ML19291C498
| ML19291C498 | |
| Person / Time | |
|---|---|
| Site: | Allens Creek File:Houston Lighting and Power Company icon.png |
| Issue date: | 01/14/1980 |
| From: | Biddle C, Copeland J, Culp R, Edwards J, Newman J BAKER & BOTTS, HOUSTON LIGHTING & POWER CO., LOWENSTEIN, NEWMAN, REIS, AXELRAD & TOLL |
| To: | TEXAS PUBLIC INTEREST RESEARCH GROUP |
| References | |
| NUDOCS 8001240503 | |
| Download: ML19291C498 (12) | |
Text
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- L, UNITED STATES OF AMERICA NUCLEAR REGULATORY COMMISSION ,
1, , ,[j b/7 f C'f v ;/S BEFORE THE ATOMIC SAFETY AND LICENSING BOARD /> ,
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In the Matter of S S
HOUSTON LIGHTING & POWER S COMPANY S Docket No. 50-466 S
(Allens Creek Nuclear S Generating Station, Unit S No. 1)
APPLICANT'S FURTHER ANSWERS TO TEX PIRG'S FIFTH INTERROGATORIES The following are Applicant Houston Lighting &
Power Company's further answers to TexPirg's fifth interrogatories:
INTERROGATORY NO. 10:
In responding to this interrogatory, Applicant assumes that the intervenor uses the terms " demand" and
" electrical demand" in a general sense and, therefore, Applicant generally describes the models and methodology for for2 casting energy sales volume in KWH.
(a) Q. Regarding the industrial demand model (after first five years), what variable, if any, explicitly accounts for industrial size? In particular, is " dollar of value added per unit output," " energy intensiveness per dollar added per unit output," or " employment" utilized to measure industrial size (production) ?
A. In the HL&P forecasting methodology, energy sales in MWH and peak demands of MW for the Industrial Class of customers are analyzed by dividing the class into two separate components known as Small Industrial and Large Industrial. We assume this question refers to methodology used for the Large Industrial group. In the context of the Large Industrial group, average or typical customer size (in KWH per customer) is a figure which is arbitrary, irrelevant, and incon-8 *"4 '
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sequential. Therefore, average KWH per customer is disregarded in the forecast methodology. Equations, factors, and ratios related to this group for the forecast after five years are all of an aggregate nature, being based on total MWH or MW for the group as a whole.
The number of customers is predicted, but does not enter into any of the MWH or MW calculations and serves only to complete the system total customer count.
The two main variables in the long range Industrial estimates of aggregate MWH sales are employment and MWH per employed worker. The quantity being modeled in this procedure is total MWH consumed for the entire non-residential sector, including both that MWH which is generated and used by the customers as well as that MWH which is purchased from HL&P. From this estimate for the entire sector are subtracted those portions such as Small Commercial and Small Industrial which have been estimated by other means, and estimated self-generation.
The residual represents the MWH sales to the Large Industrial group. This procedure is followed because the available employment data cannot be disaggregated into commercial, small industrial, and large industrial components.
(b) Q. Regarding the commercial demand model, what variable, if any, explicitly accounts for the size of the commercial user? Is " floor space" explicitly accounted for?
A. With the exceptions of master-metered apartments and temporary services, commercial KWH are modeled in the aggregate. Sizes of individual customers are only relevant insofar as it is possible to have a homogeneous group of customers within any one modeling class. With a group of customers as diverse as the commercial class, the only homogeneity one could expect to find is that of size of load. Consequently, the commercial class is defined according to this criterion.
Floor space does not appear explicitly in the model.
(c) Q. Is the forecasting model better described as "enumerative (engineering) " or "econometric" in concept?
A. HL&P forecasting methodology is largely of the "econometric" type, although it has some survey elements and some other elements.
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(d) Q. Does the model differentiate end uses for the electricity and energy consumption within each user class (e.g., space heating, refrigeration, food freezing, etc. ) ? Please list each end use accounted for by user class (residential, commercial and industrial) .
A. The only end use explicitly accounted for in the various sectors of the forecast is use for street lighting. Other uses are incorporated in various types of income, employment, and use per worker variables. In addition, some models include a hot weather variable (cooling degree hours) which is the primary determinant of air conditioning usage and a cold weather variable (heating degree hours) which is the primary determinant of space heating use.
(e) Q. S.8-6 of the FS-FES notes that the model makes assumptions as to multi-f amily and single-f amily composition. Are similarly separate assumptions made with respect to mobile homes? Generally, do individually metered multi-family housing units use less electricity per capita than single-family detached units?
A. From its own studies, HL&P has found that master-metered apartment units on the average use somewhat more KWH than single-family detached dwellings. It was also found that when master-metered units convert to individual metering, average usage tends to call.
However, HL&P has no data to indicate, nor any reason to assume that individually-metered apartments use less KWH on the average than single-family detached dwellings.
No specific assumptions are made as to KWH usage of mobile homes. Individually-metered mobile homes are included in the Residential Class.
(f) Q. Does HL&P's model establish sub-categories of types of commercial users? What are those sub-categories?
A. Sub-categories of commercial users are these:
- 1) A portion of the Master Metered Apartments.
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- 2) Temporary Services.
- 3) Customers on the Miscellaneous General Service rate under 50 KVA.
- 4) A portion of those customers on the Miscellaneous General Service rate over 50 KVA who are non-residential (non-apartment) in nature.
- 5) A portion of the Street Light group.
(g) Q. State which of the following are explicitly included as an independent variable in the forecasting model, and note if the variable is used only with respect to forecasting one or two user classes: population; household size and number; housing by type; industry by type and size; commercial building by type and size; gross product of service area; sales; employment; interest rates; income; price and income elasticities of demand, by customer class and by end use; appliance / equipment data; energy efficiencies; thermal integrity of structures, fuel prices, cross elasticities of demand, by customer class, by end use for alternative forms of energy; meterology [ sic); rate structure.
A. Explicit use or non-use of the listed variables in the HL&P forecasting methodology is indicated below. The fact that any particular variable is not explicit in a model does not mean that it is not implicitly considered in a real and substantive way in the choice of values for other variables which are explicit.
Population: All groups except Public Utilities.
Household Size: All groups except Public Utilities, Large Industrial and Special Loads.
Household Numbers: (This figure is an arithmetic result of population and household size.) All group except Public Utilities, Large Industrial and Special Lom.s.
Housing by Type: (Single-family and multi-family) Residential and Master Metered Apartnent groups only.
Industry by Type and Size: This item does not fit logically in the context of this question and Applicant is unable to respond completely. The Large Industrial group is estimated individually for about eighty customers for the first five years.
1793 161 Commercial Building 1,7 Type and Size: This item does not fit logically in the context of this question and Applicant is unable to rerpond completely. Floor space is not a variable in any model. Number of customers, although not by type, is estimated and is a variable either directly or as a multiplier in weather terms of all groups which comprise the Commercial Class except those street lights which are classed Commercial. .
Gross Product of Service Area: Not used in any model.
Sales: We assume this item refers to either Retail Sales or Wholesale Sales of goods and services. Neither is used in any model.
Employment: Used only in the Large Industrial model after five years.
Interest Rates: Not used explicitly in any group.
Income: Personal disposable income is used either as a per household figure or as an aggregate in all groups except Street Lights, Large Industrial, Public Utilities, Special Loads, and Temporary Services.
Price and Income Elasticities and Cross Elasticities of Demand:
These concepts by definition are not independent variables which might be input to an econometric model, but rather are output calculations which simply express relationships between the dependent variable and the independent variables.
Such elasticities may be and are calculated for all variables which are included in the regression portions of the various models, including such variables as electric prices, gas prices, income, cooling degree hours, heating degree hours, and cycle length. Such elasticities are calculated for each regression model which is a part of a given customer class.
End use elasticities with respect to KWH use may not be calculated since no specific end uses are included in any regression equation for KWH usage.
Appliance / Equipment Data: Not explicitly used in any group, but incorporated in the income, price and other variables.
Energy Efficienciec: Not explicitly used in any group, but incorporated in the income and price variables and incorporated in other ways in the Large Industrial group through the survey and the MWH per employed worker variable.
Thermal Integrity of Structures: This item appears to be a con-cept without quantitative measurement. Not used in any 1793 162
group.
Fuel Prices: Natural gas prices are used in the Residential, Small Commercial, and Large Commercial /Small Industrial groups.
Meteorology: Cooling degree hours are used in all groups except Street Lights, Pnblic Utilities, Large Industrial, and Special Loads. Heating degree hours are used in all models except Street Lights, Public Utilities, Large Industrial, Special Loads, and the Large Commercia'1/Small Industrial groups.
Rate Structure: Type rate structure, per se, is not a quantifiable concept which can be used as a variable in a regression model. The effects on KWH usage of some types of rate structure changes can be examined through-the electric price variables in the various Commercial and Small Industrial groups. In the Residential model, those rate structure changes which would change the monthly average rates can be examined.
(h) Q. What additional independent variables, if any, are included in the demand model?
A. A variety of additional variables are included in either the arithmetic calculations or the regression equations (models) incorporated in HL&P's forecast. Some of these are included as variables in regression equations. Those variables not elsewhere mentioned are listed below. This list shows those items which are explicitly included in mathematical relationships but is not exhaustive of all factors considered.
Birth Rates Death Rates Natural Population Change Net Migration Labor Force Participation Rate Labor Force Unemployment Rate Vacancy Rates Meter Type Gross Households (housing stock)
Consumer Price Index 1793 163
Cycle Lenth Contractual Obligations of Large Industrials and Special Loads Industrial Capacity Utilization Hours of Darkness in a Month KW Ratings of Street Lights by size and Type Energy Forecasts Provided by Other Utilities Served by HL&P Billing to Calendar Month Ratios Loss Factors Energy Production (generation) in KWH Cooling Degree Hours on the Day of Peak Heating Degree Hours on the Day of Peak High Temperature on the Day of Peak Load Factor (i) Q. What is the assumed increase in the price of electricity through 1987 as used in this model? Has HL&P revised the figure for price of electricity since the FS-FES was published? If so, what is the revised figure?
A. For the residential model, the price of electricity in cents per KWH for July 1979 was 4.69.
For July 1987 the forecasted price was 6.65. These prices were later revised to 4.15 and 7.84 for 1979 and 1987, respectively. These prices are in nominal terms.
(j) Q. Does the electricity price figure (s) stated in (i) include the effects of most recent projections of price escalation at South Texas Project and ACNGS?
Does the price forecast assume that Construction-Work-In-Progress will be allowed by the PUC this year, and/or any following year?
A. The prices stated in #10 (i) do not reflect the most recent projections of price escalation at the South Texas and Allens Creek Projects.
, rice forecasts associated with the demand forecasts upon which HL&P has relied in this proceeding are developed with a process which does not include an explicit rate base calculation. Future electric prices are set so as to collect sufficient revenue to maintain as nearly as possible certain target levels of return to common equity, capitalization ratios, interest coverage ratios, dividend payments, and other cash and non-cash parameters. The size of the rate base and the extent to which CWIP would have to be included to meet the targets for those parameters has not been determined and would vary from year to year depending on the circumstances.
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(k) Q. Assuming all other variables constant, what is the ef fect of a one percent increase in electricity prices on the demand for electricity?
A. HL&P studies indicate that under these assumptions in 1985, one could expect a reduction in KWH of less than six-tenths of one percent in the residential class. For the commercial class, one could expect a reduction of slightly over one-tenth of one percent.
Almost no price elasticity of demand would be expected in the industrial class.
Respectfully submitted,
. [ TI OF COUNSEL: J. Gregory Copeland C. Thomas Biddle, Jr. f/
BAKER & BOTTS Charles G. Thrash, Jr.
3000 One Shell Plaza 3000 One Shell Plaza Houston, Texas 77002 Houston, Texas 77002 LOWENSTEIN, NEWMAN, REIS, Jack R. Newman AXELRAD & TOLL Robert H. Culp 1025 Connecticut Avenue, N.W. 1025 Connecticut Avenue, N.W.
Washington, D.C. 20036 Washington, D.C.
ATTORNEYS FOR APPLICANT HOUSTON LIGHTING & POWER COMPANY 1793 165
STATE OF TEXAS S S
COUNTY OF HARRIS S BEFORE ME , the undersigned authority, on this day personally appeared J. M. Edwards, who upon his oath stated that he has answered the foregoing Applicant's Further Answers to TexPirg's Fifth Interrogatories to Houston Lighting & Power Company in his capacity as Supervisor, Budget and Economic Studies Section, Rate and Economic Research Department for Houston Lighting & Power Company, and all statements contained therein are true and correct to the best of his knowledge and belief.
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- 7. M . Edwards SUBSCRIBED AND SWORN TO BEFORE FE on this the /bl Mk day of January, 1980.
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} l/ / / ' '- 7f NOTARY PUBLIC IN AND FOR HARRIS COUNTY, T E X A S
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UNITED STATES OF AMERICA NUCLEAR REGULATORY COMMISSION BEFORE THE ATOMIC SAFETY AND LICENSING BOARD In the Matter of S S
HOUSTON LIGHTING & POWER COMPANY S Docket No. 50-466 S
(Allens Creek Nuclear Generating S Station, Unit 1) S CERTIFICATE OF SERVICE I hereby certify that copies of the foregoing Applicant's Further Answers to TexPirg's Fifth Interrogatories in the above-captioned proceeding were served on the following by deposit in the Unit d States mail, postage prepaid, or by hand-delivery this Id day of h v 7f 4 , 1980.
)
Sheldon J. Wolfe, Esq., Chairman Richard Lowerre, Esq.
Atomic Safety and Licensing Assistant Attorney General Board Panel for the State of Texas U.S. Nuclear Regulatory Commission P. O. Box 12548 Washington, D. C. 20555 Capitol Station Austin, Texas 78711 Dr. E. Leonard Cheatum Route 3, Box 350A Hon. Charles J. Dusek Watkinsville, Georgia 30677 Mayor, City of Wallis P. O. Box 312 Mr. Gustave A. Linenberger Wallis, Texas 77485 Atomic Safety and Licensing Board Panel Hon. Leroy H. Grebe U.S. Nuclear Regulatory Commission County Judge, Austin County Washington, D. C. 20555 P. O. Box 99 Bellville, Texas 77418 Chase R. Stephens Docketing and Service Section Atomic Safety and Licensing Office of the Secretary of the Appeal Board Commission U.S. Nuclear Regulatory U.S. Nuclear Regulatory Commission Commission Washington, D. C. 20555 Washington, D. C. 20555 R. Gordon Gooch, Esq. Atomic Safety and Licensing Baker & Botts Board Panel 1701 Pennsylvania Avenue, N. W. U.S. Nuclear Regulatory Washington, D. C. 20006 Commission Washington, D. C. 20555 1793 167
Steve Schinki, Esq. Carolina Conn Staff Counsel 1414 Scenic Ridge U.S. Nuclear Reaulatory Commission Houston, Texas 77043 Washington, D. C. 20555 Elinore P. Cumings John F. Doherty Route 1, Box 138V 4327 Alconbury Street Rosenberg, Texas 77471 Houston, Texas 77021 Stephen A. Doggett, Esq.
Robert S. Framson P. O. Box 592 Madeline Bass Framson Rosenberg, Texas 77471 4822 Waynesboro Drive Houston, Texas 77035 Robin Griffith 1034 Sally Ann Carro Hinderstein Rosenberg, Texas 77471 8739 Link Terrace Houston, Texas 77025 Leotis Johnston 1407 Scenic Ridge D. Marrack Houston, Texas 77043 420 Mulberry Lane Bellaire, Texas 77401 Rosemary N. Lemmer 11423 Oak Spring Brenda McCorkle Houston, Texas 77043 6140 Darnell Houston, Texas 77074 Kathryn Otto Route 2, Box 62L F. H. Potthoff, III Richmond, Texas 77469 7200 Shady Villa, #110 Houston, Texas 77055 Frances Pavlovic 111 Datonia Wayne E. Rentfro Bellaire, Texas 77401 P. O. Box 1335 Rosenberg, Texas 77471 Charles Perez 1014 Montrose James M. Scott, Jr. Houston, Texas 77019 8302 Albacore Houston, Texas 77074 William Schuessler 5810 Darnell Bryan L. Baker Houston, Texas 77074 1118 Montrose Houston, Texas 77019 Patricia L. Streloin Route 2, Box 393C Dorothy F. Carrick Richmond, Texas Box 409, Wagon Rd. Rfd. #1 Wallis, Texas 77485 i793 168
Glen Van Slyke 1739 Marshall Houston, Texas 77098 Donald D. Weaver P. O. Drawer V Simonton, Texas 77476 Connie Wilson 11427 Oak Spring Houston, Texas 77043 Mr. J. Morgan Bishop 11418 Oak Spring Houston, Texas 77043 0' 'livhM '
C. Thomas Biddle, Jr.
2:
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