as non must-run in Sensitivity an average of $1.7/million British thermal units (MBtu) in the SPP, an average of $2.91/MBtu in the SERC, and up to $3.50/MBtu on the East Coast. Because of this, without enforcing the must-run constraints, coal and combined cycle resources in the high-cost regions are decommitted by importing the available low-cost, off-peak energy from the western regions. Because there are no penalties or additional costs associated with carbon emissions, energy from fossil resources in the Midwest can be exported when wind is not available. Adding restrictions or additional costs on carbon emissions would decrease the amount With the negative $40/MWh wind curtailment price, increased wind energy is forced into the production-cost model and results in substantial amounts of dump energy from conventional generation resources. Dump energy represents the unavoidable surplus minimum segment generation that cannot be used to serve the load because of either unit operating constraints or transmission used only for the purpose of this report. Because the wind can be curtailed only TABLE 66. ANNUAL GENERATION ENERGY

SUMMARY

BY FUEL TYPE ~MW h)CASECCCT, GASCT, OILIGCCST, COALST, GASST, OILDUMP ENERGYORIGINAL 150,745,894 45,260,490 125,404 16,311,970 1,699,861,537 11,635,944 201,453 2,663,506CASE 1 135,636,423 42,194,976 132,876 16,360,553 1,711,360,316 15,275,894 517,574 2,236,014CASE 2 143,133,853 38,051,768 75,377 16,372,149 1,673,395,676 1,808,557 126,010 39,851CASE 3 150,896,689 45,658,954 125,974 16,189,417 1,698,071,417 11,820,054 197,049 13,660,546 184when the bus LMPs go below negative $40/MWh, before the price can reach that point, dumping energy from conventional generation is used to produce counter congestion and increase bus LMPs to greater than zero.BIDUP COMMITMENT LOGICA multistep security constrained unit-commitment and dispatch process is used in the commitment step (ignoring unit operating constraints and start-up costs), the bid-up commitment step with all unit constraints and commitment bid adders applied, and Additional production-cost simulations were run to determine the integration costs Figures 6-29 through 6-31 illustrate the effect of bid-up commitment logic on APC, integration costs, and regional LMPs. Enforcing unit minimum run- and downtimes, response units to be committed to meet all the unit operating constraints, and results in higher APCs and LMPs relative to the original cases with this logic bypassed. The overall effect on the integration costs is minimal for the high-penetration wind scenarios, with an increased cost range from a low of $0.03/MWh of wind energy in Scenario 3 to $0.52/MWh of wind energy in Scenario 1.

Figure 6-29. APCs with 2005 hourly wind and load patterns 185 Figure 6-30. Integration cost summary with 2005 hourly wind and load patterns Figure 6-31. Annual generation-weighted LMPs with 2005 hourly wind and load patterns 186HURDLE RATESHurdle rates are used in the production-cost model to allow regional transactions during the security-constrained unit commitment (SCUC) and dispatch process. The dispatch hurdle rates are the economic adders between applicable price regional transmission organization (RTO), there are no hurdle rates; the hurdle rates are between RTOs. The commitment hurdle rate is a mechanism to commit pool generation for pool load and then, based on price differentials, to commit additional units to serve load outside the pool. The project team performed sensitivity analyses on Scenarios 1 and 3 to evaluate the effect of the system-wide integrated energy market on the operational impact of wind variability and uncertainty. The regional reserve requirements remain the same as in the original cases and the hurdle rates between regions are set at zero.Table 6-7, Table 6-8, and Figure 6-32 demonstrate how the hurdle rates affect the integration costs for Scenarios 1 and 3. The results show that allowing more economic energy interchanges under the integrated energy markets across the study footprint results in less wind curtailment, lower APCs, and regional LMPs. And there is a very modest reduction in the integration costs because of the zero hurdle rates.TABLE 67. SCENARIO 1, HURDLE RATE SENSITIVITY RESULTSSCENARIO 1ORIGINALZERO HURDLE RATE SENSITIVITYDIFFERENCEANNUAL WIND ENERGY ~MW h664,798,102 665,611,102 812,812WIND CURTAILMENT ~%

6.38 6.270.11APC s ($)104,125,330,202 102,203,930,9391,921,399,263INTEGRATION COST ~$

5,290,351,725 4,879,216,5817.77INTEGRATION COST ~$/MW h of wind8.00 7.370.63INTEGRATION COST ~% OF APC 5.08 4.770.31TABLE 68. SCENARIO 3, HURDLE RATE SENSITIVITY RESULTSSCENARIO 3ORIGINALZERO HURDLE RATE SENSITIVITYDIFFERENCEANNUAL WIND ENERGY ~MW h696,093,674 698,339,429 2,245,755WIND CURTAILMENT ~%

3.26 2.950.31APC s ($)99,350,363,256 98,712,905,090637,458,167INTEGRATION COST ~$

3,988,497,258 3,993,564,039 0.13INTEGRATION COST ~$/MW h of wind5.77 5.760.01INTEGRATION COST ~% OF APC 4.01 4.05 0.03 187Figure 6-32. Annual generation-weighted LMP comparison CARBON SENSITIVITYEGEAS REGIONAL RESOURCE FORECASTING EXPANSIONSystem (EGEAS) economic model the same except for the price to produce a ton of carbon. This sensitivity analysis places a cost on carbon of $100 per ton of CO 2 Figure 6-33 shows the nameplate capacity expansion comparison when the carbon sensitivity is applied to Scenario 2. The primary difference in the new capacity to nuclear power because of the penalty applied to the production of run requirements greater than the coal capacity, however, off-peak minimum generation events became a problem and limitations had to be set on the amount of base-load nuclear capacity that could be placed within the model.in the model. The retired existing coal capacity would be replaced with new more moderate carbon-producing combined cycle facilities.

188Notes: IGCC/Seq = IGCC with sequestration; DR = demand response; RET Coal = coal plant retirements; Replacement CC = replacement combined cycleFigure 6-33. Capacity expansion by scenario including carbon sensitivity, 2008-2024Energy growth will inherently increase carbon production on the system if the new energy demand is met primarily with carbon-producing resources. Within the modeling performed, however, increasing wind energy penetration through 3) would reduce actual annual carbon production compared to 2008 2024 (Scenario 4) reduces annual modeled carbon production nearly 19% from the 2008 production. Finally, adding the $100/ton cost to carbon for Scenario 2 189Figure 6-34. Carbon impact of modeled scenarios Figure 6-35. Cost impact of modeled scenarios 190CAPACITY SITING FOR SENSITIVITYAgain, the EGEAS model gives only a type and a timing result of what capacity would be needed to meet resource adequacy requirements. Using the same wind locations as Scenario 2, the study team sited thermal units locally generation for the carbon sensitivity scenario, along with the locations of the wind generation facilities for Scenario 2.

Figure 6-36. Forecast generation locations for sensitivity to Scenario 2OPERATIONAL IMPACT ANALYSISThe potential effects of carbon cost on the system operational cost caused by wind variability and uncertainty were evaluated with the capacity expansion results. This sensitivity analysis used the same day-ahead wind forecasts and additional reserve requirements as in Scenario 2 with 2005 hourly wind and load patterns. Figure 6-37 shows the annual production of conventional generation resources by fuel type. With a carbon cost penalty in capacity expansion, the base-load lower coal generation than in Scenario 2. Coal, however, is still required to meet the majority of demand compared to other types of resources.Figure 6-38 shows the annual generation changes between the ideal and actual wind cases by fuel type and scenario with 2005 wind and load patterns.

5 As in 191 5 Note that in the ideal case, wind generation is known perfectly and does not add within-the-hour variability. In the actual case, day-ahead forecasts of wind generation will contain some error, and more regulating reserves must be carried to deal with increased variability. The combination of additional forecast error and additional variability will favor units that are more exible.Scenarios 1 to 4, the carbon sensitivity scenario follows the same trend of coal-in the carbon sensitivity. Because of this, the coal and combined cycle generation changes from the ideal to actual wind cases are much higher in carbon sensitivity, as seen in Figure 6-38. Figure 6-39 shows a major increase in the average generation-weighted LMPs caused by a $100/ton carbon cost. As summarized in Table 6-9, the carbon cost wind curtailment improvement and only a minimal integration cost reduction. Figure 6-37. Annual generation production by fuel type with 2005 hourly wind and load patterns

192Figure 6-38. Annual generation energy changes from ideal case to actual case Figure 6-39.

Annual generation-weighted LMP comparison 193TABLE 69. SCENARIO 2, CARBON SENSITIVITY OPERATIONAL IMPACT RESULTSSCENARIO 2ORIGINALCARBONSENSITIITYDIFFERENCEANNUAL WIND ENERGY ~MW h696,317,330 706,155,399 9,838,069WIND CURTAILMENT ~%

6.79 5.471.32APC s ($)101,359,089,490 127,228,010,909 25,868,921,419INTEGRATION COST ~$

4,249,967,969 4,652,597,813 402,629,844INTEGRATION COST ~$/MW h of wind6.13 6.70 0.57 194SECTION 7: WIND GENERATION CONTRIBUTIONS TO RESOURCE ADEQUACY AND PLANNING MARGINBACKGROUNDpossible future capacity value of wind generation based on projected penetration levels and potential wind location scenarios. The second is to isolate and quantify that the reliability-focused analysis described in this section is an independent piece of work done separately from the economic and operation effects analysis covered in the other report sections. ANALYTICAL APPROACHTo estimate a 2024 capacity value for wind, analysts used the 2004, 2005, and to calculate the effective load-carrying capability (ELCC) of wind at the future penetration level. This analysis was also conducted using the same four wind penetration scenarios examined in Section 2.The four scenarios are as follows:The team also performed three different transmission level sensitivity analyses in this study. The level of transmission being modeled varied from no ties between areas to the different transmission levels of each existing and conceptual overlay scenario. The three transmission sensitivities are as follows:and new ties.ELCC CALCULATION METHODOLOGYdependably and reliably contribute to serve load, considering the probabilistic 195nature of generation shortfalls and random forced outages that result in load not being served. The probabilistic measure of load not being served is known as loss of load probability (LOLP), and when this probability is summed over a time frame (e.g., 1 year), it is referred to as loss of load expectation (LOLE). The accepted industry standard for what has been considered a reliable system has been the "less than 1 day in 10 years" criterion for LOLE. This measure is often expressed as 0.1 d/yr (0.1 d/yr = 1 day per 10 years), because that is often the time period (1 year) over which the LOLE index is calculated. To measure the ELCC of a particular resource, the reliability effects of all the other sources must be isolated from the resource in question. This is accomplished by calculating the LOLE of two different cases: one with and one without the resource consequently have fewer days per year of expected loss of load (smaller LOLE).

Figure 7-1. ELCC example system with and without resourceELCC EXAMPLE SYSTEM WITH AND WITHOUT RESOURCEThe new resource in the ELCC example made the system 0.07 d/yr more reliable, but there is another way to express the reliability contribution of the new resource besides the change in LOLE. The other option requires establishing a common baseline reliability level and then adjusting the load in each case with and without the new resource to a common LOLE level (Figure 7-2). The common baseline is the industry-accepted reliability standard of the 1 day in 10 years (0.1 d/yr) LOLE criterion.Figure 7-2. ELCC example system at the same LOLE 196ELCC EXAMPLE SYSTEM AT THE SAME LOLEWith each case at the same reliability level, the only difference between them is the amount by which the load was adjusted in each case. This difference is the amount of ELCC expressed in load or megawatts (MW). Sometimes this number is divided by the nameplate rating of the new resource and then expressed as a percentage. The new resource in the ELCC example has an ELCC of 300 MW, or 30% of the resource nameplate.The same analytical approach used in this simple single-zone example was model in GE Energy's Multi-Area Reliability Simulation (GE-MARS) program, and in that model, a load-modifying resource is adjusted in each interconnected instead of adjusting the 8,760 hourly load values of each of the multiple zones modeling technique is implemented in the software program by means of the LOLE calculation and does not result in any difference from the indirect load adjustment method.LOLE MODEL INPUT ASSUMPTIONSThe source for all LOLE model input data was the same database and source format for use in the LOLE model. Because the GE-MARS LOLE model uses a transportation style of modeling, which consists of a system of interconnected and interface limits between these zones must be calculated. Analysts used this study; they are listed in Table 7-1 with their total nameplate amount of wind for each study scenario.

197TABLE 71. RELIABILITY ZONES FOR LOLE ANALYSIS WITH INSTALLED WIND GENERATION CAPACITY ~NAMEPLATE WIND IN MEGAWATTSZONESCENARIO 1SCENARIO 2SCENARIO 3SCENARIO 4MISO West 59,260 39,953 23,656 59,260MISO Central 12,193 11,380 11,380 12,193 MISO East 9,091 6,456 4,284 9,091 MAPP USA 13,809 11,655 6,935 14,047SPP North 48,243 40,394 24,961 50,326SPP Central 44,055 46,272 25,997 44,705PJM 22,669 33,192 78,736 93,736TVA 1,247 1,247 1,247 1,247SERC 1,009 5,009 5,009 5,009NYISO 7,742 16,507 23,167 23,167ISO-NE 4,291 13,837 24,927 24,927Entergy 0 0 0 0 IESO 0 0 0 0MAPP Canada 0 0 0 0FULL STUDY SYSTEM 223,609 225,902 230,299 337,708Notes: Midwest ISO is shortened to MISO here because of space considerations. Other denitions follow: MAPP = Mid-Continent Area Power Pool; SPP = Southwest Power Pool; PJM = PJM Interconnection; TVA = Tennessee Valley Authority; SERC = Southeastern Electric Reliability Council (Entergy is operated as part of SERC); NYISO = New York ISO; ISO-NE = New England ISO; IESO = Independent Electricity System Operator.

The last step in developing the LOLE model and the input parameters was to calculate the interface limits between the study zones. Because of its ability to realistically model unit operating characteristics and produce detailed hourly that dispatchable generators within the zone are given a penalty factor to induce the each zone's daily peak load (365 values). The logic behind using only those is also calculated over the same daily peak load hours. These values were then averaged into monthly interface numbers, which is what the LOLE model program uses. This calculation was performed for every zone and every scenario twice, once with only the existing transmission system and once with the new additional transmission system of the scenario overlays included. Figures 7-3 through 7-10 show the results of these calculations. The diagrams also illustrate the interconnectivity of the zones for each scenario and transmission sensitivity. Note that for simplicity, only the values for August are shown in the diagrams (August is the study system's peak load month).

198 Figure 7-3. Scenario 1, existing transmission system August interface limits (MW)

Figure 7-4. Scenario 2, existing transmission system August interface limits (MW) 199 Figure 7-5. Scenario 3, existing transmission system August interface limits (MW)

Figure 7-6. Scenario 4, existing transmission system August interface limits (MW) 200 Figure 7-7. Scenario 1, conceptual transmission overlay August interface limits (MW)

Figure 7-8. Scenario 2, conceptual transmission overlay August interface limits (MW) 201 Figure 7-9. Scenario 3, conceptual transmission overlay August interface limits (MW)

Figure 7-10. Scenario 4, conceptual transmission overlay August interface limits (MW) 202RESULTSISOLATED SYSTEMTable 7-2 shows the ELCC results summed over the entire Eastern basis" means that there is no transfer capability or ties between any of the zones and thus no ability to share the wind resource with the rest of the system. This transmission sensitivity limits the wind capacity to serving load only in the zone where the wind resource is actually located.TABLE 72. ELCC RESULTS FOR ISOLATED SYSTEM ~NO TIESRESULTSSCENARIO 1SCENARIO 2SCENARIO 3SCENARIO 4Nameplate Wind (MW) 223,609 225,902 230,299 337,7082004 Pr-ELCC (MW) 32,144 35,868 41,264 54,4082004 Pr-ELCC (%)

14.4 15.9 17.9 16.12005 Pr-ELCC (MW) 31,433 40,322 46,484 54,2182005 Pr-ELCC (%)

14.1 17.8 20.2 16.12006 Pr-ELCC (MW) 36,126 43,986 53,375 63,5862006 Pr-ELCC (%)

16.2 19.5 23.2 18.8EXISTING TRANSMISSIONTable 7-3 shows the ELCC results under the transmission sensitivity of using only the existing transmission system. This allows for transfer capability and ties between zones at levels of today's existing infrastructure. Figures 7-3 through 7-6 TABLE 73. ELCC RESULTS FOR EXISTING TRANSMISSION SYSTEMRESULTSSCENARIO 1SCENARIO 2SCENARIO 3SCENARIO 4Nameplate Wind (MW) 223,609 225,902 230,299 337,7082004 Pr-ELCC (MW) 35,708 42,468 52,286 68,9322004 Pr-ELCC (%)

16.0 18.8 22.7 20.42005 Pr-ELCC (MW) 45,216 54,764 60,765 69,6552005 Pr-ELCC (%)

20.2 24.2 26.4 20.62006 Pr-ELCC (MW) 44,560 53,864 70,155 83,0072006 Pr-ELCC (%)

19.9 23.8 30.5 24.6OVERLAY TRANSMISSIONTable 7-4 shows the ELCC values calculated for the transmission sensitivity case of the conceptual transmission overlay system. The overlay transmission system increases the transfer capability between zones and allows more of the wind capacity to serve load outside the zone where it is physically located. The transmission overlay consists of multiple new DC and AC lines in various 203 the transfer limits and add new interfaces between the zones. These changes in interface limits and new ties can be seen in Figures 7-7 through 7-10.TABLE 74. ELCC RESULTS FOR OVERLAY TRANSMISSION SYSTEMRESULTSSCENARIO 1SCENARIO 2SCENARIO 3SCENARIO 4Nameplate Wind (MW) 223,609 225,902 230,299 337,7082004 Pr-ELCC (MW) 61,884 61,655 65,205 89,7632004 Pr-ELCC (%)

27.7 27.3 28.3 26.62005 Pr-ELCC (MW) 56,737 63,248 64,711 83,8072005 Pr-ELCC (%)

25.4 28.0 28.1 24.82006 Pr-ELCC (MW) 53,956 60,913 75,552 100,6802006 Pr-ELCC (%)

24.1 27.0 32.8 29.8Figure 7-11 shows the ELCC results for both the existing and the overlay overlay system because the conceptual overlay increased the transfer capability between zones. These results are depicted for the four scenarios, and the different Figure 7-11. ELCC results for existing and overlay transmission 204TIEONLY BENEFITScases without any wind resources modeled and then comparing the isolated system results with those of an interconnected system such as the existing and not be realized if each of the zones were not part of an interconnected system, meaning that they would function like an isolated system.

Figure 7-12. Tie benet results for existing and overlay transmission 205TABLE 75. BENEFITS FROM OVERLAYLOLE TIE BENEFITS ~MWSCENARIO 1SCENARIO 2SCENARIO 3SCENARIO 4PROFILE 2004Existing Transmission System 53,747 53,444 53,321 53,025Conceptual Overlay System 55,714 55,163 54,759 54,321LOLE Tie Bom Overlay 1,967 1,719 1,438 1,296 2005Existing Transmission System 55,458 55,400 55,437 54,922Conceptual Overlay System 64,010 62,961 62,783 62,726LOLE Tie Bom Overlay 8,552 7,561 7,346 7,804 2006Existing Transmission System 49,407 49,334 49,274 48,885Conceptual Overlay System 51,898 51,587 51,898 51,017LOLE Tie Bom Overlay 2,491 2,253 2,624 2,132ANALYSISWIND CONTRIBUTIONThe ELCC results from this analysis based on the four wind penetration contribution to serving load with the conceptual transmission overlay added.

The existing transmission system without the transmission overlay could expect the conservative lower bounds of the overall study results because of the risk associated with overestimating the capacity contribution of wind. Discretion and prudence must be practiced when considering these results. This consider is the limitation of using a transportation-style model. Although there are interface limits and ties between study zones, it is still assumed that there are no internal constraints or deliverability issues within the study zones. When OVERLAY CONTRIBUTIONoverlay would help move capacity needed for resource adequacy out of one area where it is not particularly needed into another area where it is needed. With the conceptual transmission overlay in place, there is less need for new power plants.

206As with the ELCC results, discretion must be practiced when looking at these of a system, which can vary greatly and yearly throughout the entire Eastern 207SECTION 8: SYNTHESIS AND IMPLICATIONSNOTES ON THE ANALYTICAL METHODOLOGYprocedure illustrated in Figure 8-1. The National Renewable Energy Laboratory's (NREL) mesoscale data set for the eastern United States was the starting point Wind plant locations in each scenario were mapped into a generation expansion model that estimated the amount of new conventional generation that would be incorporated new generation from the expansion process and wind from the a case with transmission constraints enforced to one with no transmission constraints allowed calculation of annual congestion charges over constrained interfaces in the production model. The congestion charges then served as the basis for the design of overlay transmission and regional transmission upgrades to move energy from sources to sinks. 2024 load data were also used in various statistical analyses. These analyses were designed to determine the requirements for regulation and operating reserves that would be needed in each of the operating areas to manage the incremental variability and uncertainty introduced by wind generation. Wind generation, new nonwind generation, transmission overlay designs, and results of the statistical analysis were merged into a new set of annual production simulations. The objective here was to simulate as closely as possible the operation of individual operating pools or markets in the Eastern used to estimate the operating cost of the incremental variability and uncertainty introduced by wind generation.

The production model was also the basis for analyzing resource adequacy. The comprehensive loss of load expectation (LOLE) analysis looked at the contribution of wind generation to resource adequacy for individual regions in isolation, with existing transmission ties, and with the transmission overlays 208 developed in the earlier step. Running cases with and without wind generation allowed the project team to calculate the effective load-carrying capability (ELCC) of wind generation in each scenario. Figure 8-1 clearly shows that the through the analyses that make up the process would allow for reconciliation of inconsistencies among interim results and for improvement of subsequent study scope and schedule limitations. Figure 8-1. Flow diagram for study analytical methodologyBottom-up processes make decisions from the present to the future based on annual incremental expansions. Most previous transmission expansions used bottom-up processes; most states, for example, use such a process for approval of projects from a list of alternatives. Each transmission line is decided one at a time to meet near-term resource adequacy or delivery requirements. Bottom-up processes are usually based on resolving line-loading or voltage-level problems associated with reliability criteria.

209transmission expansion. These methods tend to create transmission designs with more transmission than bottom-up transmission methods, primarily because the transmission. The combination of capturing the economic potential of nonwind and wind generation loads transmission lines to high load factors, resulting in off peak for the wind generation and on peak for the nonwind generation. Previous sections of this report focused on results from the individual analytical steps of the process. This section brings together the various results of the analyses for an overall perspective.TOTAL COSTSchanges made include adding very large amounts of wind, regional and overlay transmission, and conventional generation. As described earlier, the top-down method leads to a snapshot of 2024; it does not consider the evolution of today's additional iterations of the study process, more conventional planning methods Components of cost and the approaches for tabulating them are described next.ASSUMPTIONSCosts for each scenario comprise both capital investment and production-related costs. To better compare between scenarios, the team annualized capital costs. Table 8-1 gives the assumptions used for capital costs. TABLE 81. ASSUMPTIONS USED IN SCENARIO COST CALCULATIONSTYPELEVELIZED FIXED CHARGE RATE ~%CAPITAL COST, US$2008 ~$/

kW a)COAL 12.50 1,833COMBUSTION TURBINE (CT) 12.43 597COMBINED CYCLE (CC) 12.50 857 NUCLEAR 12.53 2,928ONSHORE WIND 11.92 1,875OFFSHORE WIND 11.92 3,700TRANSMISSION 15NOT APPLICABLEESCALATION 3NOT APPLICABLE a kW = kilowatt 210CAPITAL COSTSNEW GENERATIONThe economic transmission development process began with a conventional generation in the case to meet the load reliably in the future year being studied. for each scenario, then determining what new generation would be required to maintain regional resource adequacy. Wind generation was assigned a capacity value of 20% of nameplate for the generation expansion runs. The LOLE analysis described in Section 7 revealed that the actual capacity value with the overlay transmission was higher than 20% for all scenarios. To compensate, the original conventional generation expansion transmission, a certain amount of that capacity would not be needed for resource adequacy. Given that capital costs for conventional generation technologies vary widely, the adjustment cannot be done without further iterations of the generation expansion model. Consequently, the conventional generation capital CAPITAL COSTSNEW TRANSMISSIONSection 4 covered the cost of regional and overlay transmission for each The transmission capital costs include estimates for the extra-high voltage substation equipment. Some regional or local upgrades would be necessary for moving energy to or from the transmission backbone, and cost estimates for these upgrades are Southwest Power Pool (SPP), some detail was available for regional upgrades in these areas. Much less information was available for the other operating areas, and the transmission capital costs may be understated as a result. The overlay transmission makes up the majority of the transmission capital cost, however, and the results are from a single iteration of the top-down economics-based capital costs. PRODUCTION COSTSProduction costs for each scenario were extracted from the annual PROMOD maintenance (O&M) costs, make up production costs.

211TOTAL COSTSFigure 8-2 shows the total costs for each scenario. Costs for each scenario are calculated as the sum of production-related costs plus annualized amounts for capital investments in new conventional generation, wind plants, and transmission. The results for the Reference Scenario and the least costly of the 20% scenarios and that the increased cost of offshore wind is a major cost element of Scenarios 3 and 4. Transmission costs are a relatively small fraction for all scenarios, with only a small absolute difference in this component in the 20% cases.

None of the scenarios includes any costs associated with carbon.Figure 8-2. Costs by scenarioIMPLICATIONS OF THIS STUDYthe study.

212GENERAL OBSERVATIONSgeneral observations. The wind generation does not need 100% transmission for the rated wind generation connected to the transmission system. The geographic diversity of wind generation produces a coincident peak capacity of 80%-90% of the total rated wind generation. Transmission does not need to be sized to handle all the wind generation at its maximum coincident output. Some wind can be curtailed for some hours more economically than building transmission that would be loaded only for those few hours. Adding more generation with small curtailments to meet the renewable energy standards can be more cost-effective than designing a transmission system for the peak coincident output of compared to the cost.The combination of large pools of low-cost energy delivered to higher priced areas and the abundance of generation capacity off peak creates a large market at the 20% wind energy level. The price signal is quite sensitive to the price of drives the need for transmission. The assumed price of $8/MBtu (millions of across the interconnection. At the US$2009 price of natural gas in the $3-$4/MBtu range, the energy market prices are already level and the difference in energy price across the Eastern At 30% wind energy, energy market prices are practically level across the Eastern transmission expansion based on marginal prices. Wind generation generally does not appear on peak and contributes less to serving load on peak than off peak. Wind generation on the peak hour in the diversity is expected to increase the capacity contribution of wind on the peak period.

213The capacity credit given to wind reduces the amount of other generation that must be constructed. The incremental economic value of the diversity factor (the capacity factor owing to diversity) can be estimated by running a case with example, an 8% reduction in other generation would apply to about 30% of the total cost of the wholesale price energy. POLICY IMPLICATIONSresults of this study pose some interesting policy and technology development questions:Could the levels of transmission, including the Reference Case, ever be permitted and built, and if so, what is a realistic time frame?Could the level of offshore wind energy infrastructure be ramped up fast scenarios?bottom-up process with more stakeholders involved?How can states and the federal government best work together on regional transmission expansion and the massive development of onshore and offshore wind infrastructure?What is the best way for regional entities to collaborate to make sure wind is optimally and reliably integrated into the bulk electrical grid? What is the difference between applying a carbon price instead of mandating and giving incentives for additional wind?

214SECTION 9: FINDINGS, CONCLUSIONS, AND RECOMMENDATIONS The analytical modeling of the operational impacts of wind generation in the scale and at a level of detail not previously attempted for this type of analysis. generation over three annual periods. recommends follow-up actions or further investigation. KEY FINDINGS AND CONCLUSIONS A number of conclusions can be drawn from the process and results of the analytical work in this study. These are summarized by topical category in the following subsections.CONCEPTUAL TRANSMISSION OVERLAYtransmission across an interconnection was a principal element of this study. is preferred if not required because of the volumes of energy that must be transported across and around the interconnection and the distances involved.Similar levels of new transmission are needed across the four scenarios, and certain major facilities appear in all of the scenarios. The study focused on a snapshot of four possible 2024 futures; how to get to any one of those futures was outside the scope of the study. The commonality of transmission elements across the four scenarios reveals important information should that effort be undertaken.The modeling indicates that a substantial amount of wind can be accommodated if adequate transmission is available.the capacity credit contribution of wind generation by a measurable and here. For example, it would be possible to schedule reserves from one area 215to another, effectively transporting variability caused by wind and load to areas that might be better equipped to handle it. And the transfer capability of the underlying AC network could be enhanced by using the DC terminals to mitigate limitations related to transient stability issues. WIND GENERATION IMPACT ON RESERVESCurrent operating experience gives little guidance on how to manage the incremental variability and uncertainty associated with large amounts of wind conducted on the time-series data from the scenarios, however, produced a very reasonable analytical foundation for the assumptions and reserve requirement results that were carried forward to the production simulations. include the following:operated in 2024 played an important role in minimizing the additional amounts of spinning reserve that would be required to manage the variability of large amounts of wind generation. The pooling of larger amounts of load and discrete generating resources variability of load declines as the amount of load increases; larger markets also have more discrete generating units of diverse fuel types and capabilities to use for meeting load and managing variability.With real-time energy markets, changes in load and wind that can be term forecasts of wind generation. Both variability and uncertainty of aggregate wind decrease percentage-wise with more wind and larger geographic areas.using National Renewable Energy Laboratory (NREL) mesoscale data.Characterizations are useful for estimating incremental reserve require

-ments.Load changes over 10-minute intervals can be forecast well, and are therefore cleared in the regional transactions market.Current energy market performance shows that subhourly market prices, on average, do not command a premium over day-ahead prices. As a consequence, the hourly production simulation will capture most of the costs associated with units moving in subhourly markets, and the spinning 216reserve requirements for regulation and contingency will appropriately constrain the unit commitment and dispatch.OPERATIONAL IMPACTSThe detailed production modeling of a system of such size and scope reduces the number of assumptions and approximations required to obtain a solution. The extremely large volume of results is a disadvantage, but they do contain with respect to wind generation impacts on other system resources:Generation displacement depends on location and amount of wind generation.Fossil units are displaced because of the requirements for additional Wind generation reduces locational marginal price (LMP) in all operating regions.The effect appears to be greatest with local wind resources.Offshore wind has more effect on LMP in eastern load centers.WIND GENERATION CURTAILMENTrelieve congestion. Even so, a modest amount of curtailment was observed in some operating areas. Local or subregional transmission congestion is the probable cause because the production simulation results gave no clear evidence A certain amount of wind generation curtailment was to be expected, based on the process by which the overlay transmission concepts were developed. transaction energy from the unconstrained production simulation case.WIND GENERATION CONTRIBUTION TO RESOURCE ADEQUACY Assessing the capacity value of wind generation has been a staple of most of the integration studies conducted over the past several years (see Bibliography). The approach taken in this project represents the most thorough and detailed investigation to date because of the size and scope of the model, the process by which areas transfer limits were determined, and the sensitivity analyses performed. The study team recognizes that the results represent a macro view and do not consider some important intraregional transmission constraints. Because of the project focus on transmission, however, the results represent a target resource adequacy contribution that could be achieved for the wind generation scenarios.

217The loss of load expectation (LOLE) analysis performed for this study shows that the existing transmission network in the Eastern the transmission overlays developed in this study estimates that the ELCC of the wind generation ranges from 24.1% to 32.8% of the rated installed capacity. The transmission overlays increase the ELCC of wind generation anywhere from a few to almost 10 percentage points (e.g., 18% to 28%). The ELCC of wind can vary greatly geographically depending on which observed interannual variations; these variations, however, were much smaller than had been observed in previous studies (e.g., EnerNex 2006). be the same between all four scenarios.WIND INTEGRATION COSTS AND IMPACTSAssessing the costs and impacts of integrating large amounts of wind generation was another key aspect of this study. Methods and analytical approaches used in earlier integration studies were the starting point, but as interim results became available, nuances and challenges in those methods when they are applied to a large, multiarea production model became apparent. As a result of this project, then, the team learned a great deal of useful information about the total costs associated with managing the delivery of wind energy.over the entire model footprint. Salient points include the following:The conventional proxy resource assumption is not usable with very large amounts of wind generation.Because the production simulation model contains multiple operating areas, and transactions between these areas are determined on an economic basis, variability from wind in a given area will be carried through economic transactions to other areas.Earlier integration studies isolated the subject area by restricting The integration costs over the entire model are accurate because all transactions sum to (nearly) zero.Costs for integrating wind across the interconnection vary by scenario.

For the 20% cases, Scenario 1 showed the highest cost at $8.00/MWh (megawatt-hour) of wind energy; Scenario 2 follows at $7.21/MWh.

Scenario 3 shows the lowest integration costs at $5.77/MWh. These costs 218are in US$2024; using the 3% escalation factor, the integration costs in US$2009 would be $5.13/MWh for Scenario 1, $4.63/MWh for Scenario 2, and $3.10/MWh for Scenario 3.The integration cost results for the 20% scenarios show that spreading the wind more evenly over the footprint reduces the cost of integration. MWh in US$2009. This scenario is roughly a combination of Scenarios 1 and 3.Using the actual shape as the proxy resource (with no intrahour variability or uncertainty over any forward time frame) eliminates any issues related to the "value" of wind energy between the actual and ideal wind cases.The actual shape proxy, however, does potentially mask or leave out some true operational costs, for example, backing down or possibly even decommitting fossil-fuel units to accommodate wind generation. Wind generation reduces LMP in all operating regions.The reduction appears to be greatest with local wind resources.Offshore wind has more effects on LMP in eastern load centers.SENSITIVITIESPRODUCTION MODEL ASSUMPTIONS AND CURTAILMENT The study team investigated the cause of wind generation curtailment by running additional production simulation cases. The results produced quantitative information about the causes, revealing the following:generation curtailment (decrease of 0.27%).Setting the dispatch price of wind generation to negative $40/MWh re

-duced curtailment by just under 50% (6.38% to 3.51%).The copper sheet case shows a curtailment level of 0.12%, which is most likely because of minimum generation constraints.

-tailment by only 2%.

This information led the team to conclude that transmission congestion is the primary cause of wind generation curtailment in Scenarios 1 through 4. UNIT COMMITMENT WITH PROMOD IV BID LOGICalgorithm that was not used for the base production simulations in the study because it increases simulation time. A sensitivity case using wind and load approach and the effect on production and integration costs.

219Figure 9-1 presents a comparison of the two approaches and shows that although the bid-logic approach does increase production and integration costs, the effects are relatively minor.

Figure 9-1. Comparison of production simulation results (integration cost) for base unit-commitment algorithm and more sophisticated "bid-logic" approachINFLUENCE OF HURDLE RATEScommitment and dispatch of resources within each region. To assess the effect of these tariffs on production and integration cost, the study team ran production simulations again for two scenarios with hurdle rates set to zero. Under these conditions, the program optimizes the commitment and dispatch of resources Results from these simulations show that the hurdle rates have only a minor impact on production costs (shown in Figure 9-2) and integration costs.

220Figure 9-2. Comparison of LMPs for hurdle rate sensitivityCARBON SENSITIVITYNo carbon penalties or limits were considered in the base set of assumptions for evaluating the impacts of carbon penalties or other limitations on the generation expansion, transmission overlay design, production costs, and integration costs associated with wind generation. The entire analytical methodology, except for the LOLE analysis, was run for a scenario that considered a carbon price of $100/ton. The high price was generation built during the expansion process. Figure 9-3 shows the results of the expansion. Figure 9-4 gives more detail and compares the expansion for this case 221Figure 9-3. Scenario 2, carbon case generation expansionNotes: CC = combined cycle; CT = combustion turbine; DR = demand response; IGCC = integrated gas combined cycle; IGCC/Seq = integrated gas combined cycle with sequestration; CC/Seq = combined cycle with sequestration; RET Coal = coal plant retirements; Replacement CC = replacement combined cycleFigure 9-4. Generation expansion by scenario, 2008-2024 222Results from the production simulations showed that the effect on carbon emissions was substantial. Even though the case was based on Scenario 2 and carbon emissions were lower than those from Scenario 4 with 30% energy generated from wind (Figure 9-5).

Figure 9-5. Carbon emission comparisonLittle effect was observed on wind generation curtailment or integration cost. Fossil generation was reduced relative to the original Scenario 2 (Figure 9-6), and nuclear generation increased because the nuclear share of the new generation expansion was larger. Energy from combined cycle plants also increased as they became the preferred resource for managing variability. Energy prices increased across the footprint (Figure 9-7).

223Figure 9-6. Change in generation for carbon sensitivityFigure 9-7. Change in LMP for carbon sensitivity SCENARIO COST COMPARISONSCosts for each scenario were calculated as the sum of production-related costs plus annualized amounts for capital investments in new conventional generation, wind plants, and transmission. The results for the Reference Scenario and the 224four high-penetration scenarios developed in this study (Figure 9-8) show that Scenario 1 is the least costly of the 20% scenarios, and that the increased cost of offshore wind is a major cost element in Scenarios 3 and 4.Transmission costs are a relatively small fraction for all scenarios, with only a small absolute difference in this component seen in the 20% scenarios.

None of the scenarios includes any costs associated with carbon.Figure 9-8. Scenario cost comparisonsAchieving 20% wind energy penetration across the Eastern A single iteration of the economic transmission expansion methodology gives useful results and insights.Wind generation curtailment across the footprint ranges from a low of 3.9% in Scenario 3 to a high of 10.5% in Scenario 4.Further iterations would allow overlays to be improved and wind curtailment to be minimized.

225

SUMMARY

questions posed at the outset of the project:

1. What impacts and costs do wind generation variability and uncertainty impose on system operations? With large balancing areas and fully developed regional markets, the cost of integration for all scenarios is

about $5/MWh of wind, or about $0.005 per kilowatt-hour (kWh) of

electricity used by customers.

2.

multiple and geographically diverse wind resources? The study results

show that long-distance (and high-capacity) transmission can assist smaller balancing areas with wind integration, allowing penetration levels that would not otherwise be feasible. Furthermore, all scenarios, including the Reference Case, made use of major transmission upgrades

wind integration.

3.

large quantities of remote wind energy to urban markets? Long-distance

operations, contributes substantially to integrating large amounts of wind

distance transmission has other value in terms of system robustness that

4.

Truewind 2009) shows that the higher quality winds in the Great Plains have capacity factors that are about 7%-9% higher than onshore wind resources near the high-load urban centers in the East. Offshore plants have capacity factors on par with Great Plains resources but the cost of energy is higher because capital costs are higher.

5. How much does geographical diversity, or spreading the wind out substantially.
6. wind generation, forecasting will play a key role in keeping energy while maintaining system security.

226 7. wind variability and uncertainty management? This and other recent studies (see Bibliography) reinforce the concept that large operating with adequate transmission, are the most effective measures for managing wind generation.

8. How does wind generation capacity value affect supply resource adequacy? Wind generation can contribute to system adequacy, and additional transmission can enhance that contribution.

The scenarios developed for this study do not in any way constitute a plan; instead, they give a top-down, high-level view of four different 2024 futures. The transition over time from the current state of the bulk power system to any one of the scenarios would require much more technical and economic evaluation, underlying transmission systems. A more thorough evaluation of the sensitivity of the results from this study to changes in assumptions or scenarios would also RECOMMENDATIONS FOR FUTURE STUDYwas driven primarily through economics-based transmission expansion planning, resource adequacy studies, and hourly modeling simulations. were not studied or were represented approximately or by means of best engineering judgments.

This study is an important step in the uncertain world of long-range planning because it addresses questions such as feasibility and total ultimate costs, and begins to uncover important additional questions that will require answers. that additional key stakeholders must be involved to further develop an interconnection-wide view of transmission system plans.of additional technical analysis. The framework established by the scenario starting point for employing conventional power system planning to further evaluate the feasibility of these high-penetration scenarios and to improve the cost estimates.

227Production simulation results from this study could be used to identify times other periods of interest, such as times when there are large changes in wind generation would raise questions about the security of the system. For such an appropriate AC power system model. A variety of power system engineering analyses could then be conducted to determine what additional equipment or operating limitations would be necessary to maintain system reliability. Two examples follow: An AC analysis could examine in more detail the power transfer limita

-tions assumed in the production modeling. The production simulations issues associated with voltage control and reactive power dispatch. This -Local and regional transmission needs could then be analyzed in much greater detail. Longer term dynamic analysis would allow more detailed simulation and analysis. For example, the actions of automatic generation control (AGC), load tap changing on transformers, and capacitor or reactor switching for voltage control could be simulated and analyzed in much greater detail.

This, in turn, would enable examination of subhourly market operation and the response of generation to either AGC or market dispatch in

-structions. And that examination would allow investigators to consider actions, or special protective schemes. These types of analyses could be used to zoom in on the operation of the system in real time, resulting in policies needed to maintain performance and reliability.require participation and collaboration from a large number of entities across the interconnection. Personnel engaged in running similar studies with a regional focus would need to be involved, at a minimum, in a review capacity and for interpretation of results. National entities such as the North American Electric Reliability Corporation (NERC) would also need to be engaged to oversee the development of the data sets and models. The size and scope of the system models might also require computational power beyond what is used today in the power industry, and therefore could involve universities or national laboratories with appropriate resources.

228The top-down views of the interconnection produced in this study constitute, in analysis. The analysis would paint a more accurate picture of the total transmission investment necessary, and illuminate what would be required to would be beyond the scope of what has previously been attempted, and would require cooperation and coordination at many levels to succeed. The results of this study pose some interesting policy and technology development questions:Could the levels of transmission, including the Reference Case, ever be permitted and built, and if so, what is a realistic time frame?Could the level of offshore wind energy infrastructure be ramped up fast scenarios?bottom-up process with more stakeholders involved?How can states and the federal government best work together on regional transmission expansion and the massive development of onshore and offshore wind infrastructure?What is the best way for regional entities to collaborate to make sure wind is integrated into the bulk electrical grid optimally and reliably? What is the difference between applying a carbon price versus mandating and giving incentives for additional wind?

As is expected in a study of this type, especially when a wide variety of technical experts and stakeholders are giving ongoing input, a number of important of technical areas in the study present opportunities for further technical investigation that could deepen understanding or reveal new insights:Further analysis of production-cost simulation results: The output from the many annual production simulations performed in this study contains detail on every generator and monitored transmission interface the analysis conducted in this study was necessarily limited to summary results. Further analysis of this output data would likely generate additional valuable insights on impacts of wind generation on the could be conducted going forward.Smart grid implications and demand response sensitivities: The Eastern projections out to the 2024 study year. For the most part, load was considered static. Major industry initiatives are currently exploring means by which at least a portion of the load might respond like a supply 229resource, thereby relaxing the constraints on scheduling and dispatch of conventional generating units. The implications for wind generation are consider the range of smart grid implications for the bulk electric system merit further study. adoption of electric vehicles has the potential to alter the familiar diurnal shape of electric demand. Because the wind resource is abundant at night and during the low-load seasons, increases in electric demand during these times could ease some of the issues associated with integration. Commitment/optimization with high amounts of wind: The approach for scheduling and dispatching generating resources used in the production practices and energy market structures might be implemented that take advantage of the fact that uncertainty declines as the forecast horizon is that allow more frequent reoptimization of the supply resources could offer some advantage for accommodating large amounts of variable and uncertain wind energy. single "future" for prices of other fuels used for electric generation. As history attests, there is much uncertainty and volatility inherent in some fuel markets, especially for natural gas. Alternate scenarios that explore the impacts of other fuel price scenarios on integration impacts and overall costs would be valuable. The role and value of electric energy storage: With the substantial transmission overlays and the assumption of large regional markets, the accommodated without deploying additional energy storage resources. The ability to store large amounts of electric energy could potentially obviate the need for some of the transmission and reduce wind integration impacts, though. Analysis of bulk energy storage scenarios with generic storage technologies of varying capabilities would quantify penetrations of renewable energy. Transmission overlay enhancement: As described earlier, the analytical methodology was based on a single pass through what is considered an iterative process. Further analysis of the existing results could be used to and stability analysis. This could reduce the estimated costs of the overlay, and bolster the view of the required regional transmission expansion that would be needed to deliver the large amounts of wind energy to load.

230perspective to focus on a snapshot of four 2024 scenarios. The resulting transmission overlays and very substantial wind generation would be feasibility and costs of an aggressive transmission development future. Wind generation curtailment: Selective and appropriate use of wind generation curtailment could have high operational value. Although wind generation, downward movement is easily accomplished with today's wind generation technology, and could have very high economic value under certain circumstances. Wind generation is quite capable of providing "regulation down" ( for example, in an ancillary services market where the regulation service is bifurcated, meaning that regulation up and regulation down are separate services). Additional a service would be worth to wind plant operators.The current installed capacity of wind generation in many areas of the United States, coupled with prospective development over the next several years, requires that assessments of the bulk electric power system take a much broader of wind generation as an electric energy supply resource are leading the power industry to new approaches for planning and analyzing the bulk electric power system. Several of these techniques were demonstrated in this study, and are also being used in other large-scale wind integration analyses. The data sets compiled for the study represent the most detailed view to date of high-penetration wind 231GLOSSARYArea control error (ACE): The instantaneous difference between net actual and scheduled interchange within a control area on the power grid, taking into account the effects of frequency deviations.Automatic generation control (AGC): A control system that automatically adjusts generation units on regulation duty to compensate for random or sudden changes in demand. Depending on the characteristics of the balancing area, AGC adjustments occur over periods of tens of seconds to a minute.Adjusted production cost (APC): Captures the actual cost of serving load. The cost of purchases or sales to the outside world is adjusted from the total production cost. Balancing area (or balancing authority area [BAA]): The collection of generation, transmission, and loads within the metered boundaries of the balancing authority. The balancing authority maintains load-resource balance within this area.report, B/C is expressed as a ratio.Bus-bar: The point at which power is available for transmission.Capability: The maximum load that a generating unit, generating station, or other time without exceeding approved temperature and stress limits.Capacity: The amount of electrical power delivered or required for which manufacturers rate a generator, turbine, transformer, transmission circuit, station, or system.Capacity factor: The fraction of the nameplate rating of a wind power plant that Capacity value: A measure of the productivity of a power plant, calculated as the amount of energy that the plant produces over a set time period, divided by the amount of energy that would have been produced if the plant had been running at full capacity during that same time interval. Most wind power plants operate at capacity factors ranging from 25% to 40%.Capital costs: The total investment cost for a power plant, including auxiliary costs.

232CAPX 2020: A joint initiative of 11 transmission-owning utilities in Minnesota and the surrounding region, designed to expand the electric transmission grid.Copper sheet simulation: Sensitivity analysis with no transmission constraints in the system.Control Performance Standards 1 and 2 (CPS1 and CPS2): The reliability period. CPS1 is a statistical measure of the variability of the ACE and CPS2 is a measure of the magnitude of the ACE.Curtailment: Shutting down or limiting the output of generators to mitigate a transmission constraint or other binding constraint such as excess electricity supply relative to demand and must-run generation (minimum generation limits); limitations in ramping capability; or availability of adequate operating reserves.Dispatch: The physical inclusion of a generator's output onto the transmission grid by an authorized scheduling utility.Distribution: The process of distributing electricity. Distribution usually refers to the series of power poles, wires, and transformers that run between a high-voltage transmission substation and a customer's point of connection.Dump energy: A term representing the unavoidable surplus generation that cannot be used to serve load because of unit operating or transmission constraints. conceptual and used only for reporting purposes.Effective load-carrying capability (ELCC): The amount of additional load that can be served at the target reliability level by adding a given amount of generation. For example, if adding 100 megawatts (MW) of wind could meet an increase of 20 MW of system load at the target reliability level, the turbine would have an ELCC of 20 MW, or a capacity value of 20% of its nameplate value. Electric Generation Expansion Analysis System (EGEAS): Software from the resource forecasting. EGEAS performs capacity expansions based on long-term, least-cost optimizations with multiple input variables and alternatives. The software can perform optimizations on a variety of constraints such as reliability (loss-of-load hours), reserve margins, or emissions.Electricity generation: The process of producing electricity by transforming other forms or sources of energy into electrical energy. Electricity is measured in kilowatt-hours.

233Energy penetration: The ratio of the amount of energy delivered from one type of resource to the total energy delivered. For example, if 200 megawatt-hours (MWh) of wind energy supplies 1,000 MWh of energy consumed, wind's energy penetration is 20%.European Wind Integration Study (EWIS): An initiative established by the European transmission system operators in collaboration with the European common solutions to wind integration challenges. They are also identifying arrangements that will best use the pan-European transmission network to deliver Eastern Wind Integration and Transmission Study (EWITS): A project that evaluated the power system impacts, costs, and conceptual transmission overlays attendant with increasing wind generation capacity to 20% and 30% of retail electric energy sales in 2024 for the study area, which includes a large fraction of Federal Energy Regulatory Commission (FERC): An independent agency that regulates the interstate transmission of electricity, natural gas, and oil.Financial transmission right (FTR): A right to congestion credits or charges along against the cost and uncertainty that can arise from congestion in the market.GE Energy's Multi-Area Reliability Simulation (GE-MARS): A transportation-style model based on a sequential Monte Carlo simulation that steps through time chronologically and produces a detailed representation of the hourly loads and Gigawatt (GW): A unit of power that is instantaneous capability equal to 1 million kilowatts.Gigawatt-hour (GWh): A unit or measure of electricity supply or consumption of 1 million kilowatts over a period of 1 hour1.157407e-5 days <br />2.777778e-4 hours <br />1.653439e-6 weeks <br />3.805e-7 months <br />. Grid: A common term for an electricity transmission and distribution system. See also power system and utility grid.Hurdle rate: Rates used in the production-cost model to allow regional transactions during the security-constrained unit-commitment and dispatch commitment. The dispatch hurdle rates are the economic adders between 234impacts. Within a regional transmission organization (RTO), there are no hurdle rates; the hurdle rates are between RTOs. The commitment hurdle rate is a mechanism to commit pool generation for pool load. Commitments to serve load outside the pool are made based on the price differentials.Kilovolt (kV): One volt is the basic unit of electromotive force, or difference in a resistance of one ohm. One kilovolt is equal to 1,000 volts.Kilowatt (kW): A standard unit of electrical power that is instantaneous capability equal to 1,000 watts.Kilowatt-hour (kWh): A unit or measure of electricity supply or consumption of 1,000 watts over a period of one hour.Load (electricity): The amount of electrical power delivered or required at any energy-consuming equipment.Load factor: interval.Load following: An electric system's process of adjusting its generation to follow changes in demand over periods of several minutes to hours. The goal of the practice is to ensure that generators are producing neither too little nor too much energy to supply the utility's customers.Levelized cost of energy (LCOE): An important measure of wind-resource quality for each facility in the database and for the wind database as a whole. The LCOE Locational marginal price (LMP): The marginal cost of serving the next megawatt of demand. LMP depends on the system transmission constraints and the performance characteristics of generation resources. Loss of energy expectation (LOEE): The expected unsupplied energy resulting from generating inadequacy. The LOEE incorporates the severity of the Loss of load expectation (LOLE): which the load exceeds the available generating capacity.

235Loss of load probability (LOLP): The probability that the load will exceed the generation at a given time. When this probability is summed over a time frame ( e.g., 1 year), it is known as LOLE.Megawatt (MW): The standard measure of power plant electricity-generating capacity. One megawatt is equal to 1,000 kilowatts or 1 million watts.Megawatt-hour (MWh): A unit of energy or work equal to1,000 kilowatt-hours or 1 million watt-hours.Mesoscale: Atmospheric phenomena (temperature, pressure, precipitation, and wind, for example) on scales of several kilometers to several hundred kilometers.Nameplate rating (or nameplate capacity): The maximum continuous output manufacturer.Power: The rate of production or consumption of energy.Power system: A common term for an electricity transmission and distribution system. See also utility grid.Production-cost model: generators. The model has been developed into an hourly, security-constrained, dispatch algorithm that minimizes costs while simultaneously adhering to Ramp rate: The rate of change in output from a power plant.Regional Transmission Organization (RTO): An independent organization established to operate the transmission assets and deliver wholesale transmission transmission facilities but operates them on behalf of the transmission-owning utilities. A RTO can operate a central energy market in addition to furnishing transmission services. (Note: RTO can sometimes stand for regional transmission operator, depending on context.)ReservesContingency Reserves: as the unexpected failure or outage of a system component such as a generator, transmission line, circuit breaker, switch, or other electrical element.

236balancing authority to meet the disturbance control standard (DCS) and other NERC and regional reliability organization contingency requirements.Operating Reserves: regulation, load forecasting error, forced and scheduled equipment outages, and local area protection. This type of reserve consists of both generation synchronized to the grid and generation that can be synchronized and made Regulating Reserves: normal regulating margins. Regulating reserves, which are responsive to AGC, are the primary tool for maintaining the frequency of the bulk electric system at 60 Hz. Spinning Reserves: The portion of operating reserve consisting of (1) generation synchronized to the system and fully available to serve load within the disturbance recovery period following the contingency event; or (2) load fully removable from the system within the disturbance recovery period following the contingency event.Reserve margin: Percentage by which available generating capacity is expected to exceed forecast peak demand.Security-constrained unit commitment (SCUC): An area-wide optimization process designed to meet electricity demand at the lowest cost, given the transmission system.Single largest hazard (SLH): Largest possible single loss of generating capacity resulting from either forced outage of generation or transmission equipment. Also called single largest contingency. Utility grid: A common term for an electricity transmission and distribution system. See also power system.Western Electricity Coordinating Council (WECC): The regional entity responsible for coordinating and promoting bulk electric system reliability in the Western Wind and Solar Integration Study (WWSIS): A study examining the planning and operational implications of adding up to 30% of wind and solar energy penetration to the WestConnect footprint in the WECC.

237Wind integration costs: that can be attributed to the variability and uncertainty introduced by wind generation.Wind power: Power generated by using a wind turbine to convert the mechanical power of the wind into electrical power. Wind power plant: A group of wind turbines interconnected to a common utility system.Wind resource assessment: The process of characterizing the wind resource and Wind speed: Wind turbine: A device that converts wind energy to electricity.

238BIBLIOGRAPHYAWS Truewind, LLC. 2009. Development of Eastern Regional Wind Resource and Wind Plant Output Datasets. Prepared under Subcontract No. ACO-8-88500-01. NREL/SR-550-46764. Golden, CO: National Renewable Energy Laboratory (NREL).Bai, X., K. Clark, G. Jordan, N. Miller, and R. Piwko. July 2007.

Intermittency Analysis Project: Appendix B, Impact of Intermittent Generation on Operation of California Power Grid. Prepared by GE Energy Consulting for the California GE Energy Consulting. http://www.energy.ca.gov/2007publications/CEC-500-2007-081/CEC-500-2007-081-APB.PDF.Transmission and Operating Issues and Recommendations for Integrating Renewable Resources on the California ISO-Controlled Grid. http://www.caiso.com/1ca5/1ca5a7a026270.pdf.and Transmission of Higher Levels of Wind Power." IEEE Power and Energy , 7(6): 36-46.Corbus, D., M. Milligan, E. Ela, M. Schuerger, and R. Zavadil. September 2009. Eastern Wind Integration and Transmission Study-Preliminary Findings. Preprint. Power and on Transmission Networks for Offshore Wind Farms, October 14-15, Bremen, Germany. NREL/CP-550-46505. Golden, CO: NREL. http://www.nrel.

gov/docs/fy09osti/46505.pdf.Department of Communications, Energy and Natural Resources (Governments All Island Grid Study. http://www.dcenr.20% Wind Energy by 2030: Increasing Wind Energy's Contribution to U.S. Electric Supply. DOE/GO-102008-2567. Washington: EERE. http://www1.eere.energy.gov/windandhydro/pdfs/41869.pdf.EnerNex Corporation. March 2007. Final Report-Avista Corporation Wind Integration Study. Knoxville, TN: EnerNex. http://www.uwig.org/

239 EnerNex Corporation. November 2006. Final Report-2006 Minnesota Wind Integration Study, Volume I. Knoxville, TN: EnerNex. http://www.uwig.org/

windrpt_vol%201.pdf.Xcel Energy and the Minnesota Department of Commerce Wind Integration Study-Final Report. Knoxville, TN: EnerNex and St. Paul, MN: WindLogics. http://www.uwig.org/

XcelMNDOCStudyReport.pdf.EWEA (European Wind Energy Association). December 2005. Large-Scale Integration of Wind Energy into the European Power Supply: Analysis, Issues and Recommendations. Brussels: EWEA. http://www.ewea.org/index.php?id=178. GE Energy. March 2008. Attachment A: Analysis of Wind Generation Impact on ERCOT Ancillary Services Requirements. Prepared for the Electric Reliability GE_Study.zipCouncil). 2009.

2009 State of the Markets Report. http://www.hks.harvard.edu/JCSP (Joint Coordinated System Plan). 2008. J CSP'08 Executive Summary; JCSP'08 Report, Volume 1: Economic Assessment; and JCSP'08 Volume 2: Reliability Assessment. http://www.jcspstudy.org/.Kirby, B., M. Milligan, R. Gramlich, and M. Goggin, July 2009.

Impact of Electric Industry Structure on High Wind Penetration Potential. NREL/TP-550-46723.

Golden, CO: NREL.Meibom, P., R. Barth, H. Brand, B. Hasche, D. Swider, H. Ravn, and C. Weber.

July 2007. All Island Grid Study, Wind Variability Management Studies, Workstream 2B egional Generation Outlet Study: Phase I Executive Summary Report. http://www.midwestmarket.org/publish/Report_DRAFT_121109.pdf?action=download&_property=Attachment 240Milligan, M., and B. Kirby. June 2008.

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241Wiser, R., and M. Bolinger. July 2009. Wind Technologies Market Report. DOE/GO-102009-2868. Washington: EERE, July 2009. http://eetd.lbl.gov/ea/ems/reports/2008-wind-technologies.pdf.Xcel Energy. March 2007.

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PSCoELCCFinalReport030107.pdf Zavadil, R. 2006. Final Report: Wind Integration Study for Public Service Company of Colorado. Knoxville, TN: EnerNex. http://www.nrel.gov/wind/systemsintegration/pdfs/colorado_public_service_windintegstudy.pdf F1146-E(10/2008)

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1. REPORT DATE (DD-MM-YYYY) 04-19-10 2. REPORT TYPE Subcontract report
3. DATES COVERED (From - To) 2007 - 2009 4. TITLE AND SUBTITLE Eastern Wind Integration and Transmission Study 5a. CONTRACT NUMBER DE-AC36-08-GO28308 5b. GRANT NUMBER 5c. PROGRAM ELEMENT NUMBER
6. AUTHOR(S) Robert Zavadil

- Study Lead 5d. PROJECT NUMBER NREL/SR-550-470 78 5e. TASK NUMBER WE10-4311 5f. WORK UNIT NUMBER

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EnerNex Corporation, Knoxville, Tennessee

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National Renewable Energy Laboratory 1617 Cole Blvd.

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NREL 11. SPONSORING/MONITORING AGENCY REPORT NUMBER NREL/SR-550-470 78 12. DISTRIBUTION AVAILABILITY STATEMENT National Technical Information Service U.S. Department of Commerce 5285 Port Royal Road Springfield, VA 22161

13. SUPPLEMENTARY NOTES NREL Technical Monitor: David Corbus
14. ABSTRACT (Maximum 200 Words)

The Eastern Wind Integration and Transmission Study was designed to answer questions posed by a variety of stakeholders about a range of important and contemporary technical issues related to a 20% wind energy scenario for the large portion of the electric demand in the Eastern Interconnection.

15. SUBJECT TERMS wind energy integration: wind energy interconnection; transmission study; utility grid;
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