ML18023B081
| ML18023B081 | |
| Person / Time | |
|---|---|
| Site: | Susquehanna  |
| Issue date: | 03/05/1979 |
| From: | Pennsylvania Power & Light Co |
| To: | Office of Nuclear Reactor Regulation |
| References | |
| Download: ML18023B081 (58) | |
Text
SUSQUEHANNA SES-ER-OL SECTION TITLE APP EN DX AN EVALUA
'XZS.... ~ .............
ON OF THE COST OF SF. VICE IMPACT
~
VOLUME XII OF A DELAY SUSQUEHANNA N THE IN-SERVICE ES (JANUARY 1978 D
............IXI TES OF B1 PEAK LOAD 1976-1 0.................... IXI CURRENT LONG-RA GE FORECAS ENERGY SALES 6
~ ~
APPLICANT'S FORECAS NG METHODOLOGY KMH
'IX SALES AND PEAK LOADS CE!1BER, 1976.........IXI FPC ORDER NO 496..............
NATXONMIDE FUEL EilZ GENCY ZSPONSZ TO
~ XII SUSQUEHANNA RIVE MATER ANALYSZ
SUMMARY
.. III EQUATIONS AND SSUMPTIONS UTILIZED N THE CALCULATION 0 INDIVIDUAL AND POPULA ON DOSES TO MAN .
ENVIRON%EN AL TECHNXCA L S PECIFXC ATION S.. XII
SUSQUEHANNA SES-ER-OL TABLE 1.1-3 1977 PROJECTION OF APPLICAN'A LOADS-CAPACITY-RESERVES (HIGH LOAD PROJECTION Year 1978 1979 1980 1981 1982 1983 1984 1985 Winter Peak MWe 4960 5320 5670 6100 6480 6840 7200 7570 Capacity Changes Fossil (Oil)
Nuclear 945(') 945(l)
Hydro 63(2)
Reratings Total Capacities Fossil (Coal) 4145 4145 4145 4145 4145 4145 4145 4145 Fossil (Oil) 1640 1640 1640 1640 1640 1640 1640 1640 CT 6 (j~sel 539 539 539 539 539 539 539 539 Hydro 146 146 146 146 146 146 146 209 Nuclear 945 1890 1890 1890 1890 Firm Purchase 76 76 76 76 76 76 76 76 Capacity Transactions (41) (50)
Total MWe 6505 6496 6436 7426 8405 8374 8343 8374 Reserve over winter peak:
With Susquehanna MWe Capacity 1326 1925 1534 1143 804 X of Load 22 30 22 16 ll Without Susquehanna MWe Capacity 1545 1176 766 326 (65) (436) (807) (1126)
% of Load 31 22 14 5 (1) (6) (ll) (15)
With Susquehanna But Without Oil S (1075) (476) (867) (1258) (1660)
Hydro Generation (18) (7) (13) (17) (22)
Without Susquehanna~
Oil 8 Hydro Generation (856) (1225) (1635) (2075) (2466) (2837) (3208) (3590)
Hwe Capacity (17) (23) (29) (34) (38) (41) (45) (47)
Note: See Footnotes Following Table 1.1-6.
SUSQUEHANNA SES"ER-OL TABLE 1.1-4 1977 PROJECTION OF APPLICAFA LOADS-CAPACITY-RESERVES MID-RANGE LOAD PROJECTION)
Year 1978 1979 1980 1981 1982 1984 1985 Winter Peak MWe 4820 5050 5310 5690 5990 6280 6560 6850 Capacity Changes 945"'983 Fossil (Oil)
Nuclear 945(l)
Hydro 63(2)
Reratings Total Capacities Fossil (Coal) 4145 4145 4145 4145 4145 4145 4145 4145 Fossil (Oil) 1640 1640 1640 1640 1640 1640 1640 1640 CT 6 (j~sel 539 539 539 539 539 539 539 539 Hydro 146 146 146 146 146 146 146 209 Nuclear 945 1890 1890 1890 1890 Firm Purchase 76 76 76 76 76 76 76 76 Capacity Transactions (41) (50) ~(110 (65) (31) (62) ~(93 (125)
Total MWe 6505 6496 6436 7426 8405 8374 8343 8374 Reserve over winter peak:
With Susquehanna MWe Capacity 1736 2415 2094 1783 1524 g of Load 31 40 33 '27 22 Without Susquehanna MWe Capacity 1685 1446 1126 736 425 124 (167) (406)
$ of Load 35 29 21 13 7 2 (3) (6)
With Susquehanna But Without Oil 6 (665) 14 (307) (618) (940)
Hydro Generation (12) 1 (5) (9) (14)
Without Susquehanna~
Oil 6 Hydro Generation (716) (955) (1275) (1665) (1976) (2277) (2568) (2870)
Mwe Capacity (15) (19) (24) (29) (33) (36), (39) (42)
NOTE: See Footnotes Following Table 1.1-6
SUS(UEHANNA SES-ER-OL TABLE 1.1-5 1977 PROJECTION OF APPLICANT LOADS-CAPACITY"RESERVES (IOW IOAD PROJECTION Year 1978 1979 1980 1981 1983 1984 1985 Winter Peak MWe 4650 4720 4910 5170 5390 5650 5920 6050 Capacity Changes 945"'982 Fossil (Oil)
Nuclear 945("
63(2)
Hydro Reratings Total Capacities Fossil (Coal) 4145 4145 4145 4145 4145 4145 4145 4145 Fossil (Oil) 1640 1640 1640 1640 1640 1640 1640 1640 CT 8 539 539 539 539 539 539 539 539 (j~sel 146 146 146 146 146 146 Hydro 146 146 Nuclear 945 1890 1890 1890 1890 Firm Purchase 76 76 76 76 76 76 76 76 Capacity Transactions (125)
Total Mwe 6505 6496 6436 7426 8405 8374 8343 8374 Reserve over winter peak:
With Susquehanna MWe Capacity 2256 3015 2724 2423 2324
~ of Load 44 56 48 41 38 Without Susquehanna MWe Capacity 1855 1776 1526 1256 1025 754 473 394 4 of Load 40 38 31 24 19 13 8 7 With Susquehanna But Without Oil 6 (145) 614 323 22 (140)
Hydro Generation (3) 11 6 1 (2)
MWe Capacity g of Load Without Susquehanna>
Oil 8 Hydro Generation (546) (625) (875) (1 145 ) (1376) (1647) (1928) (2070)
MWe Capacity (12) (13) (18) (22) (26) (29) (33) i (34) g of Load NOTE: See Footnotes Following Table 1.1-6.
SUSQUEHANNA SES-ER-OL TABLE 1.1-6 1977 PROJECTION OF APPLICAN'8 LOADS-CAPACITY-RESERVES (LOW-LOW LOAD PROJECTION)
Year 1978 1979 1980 1981 1982 1983 1984 1985 Winter Peak HWe 4530 4580 4720 4890 5050 5230 5420 5500 Capacity Changes Fossil (Oil)
Nuclear 945(l) 945( )
Hydro 63(2)
Reratings Total Capacities Fossil (Coal) 4145 4145 4145 4145 4145 4145 4145 4145 Fossil (Oil) 1640 1640 1640 1640 1640 1640 1640 1640 CT 8 (j~sel 539 539 539 539 539 539 539 539 Hydro 146 146 146 146 146 146 146 209 Nuclear 945 1890 1890 1890 1890 Fixm Purchase 76 76 76 76 76 76 76 76 Capacity Transactions (41) (50) ~llO (65) (31) (62) (93) (125)
Total HWe 6505 6496 6436 7426 8405 8374 8343 8374 Reserve over winter peak:
With Susquehanna NWe Capacity 2536 3355 3144 2923 2874
$ of Load 52 66 60 54 52 Without Susquehanna MWe Capacity 1975 1916 1716 1536 1365 1174 973 944 g of Load 44 42 36 31 27 22 18 17 With Susquehanna But Without Oil 6 135 954 743 522 410 Hydro Generation 3 19 14 10 7 MWe Capacity g of Load Without Susquehanna, Oil 6 Hydro Generation (426) (485) (685) (865) (1036) (1227) (1428) (1520)
MWe Capacity (9) (11) (15) (18) (21) (23) (26) (28) g of Load NOTE: See Footnotes Following Table 1.1-6
S USQ UEH A N NA S ES-E R-OL 2 3. 1 2. 2 Tornadoes The incidence of tornadoes in the site area is very low. Between the years 1950 and 1973 only 38 tornadoes were reported within 50 miles of the site. Tornado activity is at a maximum during the summer months with most tornadoes occurring in the late afternoon or evening. Figure 2. 3-1, Tornado Occurrence <<nd Intensity in the Susquehanna SZS Reqion, is a histogram for the years 1953-1962 showing tornado frequency by month, hour and intensity within a 3 by 3~ square which is centered on the site. The intensity cateqories are based on the Fujita tornado intensity classification (Ref. 2.3-5). Prom Figure 2.3- 1 it can be seen that maximum tornado occurrence is in the summer. Diurnally, tornado frequency reaches a maximum during late afternoon, shortly after the period of greatest instability 2 3.1. 2 3 Th>>ndec storms Thunderstorms in the area are usually of brief duration and concentrated in the warm months. They are responsible foc most of the summertime rainfall which normally avecaqes around 3.7 inches per month at Avoca. Based on a 19 year average at Avoca the mean number of "days with thunder heard" is 30 (Ref 2.3-3) .
A monthly breakdown of the mean number of thunderstorm days that is representative of the site is shown in Table 2.3-2, Th understocm Days for Avoca.
- 2. 3. 1 2. 4 L~iht ning There is neither documentation nor direct measurement of the occurrence of lightning other than the observation of associated thunder. Local climatological data tabulated by the National Weather Service (Ref. 2. 3-3) does not provide information reqardinq the incidence, severity. or frequency of lightning occurrences A .thunderstocm can usually be heard unless the liqhtning causing the thunder is more than 15 miles away; therefore, thunder incidence can presumably be used to confirm the presence of some lightning The number of lightning strikes per square mile per year has been established by, Uman (Ref. 2.3-6) . The combined results of several studies summarized ny Uman indicate that the number of flashes to the qround pec square mile per year is between 0 05 and 0.80 times the number of thunderstorm days pec year. The mean number of days with thunderstorms probably over estimates the actual occurrence of cloud-to-ground lightning since some thunderstocms probably contain only cloud-to-cloud lightning.
2~3 3
SUSQUEHANNA S ES-ER-OL Therefore. if the annual thunderstorm frequency at Avoca is used (30 days), the number of: ground lightning strikes is between two and 24.
2 3 1 2 5 Hail Hail in the site region sometimes falls from severe thunderstorms. Because hail f alls in narrow swa ths, only a small fraction of occurrences is recorded at regular reporting stations The average annual number of days with hail at a point in the area is 23. The occurrence of large hail (greater than
- 0. 75 inches diameter) averaqes one or two occurrences annually Accordinq to Pautz (Ref. 2.3-7) the number of hailstorms with hail 0.75 inch or greater in a one-degree longitude-latitude square area in the vicinity of the site for the period 1955-1967 was about five For Avoca from 1973-75 there was one hailstorm in June and one in July of 1973 and 1974. Xn 1975 there was also one hailstorm in August and one in October There were no occurrences of hail recorded in 1976 at Avoca (Ref. 2.3-3) 2 3.1 2 6 Extreme Minds Strong winds occur in Pennsylvania as a result of occasional hurricanes, thunderstorms, tornadoes and tropical storms. The followinq is the fastest mile of wind and its associated direction, by month, at Avoca (1955-1976) (Ref. 2.3-3).
Fastest Mile of Wind Month mph Direction Month mph Direction January 43 SE July 42 NW February 60 W Auqust 50 NE March 49 S September 38 SM April 47 NW October 38 E May 40 NW November 45 S June 43 W December 47 SM The 50-year and 100-year mean fastest mile wind speeds for the site area are 75 miles per hour and 80 miles per hour, respectively (Ref. 2.3-8) . Accordinq to Pautz, there were eight windstorms 50 knots and qreater for the one degree latitude-longitude square that includes the Susquehanna SES for the period 1955-1967 (Ref. 2 3-7) .
2 3-4
SUSQUEHANNA SES-ER-G'i.
TABLE 2.3-33 LONG-TERM TEMPERATURE ( F) AT AVOCA (Period of Record: 1956-1974)
Extreme Month ~Dail Max ~Dail Mia Mean Hicihest lowest January 33.5 18.4 26.0 67 -10 February 35.3 19.3 27.3 62 March 44.7 27.2 36.0 78 April 58.9 38.0 48.5 89 15 70.0 47.8 58.9 93 27 June 79.0 56.8 67.9 97 34 July 83.0 61.3 72.2 101 45 August 80.7 59.2 70.0 94 43 September 73.6 52.1 62.9 95 30 October 63.0 42.2 52.6 84 19 November 48.8 32.8 40.8 77 10 December 36.1 22.0 29.1 65 Annual 58.9 39.8 49.4 101 Ref. 2.3-3
SUSQUEHANNA SES-ER-OL TABLE 2.3-49 PRECIPITATION DATA FOR AVOCA (Period of Record: 1956-1974)
Month Total Greatest 24-Hour (in inches) (in inches)
January 2.04 1.52 February 1.96 1.60 March 2.50 2.20 April 3.06 1.59 May 3.50 2.58 June 3.40 3.61 July 4.09 2.33 August 3.21 3.18 September 2.82 3.09 October 2.71 2.61 November 3.01 2.91 December 2.51 2. 30 Annual 34.81 3.61 Ref. 2.3-3
SUSQUEHANNA SES-ER-OL.
TABLE 2.3-81 JOINT FRE UENCY (%) OF WIND DIRECTION, WIND SPEED AND STABILITY FOR AVOCA (Period of Record: 1971-1975)
Stability Class B Wind Speed (kts)
Se'ctor 0-3 4-6 7-10 11-16 17-21 >21 Total 898 .1507 .1164 .3569 NNE 975 .0548 .0548 .2071 NE 654 .0548 .0616 .1819 ENE 1112 .0411 .0137 .1660 768 .0274 .0342 .1385 ESE 516 .0205 0 .0721 SE 528 .0274 .0068 .0870 SSE 424 .0137 .0137 .0698 1675 .0890 .0205 .2771 SSW 898 .1507 .0959 .3364 SW 991 .2055 .1507 .4553 WSW 1773 .2877 .2123 ~ 6773 2118 .2493 .1301 .6913 1449 .1918 .1164 .4531 NW 1449 .1986 .0890 .3856 NNW 748 .1096 .1027 .2871 Total 6507 1.9726 1.2192 Relative frequency of occurrences of B Stability = 4.8425 Ref. 2.3-4
SUSQUEHANNA SES-ER-OL SECTION TITLE VOLUME APPENDICIESo o r o o o o o o e o ~ o o o o o o o o o r o or o o o o o o o o o o o III B1 AN EVALUATION OF THE COST OF SERVICE .IMPACT OF A DELAY IN THE IN-SERVICE DATES OF SUSQUEHANNA SES (JANUARY 1978)
CURRENT LONG-RANGE FORECAST ENERGY SALES 6
....... III PEAK LOAD 1976-1990................. III B2 APPLICANT' FORECASTING METHODOLOGY KWH SALES AND PEAK LOADS DECEMBER, 1976.. III FPC ORDER NO 496.............
NATIONWIDE FUEL EMERGENCY RESPONSE TO
~ III SUSQUEHANNA RIVER WATER ANALYSES
SUMMARY
...III EQUATIONS AND ASSUMPTIONS UTXLIZED IN THE CALCULATION OF IN DIVIDUAL AND POPULATION DOSESTOMANoorooooo ~ or eooeoooooooooor ~ ooo coro ~ III ENVIRONMENTAL TECH NICA L S PECIFIC ATIONS... ~ III
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SUSQU EHANNA S ES-ER-OL site) and at Danville (about 31 miles (49.9 km) downstream. The Corps of Engineers has compiled flood stage and discharge
. information for the Susquehanna River at Wilkes-Barre (Ref. 2.4-7). These data are based on records of flood stages dating from 1991 Data for the four most severe floods of record are presented in Table 2.4-5, Historic Floods in the Vicinity of the Susquehanna SES Table 2.4-5 also includes the stages and discharges f or floods at the site and at Danville. The flood frequency characteristics of the Susquehanna as measured at Danville are illustrated in Figure 2.4-6, Flood Discharge Frequency.
The passaqe of Tropical Storm Agnes through. Pennsylvania on June 22 and 23, 1972 resulted in record flood levels in the Susquehanna River Basin Flood crests exceeded the previous record flood level .of 1936 at Wilkes-Barre by 7 5 feet (2.3 m).
At Danville, a local maximum qaqe level resulting from a 1904 ice jam was exceeded by 1.6 feet (0.5 m) . Peak discharqe at Wilkes-Barre was an estimated 345,000 cfs (9,770 m~/sec) or a unit discharge of 34.5 cubic feet per second per sguare mile (cfsm)
(0 4 m~/sec/km~) . Accumulated runoff for the drainage area above Wilkes-Barre for the period of 0000 hours0 days <br />0 hours <br />0 weeks <br />0 months <br />, June 21, 1972 through 2200 hours0.0255 days <br />0.611 hours <br />0.00364 weeks <br />8.371e-4 months <br />, June 27, 1972 totaled 4.32 inches (11.0 cm) (Ref.
2 4-13) .
2 4. 2 5 Low Plows Long term records from the USGS gaging stations at Danville and Wilkes-Barre provide the data base for the low flew f requency analyses presented in this Subsection. Long duration low flow f requency analysis has been performed by the Pennsylvania Department of Environmental Resources (DER). The resulting curves for low f3.ow durations of two to 60 months and recurrence intervals up to 100 years for Danville and Wilkes-Barre are provided in Figures 2.4-7, Low Flow Duration at Danville and 2.4-8 ~ Low Flow Duration at Wilkes-Barre, respectively.
Tables 2.4-6 and 2.4-7, Maqnitude and Frequency of Annual Low Flow of the Susquehanna River at Danville and Wilkes-Barre, Pa.
respectively, discuss the discharge for different recurrence intervals. Tables 2.4-8 and 2.4-9, Duration Table of Daily Flow of the Susquehanna River at Danville and Wilkes-Barre, Pa.
respectively indicate the river discharge (Ref. 2.4-14) .
The most extended drought period occurred in the 1960's. The lowest consecutive day flows for periods of 183 days and less have also occurred in this period The mean monthly flows at Danville and Wilkes-Barre are provided in Table 2.4-10a, Mean Monthly Drought Year Flow Sequences. Mean Daily Flows During 1964 Drought, Table 2.4-10b for these two stations are provided for the four lowest flow months of this year 2 4-5
S USQ U EH A NNA S ES-E R-OL A policy decision 'of the Susquehanna River Basin Commission reqardinq consumptive withdrawals during low flow periods provides that natural flows during droughts will not he diminished by future water users. On September 30 1976, this policy decision was implemented as an Amendment to 18 CFR Part 80'3 (Susquehanna River Basin Commission, Subpart D Standards for Review, Section 803.61, Consumptive Uses of Mater) (Ref. 2.4-15). Compensation shall be reguired for consumptive uses of water during periods of low flow. The provisions of this regulation apply to consumptive uses initiated since January 23, 1971 2 4 2 6 Sedimentation Annual sediment yields in the reqion surrounding the site are spacially uniform. Neasurements at Towanda, Pa. about 105 miles (169 km) above the station, indicate on annua'ediment yield of 150 tons/sq mi (52.5 metric tons/km~) from a drainaqe area of 7797 sq mi (20,194 km~) . Annual yields at Danville, 11,220 sq mi (29,060 km~) drainage area are estimated to be 140 tons/sq mi (49.0 metric tons/km~) (Ref 2 4-8) . Daily sediment discharges at individual stations are highly variable The daily sediment discharqe at Danville ranqes from a high of 556, 000 tons/day (504,400 metric tons/day) to a low of 18 tons/day (16.3 metric tons/day) .
Mater quality samplinq at the site included measurement of total suspended solids A range of values from 1. 6 mg/1 to 912.6 mq/1 with an averaqe value of 57. 0 mg/1 was found. These results are further reported in Subsection 2.4 3.
Grain size analysis was performed on water samples taken in 1974 usinq an automatic image analyzer. The grain size determination was performed on treated and untreated river water samples. The results for the untreated samples are reported in Table 2 4-11, Sediment Grain Size Distribution.
2 4 2 7 Mater Impoundments The Susquehanna River supplies all the water required for normal station operation. A seven-acre (2.8 ha.) spray'ond is located onsite to supply water to emergency heat dissipation systems.
The warmed water from the reactors is cooled via the pond's spray system and then recirculated throuqh the emergency cooling systems.
This spray pond has a relatively impervious liner. It is free-form in shape to conform to the natural topography of the area.
Embankments and ditches are provided to direct surface water
- 2. 4-6
SUS(}UEHANNA SES-ER-OL TABLE 2.4-2 MONTHLY AVERAGE RIVER CONDITIONS STAGE, VEIOCITY AND DISCHARGE OCTOBER STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected
~
Sunbury 420.9 420.9 2.0 1.9 9164 8631 (122.0)
Northumberland 422.9 422.8 0.8 0.7 5144 4761 (123.5)
Volverton Sta. 427.8 427.6 3.0 3.3 5122 4742 (128.5)
Danville 434. 6 434.4 1.8 1.8 5083 4707 (134.7)
Catawissa 447. 9 447.7 1.6 1.6 4939 4590 (143.7)
Bloomsburg 453.5 1.0 1.0 4749 4408 (146.6) 453.3'57.
1 Almedia 457. 4 2 1.6 1.5 4749 4408 (150.4)
Berwick 476.4 476.3 1.0 4623 4290 (159.1)
Nescopeck 477.6 477.4 1.9 1.9 4623 4290 (160.0)
Beach Haven 483.0 482.9 1.6 1.5 4623 4290 (162.4)
Mapwallopen 483.3 483.1 1.5 1.4 4623 4290 (163.9)
Plant Site 484.7 484.5 1.0 0.9 4595 4280 (165;5)
Shickshinny 487.9 487.7 2' 2.7 4570 4270 (169.5)
SUS(}UEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 422.0 421.9 3.4 3.4 21146 20613 (122.0)
Northumberland 425.0 424.9 1.0 1.0 12478 12095 (123.5)
Wolverton Sta. 429.8 429.7 2.4 2.4 12457 12077 (128.5)
Danville 437.1 437.0 2.2 2.2 12421 12045 (134.7)
Catawissa 451. 2 451. 1 1.9 1.9 12090 11741 (143.7)
Bloomsburg 456.2 456.1 1.5 1.5 11648 11307 (146.6)
Almedia 460.0 59.9 2.2 2.1 11648 11307 (150.4)
Berwick 478.9 478.8 1.6 1.5 11356 11023 (159.1)
Nescopeck 479.8 479.7 2.0 2.0 11356 11023 (160.0)
Beach Haven 484.8 484.8 2.1 2' 11356 11023 (162.4)
Wapwallopen 485.5 485.4 2.4 2.3 11356 11023 (163.9)
Plant Site 487.4 487.3 1.6 1.6 11291 10976 (165.5)
Shickshinny 490. 8 490.7 3.9 3.9 11232 10932 (169.5)
SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Pt /sec)
Present Pro ected Present Pro ected Present Pro'ected Sunbury 422.6 422.6 4.2 4.2 29842 29309 (122.0)
Northumberland 426.2 426.1 18028 17645 (123.5)
Wolverton Sta. 431.0 430.9 2.5 2.5 17958 17578 (128.5)
Danville 438.5 438.4 2.4 2.4 17835 17459 (134.7)
Catawissa 453. 2 453. 0 2.0 2.0 17348 16999 (143.7)
Bloomsburg 457.5 457.4 1.8 1.7 16698 16357 (146.6)
Almedia 461.4 461.3 2.5 2.5 16698 16357 (150.4)
Berwick 480.2 480.10 1.8 1.8 16270 15937 (159.1)
Nescopeck 481.1 481.0 2.2 2.2 16270 15937 (160.0)
Beach Haven 485 ' 485.7- 2.3 2.3 (162 ') 16270 15937 Wapwallopen 486.7 486.6 2.9 2.8 16270 15937 (163.9)
Plant Site 488.8 488.7 2.0 1.9 16175 15860 (165.5)
Shickshinny 492.4 492.3 4.2 4.2 16089 15789 (169.5)
SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro'ected Present Pro ected Present Pro ected Sunbury 422.3 422.3 3.9 3.8 25852 25319 (122.0)
Northumberland 425.6 425.6 1.0 14978 14595 (123.5)
Volverton Sta. 430.4 430.3 2.4 2.4 14908 14528 (128.5)
Danville 437.7 437.6 2.3 2.3 14785 14409 (134.7)
Catawissa 452.0 451.9 1.9 1.9 14349 14000 (143.7)
Bloomsburg 456.7 456.6 1.6 1.6 13768 13427 (146.6)
Almedia 460.6 460.5 2.3 2.3 13768 13427 (150.4)
Berwick 479.5 479.4 -l. 7 1.7 13384 13051 (159.1)
Nescopeck 480.4 480.3 2.1 2.1 13384 13051 (160.0)
Beach Haven 485.2 485.2 2.2 2.2 13384 13051 (162.4)
Mapwallopen 486.0 485.9 2.6 2.5 13384 13051 (163.9)
Plant Site 488.0 487.9 1.8 1.7 13299 12984 (165.5)
Shickshinny 491.5 491.4 4.0 4.0 13222 12922 (169.5)
)
0
SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) *
(Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 422.7 422.6 4.3 4.3 30704 30171 (122.0)
Northumberland 426.2 426.2 17576 17193 (123.5)
Wolverton Sta. 430.9 430.8 2.5 2.5 17479 17099 (128.5)
Danville 438.3 438.3 2.4 2.4 17308 16932 (134.7)
Catawissa 453.0 452.9 2.0 2.0 16920 16571 (143.7)
Bloomsburg 457.5 457.4 1.8 1.8 16685 16344 (146.6)
Almedia 461.4 461.3 2.5 2.5 16685 16344 (150.4)
Berwick 480.1 480.1 1.8 1.8 16071 15738 (159.1)
Nescopeck 481.0 481.0 2.2 2.1 16071 15738 (160.0)
Beach Haven 485.8 485.7 2.3 2.3 16071 15738 (162.4)
Wapwallopen 486.7 486.6 2.8 2.8 16071 15738 (163.9)
Plant Site 488.7 488.6 1.9 1.9 15828 15513 (165.5)
Shickshinny 492.3 492.2 4.2 4.2 15759 15459 (169.5)
SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS)HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 424.4 424.4 5.6 5.6 55420 54887 (122.0)
Northumberland 428.7 428.7 1.3 1.3 31852 31469 (123.5)
Wolverton Sta. 433.4 433.4 2.7 2.7 31732 31352 (128.5)
Danville 441.2 441.1 2.8 2.8 31521 31143 (134.7)
Catawissa 456.7 456. 7 2.4 2.4 31065 30716 (143.7)
Bloomsburg 460.4 460.3 2.3 2.3 30458 30117 (146.6)
Almedia 464.4 464.4 3.1 3.1 30458 30117 (150.4)
Berwick 482.9 482.8 2.5 2.5 30058 29725 (159.1)
Nescopeck 483.8 483.7 2.6 2.6 30058 29725 (160.0)
Beach Haven 488.0 488.0 2.9 2.9 30058 29725 (164.2)
Wapwallopen 489.4 489.4 3.8 3.8 30058 29725 (163.9)
Plant Site 491. 9 491.8 2.7 2.7 29969 29654 (165.5)
Shickshinny 495.9 495.9 5.0 5.0 29888 29588 (169.5)
SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 424.8 424.7 5.8 5.9 61354 60821 (122.0)
Northumberland 429.2 429.2 1.4 1.4 37415 37032 (123.5)
Wolverton 'Sta. 434. 2 2.8 (128 ') 434.2 2.8 37256 36876 Danville 442.1 442.0 2.9 2.9 36978 36602 (134.7)
Catawissa 457. 9 457. 8 2.5 2.5 36331 35982 (143.7)
Bloomsburg 461.3 461;2 2.5 2.5 35469 35128 (146.6)
Almedia 465.3 465.3 3.3 3-3 35469 35128 (150.4)
Berwick 483.6 483.6 2.7 2.7 34900 34567 (159.1)
Nescopeck 484.6 484.6 2.7 2.7 34900 34567 (160.0)
Beach Haven 488.7 488.7 3.0 3.0 34900 34567 (162.4)
Mapwallopen 490.2 490.2 4.1 4.1 34900 34567 (163.9)
Plant Site 492.8 492.8 2.9 2.9 34774 34459 (165.5)
Shickshinny 497.0 496.9 5.3 5.3 34659 34359 (169.5)
0 SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 423.0 423.0 4.6 4.5 35015 34882 (122.0)
Northumberland 426.7 426.7 19326 18943 (123.5)
Wolverton Sta. 431.3 431.2 2.5 2.5 19229 18849 (128.5)
Danville 438.7 438.7 2.4 2.4 19060 18684 (134.7)
Catawissa 453.5 453.4 2.0 2.0 18567 . 18218 (143.7)
Bloomsburg 457.8 457.7 1.8 1.8 17909 17568 (146.6)
Almedia 461.7 461.7 2.6 2.5 17909 17568 (150.4)
Berwick 480.4 480.4 1.9 1.9 17475 17142 (159.1)
Nescopeck 481.4 481.3 2.2 2.2 17475 17142 (160.0)
Beach Haven 486.0 486.0 2.4 2.4 17457 17142 (162.4)
Wapwallopen 487.0 486.9 3.0 2.9 17475 17142 (163.9)
Plant Site 489.1 489.0 2.0 2.0 17379 17064 (165.5)
Shickshinny 492.8 492.7 4.3 4' 17291 16991 (169.5)
SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION( ) STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro 'ected Present Pro ected S unbury 421.8 421.8 3.4 3.3 19915 19382 (122.0)
Northumberland 424.8 424;7 0.9 0.9 11108 10725 (123.5)
Wolverton Sta. 429.5 429.4 2.4 2.4 11054 10674 (128.5)
Danville 436.7 436.6 2.2 2.1 10959 10583 (134.7)
Catawissa 450.6 450.5 1.8 1.8 10638 10289 (143.7)
Bloomsburg 455.7 455.6 1.4 1.4 10211 9870 (146.6)
Almedia 459.5 459.4 2.1 2.0 10211 9870 (150.4)
Berwick 47.8. 5 478.3 1.5 1.4 9930 9597 (159.1)
Nescopeck 479.4 479.3 2.0 2.0 9930 9597 (160.0)
Beach Haven 484.5 484.5 ~
2.0 2.0 9930 9597 (162.4)
Wapwallopen 485.1 485.0 2.2 2.2 9930 9597 (163.9)
Plant Site 486.9 486.8 1.5 1.5 9868 9553 (165.5)
Shickshinny 490.3 490.2 3.8 3.7 9811 9511 (169.5)
SUS(}UEHANNA SES-ER-OI TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Zt /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 421.0 420.9 2.0 2.0 9734 9201 (122.0)
Northumberland 423.0 422.9 0.7 0.7 5277 4894 (123.5)
Volverton Sta. 427.8 427.7 3.0 3.2 5247 4867 (128.5)
Danville 434.7 434.5 1.8 1.8 5194 4818 (134.7)
Catawissa 448.0 447.8 1.6 1.6 5116 4767 (143.7)
Bloomsburg 453.6 453.4 1.0 5012 4671 (146.6)
Almedia 457.5 457.3 1.6 1.6 5012 4671 (150.4)
Berwick 476.6 476.4 4944 4611 (159.1)
Nescopeck 477.7 . 477.5 1.9 1.9 4944 4611 (160.0)
Beach Haven 483.2 483.0 1.6 1.6 4944 4611 (162.4)
Mapwallopen 483.5 483.3 1.5 1.4 4944 4611 (163.9)
Plant Site 484.9 484.7 1.0 1.0 4929 4614 (165.5)
Shickshinny 488.1 487.9 2.9 2.8 4915 4615 (169.5)
SUS(}UEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION( ) STAGE VELOCI1Y DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 420.6 420.5 1.5 1.5 6520 5987 (122.0)
Northumberland 422.2 422.1 0.7 0.6 3480 3097 (123.5)
Wolverton Sta. 427.2 427.1 4.4 4.2 3460 3080 (128.5)
Danville 433.8 433.6 1.7 1.6 3425 3049 (134.7)
Catawissa 446.7 446.2 1.7 1.9 3278 2929 (143.7)
Bloomsburg 452.5 452.2 0.9 0.9 3082 2741 (146.6)
Almedia 456.5. 456.3 1.3 1.3 3082 2741 (150.4)
Berwick 475.6 475.5 0.9 0.8, 2953 2620 (159.1)
Nescopeck 476.7 476.5 2.1 2.3 2953 2620 (160.0)
Beach Haven 482.2 482.0 1.4 1.3 2953 2620 (162.4)
Wapwallopen 482.4 482.2 1.2 2953 2620 (163.9)
Plant Site 483.6 483.4 0.8 0.7 2924 2620 (165.5)
Shickshinny 487.5 487.1 2.0 1.9 2898 2598 (169.5)
SUSQUEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
STATION STAGE VELOCITY DIS(HARGE (River Mile) (Ft. msl) (Ft/sec) (Ft /sec)
Present Pro ected Present Pro ected Present Pro ected Sunbury 42o.6 420.5 1.4 1.4 6137 5604 (122.O)
Northumberland 422.2 422.0 0.7 0.7 3582 3199 (123.5)
Wolverton Sta. 427.2 427.2 4.4 4.3 3568 3188 (128.5)
Danville 433.8 433.'6 1.7 1.7 3543 3167 (134.7)
Catawissa 446.9 446.3 1.6 1.9 3383 3034 (143.7)
Bloomsburg 452.5 452.3 0.9 0.9 3170 2829 (146.6)
Almedia 456.6 456.4 1.3 1.3 3170 2829 (150.4)
Berwick 475.7 475.5 0.9 0.8 2953 2620 (159.1)
Nescopeck 476.8 476.6 2.1 2.2 3030 2697 (160.0)
Beach Haven 482.2 482.1 1.4 1.4 3030 2697 (162.4)
Wapwallopen 482.4 482.2 1.2 3030 2697 (163.9)
Plant Site 483.7 483.5 0.8 0.7 2999 2684 (165.5)
Shickshinny 487.5 487.1 2.0 2.0 2971 2671 (169.5)
SUS(}UEHANNA SES-ER-OL TABLE 2.4-2 (Continued)
Station locations are indicated as the nearest municipality or feature to the river cross-section used in the computations. The exact cross-section location is indicated by river mile.
One Foot = 0.3048 meter One Foot Per Second = 0.3048 meter per second One Cubic Foot Per Second = 0.0283 cubic meters per second.
SUSQUEHANNA SES-ER-OL TABLE 2.4"3 MONTHLY PERCENT CHANCE OF FLOODING SUS UEHANNA RIVER UPSTREAM OF SUNBURY Month Percent Chance of Floodin Jan 6.8 Feb 7.5 Mar 40.4 Apr 19.0 May 8.2 Jun 2,1 Jul 2.1 Aug 1.4 Sept 1.0 Oct 3.4 Nov 2.7 Dec 5.4 (1) Sunbury is 43 miles (69 km) downstream of the site at the confluence of the Susquehanna River and the West Branch Susquehanna River.
SUSQUEHANNA SES-ER-OL SECTION TITLE VOLUME II I
.....
APP EN DXCIESe e s s s s e o e o s ~ e e ~ e e e e s s s s s s s s ~ s e s e s ~ s e AN EVALUATION OF THE COST OF SERVICE IMPACT OF A DELAY IN THE IN-SERVICE DATES OF SUSQUEHANNA SES (JANUARY 1 978) III B1 CURRENT LONG-RANGE FORECAST ENERGY SALES PEAK LOAD 1976-1990......................III SALES AND PEAK LOADS DECEMBER, 1976...
B2 APPLICANT' FORECASTING NETHODOLOGY KMH III NATXONMIDE FPC OR DER FU NO 496.................... III EL EilER GE NCY RES PON SE TO
~
SUSQUEHANNA RIVER MATER ANALYSES SUNHARY .. III DOS ES TO MAN....
EQUATIONS AND ASSUMPTIONS UTXLIZED IN THE CALCULATION OF INDIVIDUAL AND POPULATION ENVIRONMENTAL TECHNICAL SPECIFICATIONS- -
III III
SUSg UEHAxfH A SES ER OL Accuracy 1% fu ll scale Current full-scale deflection 1. 0 milliampere s Input impedence 1400 ohms Respon se Time 0.5 seconds
'Rriting Type Curvilinear Chart Speed 3 in/hour Channels 1 on each chart 2 charts/recorder All recording devices, translator= and the digitizer are housed in a weatherproof cinderblock building. This building has thermistatically controlled heating and air conditioning.
6..1. 3. 1. 1 5 Calibration and Yaintenance of the System All calibration and maintenance is pe formed at least semi-annually in accordance with the frequencies and producedures prescribed in the manufacturer's operating and maintenance manual.
6 1.3.1.1.6 Data Analysis The analog chart records are removed every 14 days for inspection and analysis. Each chart is removed separately and placed in individual boxes labeled with date, instrument'nd level. The charts are inspected for breaks in record, time errors, power failures and other indications of system malfunction and then stored. The information gained from this inspection is used to update and verify the digital data, and to locate anomalies with any parameter. The analog recording system provides a back-up in case of digital system failure, so that a high data recovery rate can be maintained. Table 6.1-2, Data Recovery Bates, gives the recovery rates for each year.
Digital minute data are recorded at the site on magne ic tape for analysis. At the begi.nning of each scan of data a unique identification code, the date,'hour and minute is recorded.
After 14 days of recording, the tape is removed, labeled with the data period and forwarded to the Applicant, The computer
- 6. 1-13
SUSQUEHANNA S ES-ER-OL
/
facility processes these tapes converting the recorded millivoltaqes into enqineerinq units.
An hourly averaqe for each parameter is computed Data validity, range of hourly averages and the number of valid observations contributinq to the averages are tabulated to assist in the determination of data reliability. Comparisons between the analog and diqital data are performed when the bi-weekly review of the digital data reveals qu'estionable or invalid data.
Temperature and dew point hourly averages are computed using the f ollowinq scalar equation:
B. = 1 n .
Z r.j B.. ji i=1 where:
the average hourly value for the jth variable (in physical units);
the total number of minute observations during the hour (normally 60), but that hour, data are if considered n is less than 15 f or to be missing; B.. the i" minute observation on the 3+" variable (millivolts):
the conversion factor to change the j<hvariable from millivolts into physical units.
After wind speed (WS) and wind direction (WD) are converted from millivolts they are related in the followinq manner:
If WS is invalid (999) then vice versa.
WD is marked invalid (999) and If is )setthreshold WD WS (non-calm) to 360~ (North) and WD = 0 (implying calm) then If is WD WS ( threshold (calm) set to 0o (calm) and WD ) 0 (implying non-calm) then Hourly averages are computed as scalars for wind speed. Wind direction averages are, determined as follows:
If the associated averaqe wind speed is greater than 1.34112 meters/sec, (3 mph), average WD is determined by vector analysis (where WS and WD for each minute determines a vector) .
6 1-14
S USQU EH A NNA S ES-ER-OL APPENDIX B2 APPLICANT~S PORECASTING METHODOLOGY KMH SALES AND PEAK LOADS December, 1976
INTRODUCTION The PP&L energy model forecasts KWH consumption for each of the major groups in the PP&L Service Area; i.e.,
o RESIDENTIAL o COMMERCIAL o INDUSTRIAL o RESALE, STREET LIGHTING AND RAILROAD An econometric model is developed by considering the determinants for each of the sectors. While the model, by necessity, is a simplification, it captures the crucial linkages between sector activity and KWH sales, providing the user with a structural framework for forecasting. Through the model a user can produce a forecast of sales for each of the sectors consistent with an economic outlook. More importantly the impact of alternative economic scenarios can be tested.
In arriving at a forecast, the model utilizes information and forecasts of the U.S. economy, the national energy market, the Central Eastern Pennsylvania (C.E.P.)
economy, local weather conditions and company policy. This information is obtained from a combination of existing forecasting services and reviews with PP&L Energy Consultants. The PP&L model builds from that point, measuring the impact of the national economic outlook on the C.E.P. region. This outlook is then combined with assumptions about weather conditions and company policy to produce a forecast of energy sales to each of the sectors.
MODEL STRUCTURE In developing a model of electric energy consumption for a particular region it is important to, Qrst, define the demand conditions present in that service area, and second, measure their impact on sales to each of the sectors. The PP&L energy model is developed within this two-stage process.
In the first stage of the model, demand conditions are defined, i.e.,
climatic conditions, the economic environment, energy prices, and company policy.
Weather, energy'costs and company policy are all exogenous inputs to the model.
The economic conditions are developed endogenously through an'conometric frame-work.
The second stage measures the impact of these demand conditions on each sector through a set of econometric equations, relating sales to those factors that are known to affect growth. The general flow of the model is given. in Figure I.
The service area economic model is linked to the DRI Macro Economic Model, bridging the gap between the national economy and local sales by highlighting regional characteristics (i.e., industrial mix, growth trends, demographic mix, etc.) and by including explicitly the impact of the national economy on the region. This linkage is primarily through the industrial sector. For example, the steel industry in the C.E.P. region serves a national market, therefore, their sales depend on demand conditions in the nation. In developing a model to forecast the growth of the steel industry in the region it is necessary to include national economy. As another ex-ample, the housing industry in the area is heavily dependent on local wealth and demographic mix, but a depressed steel industry would lower local wealth thereby slowing housing growth. The function of the service area model is to liter these national conditions so as to measure their impact on the local economy.
Thus, the electric use forecast is developed in the following way. A fore-cast of the national economy, developed through the DRI Macro Model, is accepted or altered to reflect PP&L.'s thinking. That forecast is fQtered through the service area economic model to determine local economic conditions. Again the user has the option of accepting the results or altering them where he deems necessary. The economics are combined with assumptions about energy prices, expected weather conditions and company policy to define the local demand conditions. Finally, this information determines the expected level of megawatthour sales. At each stage the forecaster has the ability to adjust the output of the model before going on. It allows the user the required Qexibility to make the model a useful tool.
The next three sections detail the methodology used in developing the economic and energy models.
SERVICE AREA ECONOMIC MODEL The Central Eastern Pennsylvania (C.E.P.) economic model is constructed to highlight the regional economy within the PP&L service area. It provides infor-mation about four major economic areas:
A. INDUSTRIAL SECTOR B. COMMERCIAL SECTOR C. WAGES & PERSONAL INCOME
. D . HOUS ING The general Qow of the model is depicted in Figure II. Briefly, the model unfolds as follows. The industrial sector, through employment, is linked to the national economy. The tightness of the local labor market, along with inflationary conditions, determine the level of manufacturing wages. In the next stage, employ-ment in the commercial sector and per capita personal income are simultaneously determined; their levels are dependent on industrial activity and are interdependent with each other. Finally, population of the house-owning age group is combined with local wealth and employment conditions, and national information on the Qnancial markets to determine the housing market growth. A brief description of each area follows.
0
A. INDUSTRIAL SECTOR A region's manufacturing sector, primarily its exporting industries, provide the major link between the national and regional economies. Thus, we would expect this sector to follow the national patterns given that it serves a national market. However, we expect the local industries to maintain regional characteristics as well, from the standpoint of locational decisions on the part of entering/exiting manufacturing concerns, In modelling manu-facturing employment in the PP&L service area every attempt was made to include these two aspects: linkage with the national economy and regional locational decisions .
B. COMMERCIAL SECTOR The type of services provided and products sold within the commercial sector are quite similar across different regions of the country. However, the growth of this sector within a region is heavily dependent upon the local economy. In developing the commercial employment equations, the growth in each of these sectors was compared to their breakdowns nationally.
The relative growth of the area was then compared to the relative U.S.
growth in population and per capita income.
C. WAGES, PRICES, AND PERSONAL INCOME Manufacturing average hourly earnings and total personal income for the service area are forecasted within the C.E.P. economic model, building from the employment situation in the manufacturing and commercial sectors. In addition, a forecast of local inflation conditions is developed directly from the inflation conditions of the nation.
D. HOUSING SECTOR The growth in the housing stock is an important determinant of residential sales. Therefore, in the economic model, we explictly model household formations in the service area. In developing this sector we have a choice of two available data series, household permits from the federal government and new dwelling unit statistics from PPaL records.
The dwelling unit data, considered more reliable and easier to monitor, was used. The long-run demand for housing is hypothesized to be of the population of the house-owning age group and the level a'unction of household wealth.
IV. RESIDENTIAL SECTOR The residential model has been developed to forecast sales to the two major classes of residential service; viz., electrically heated homes and general residential.
In doing so the model is divided into two blocks:
o CUSTOMER BLOCK o USAGE BLOCK
In the customer block the number. of residential customers under each of the services is determined. The'usage block determines the average kWh usage per customer under each of the, services. Total residential usage is obtained by summing the product of customer stock and per customer usage in each of the groups.
Customer Block Total residential customers in any period is equal to the number in the previous period, plus the new units coming on, less the depreciation of the existing stock.
In estimating our usage per customer equations, the impact of the following determinants was measured.
o HOUSEHOLD INCOME o PRICE OF ELECTRICITY o PEOPLE PER HOUSEHOLD o WEATHER CONDITIONS o CONSERVATION EFFORTS Figure III shows the relationships that are considered in modelling this sector.
V. COMMERCIAL SECTOR The Commercial Sector presents a difficult modelling task to the forecaster.
Over the past 10 to 15 years it has shown steady growth, becoming an increasing portion of total sales; yet little information is available over this interval on the type of load served. The best that can be done in lieu of commercial surveys and expanded data collection is to include in, the model those economic indicators that best depict the growth of these customers. The modelling task is not one of obtaining satisfactory summary statistics, they come rather easily, but rather to be sure that the real determinants and indicators of growth are in the'model. In addition to the overall growth, the price of electricity, weather conditions and conservation are considered important determinants of sales and are included.
Sales to the commercial sector are collected by four major classes:
o Wholesale and Retail Trade o Financial and Personal Services o Other Commercial o Small Commercial The general outline of estimating this sector is exhibited in Figure IV.
INDUSTRIAL SECTOR The Industrial Model was developed to forecast KWH sales to fourteen major industrial classes. The sales forecast for the industrial sector is'the aggregation of these fourteen cia'sses, the breakdown of which is given in the table below and the flow diagram. shown as Exhibit V.
INDUSTRIAL SECTOR BREAKDOWN OF INDUSTRIAL CLASSES SIC INDUSTRY 20 Food & Kindred Products 22 Textile Mill Products 23 Apparel 27 Printing & Publishing 28 Chemicals & Allied Products 324 Cement 33 (less 331) Primary Metals (except Steel) 331 Steel Manufacturing 35 Non-Electrical Machinery 36 Electrical Riachinery Other Metal Products>
Other General Industry2 S mall Industrial 11512 Coal Mining Note: l. Includes Mining (SIC 10), Ordnance (SIC 19), Fabricated Metals (SIC 34), Transportation Equipment (SIC 37) and Instruments (SIC 38).
- 2. Includes Oil a Gas Extractions (SIC 13), Mining a Quarrying (SIC 14),
Tobacco Products (SIC 21), Lumber and Wood Products (SIC 24),
Furniture and Fixtures (SIC 25), Paper and Allied Products (SIC 26),
Petroleum Refining (SIC 29), Rubber and Plastics (SIC 30), Leather and Products (SIC 31), Stone, Clay and Glass, less Cement (SIC 32.
less 324), Miscellaneous Industries (SIC 39).
In the industrial model, four major factors were considered:
o Production Activity o Factor Substitution o Technological Change o Conservation
VII.
SUMMARY
In summary, the PP5L Econometric Model forecasts th'e short and long-term kWh consumption for the major consuming sectors, in the. PPaL Service Area. It utilizes forecasts from the DRI Macro Model together with regional economic, demo-graphic, and climatic conditions-to determine a scenario of the service area economy.
Assumptions about the price of electricity and competing fuels, weather expectations, the working age population, and technological changes are then made, from which point the model produces a kWh forecast. Outlooks are also prepared by PPaL Energy Consultants.
In the residential sector, the model forecasts kWh sales for Electrically Heated Homes and General Residential Service. For both of these classes, sales is the product of the number of customers and usage per .customer. The number of customers is a function of employment in the service area, real disposable income, new dwelling units, the price of electricity and competing fuels, and new mortgage commitments. Usage per customer is determined, for the most part, by the real price of electricity, real disposable income, and weather. The model, which develops the mathematical relationships among these variables, then fore-casts residential sales.
Commercial sales, which are segregated into four categories, are a function of commercial employment in the service area, real disposable income, the real price of electricity, and weather. The growth in commercial demand is positively related to the first two variables while negatively related to the third. By solving a series of equations, the model'determines the kWh consumption in the commercial sector.
The forecast of industrial sales makes use of estimates of industrial output, which in turn is a function of manufacturing employment. Manufacturing employment, by two-digit SIC Codes in the PPaL service area depends upon current levels of employment and the level of production for a particular industry. Industrial output in our service area is defined as the Federal Reserve Board Production Index times the ratio of PPSL employment to U S. employment. Sales to the fourteen industrial
~
SIC groups are mainly determined by finding the relationship of kWh sales to service area industrial output, the relative price of electricity to fuel oil, the relative price of electricity to natural gas, and any technological changes that might occur.
No model, regardless of how well it is specified, will forecast perfectly.
There will be exogenous events, such as large new loads or changes in company or government policy, that the model is unable to pick up. In these cases, the results of the model can be modified to the desired level by judgment.
VIII. PEAK LOAD FORECAST In order to adequately provide for our customers'emands for electricity, adequate generating capacity must be available. The amount of capacity required is determined by forecasting summer and winter system peak loads for ten to fifteen years into the future. Summer and winter system peaks are forecasted because it is during these periods that the greatest demands are made on our system. Air conditioning load causes the summer peaks, and lighting and space heating loads are responsible for those in the winter.
PPEL's estimating procedure produces summer and winter system peak loads by developing the contribution made by each rate class. The term "rate class" means all customers served under similar rate schedules. The sale of energy forecast developed by revenue classes is reallocated to rate classes using observed historical relationships. Load study data is then brought into the estimation process. Our load studies are designed to determine the load characteristics of a specific class of service. When meters are of the watt-hour type, stratified random samples of customers within kilowatt-hour ranges are used. In the case of most general service sample customers (up to 7000 kw with demand meter billing), load factor ranges within each rate class are used.
Larger commercial and industrial customers are studied individually. Daily load curves for the days of summer and winter system peak are derived for every stratum of each rate class for an average customer.
For customers studied by kilowatt-hour ranges, demand per customer data for each stratum of each rate class are multiplied by the number of customers in the universe of that stratum to obtain a universe daily load curve. The number of customers in a stratum is obtained from the Company's bill frequency distributions. This can also be done for years other than the load study test year because the load characteristics of a kWh or load factor stratum remain fairly constant with only the number of customers in a stratum changing from year to year.
Daily load curves for load factor stratum are stated as ratios of customer monthly maximum demand. These are applied to the sum of customer demands in each load factor stratum as determined from an hours-use distribution to obtain the universe daily load curve.
For an historical year the strata of a given rate class are added together to form the daily load curve for the universe of that rate class. The rate class load curves for the days of summer and winter system peak of a given year are corrected for losses to the net generation level and added together to form the summer and winter load curves for the system. The result is checked against actual peak loads.
Using these techniques we have developed rate class contributions to summer and winter system peaks historically for selected hours of the day. The ratio between class contribution to system peak and annual sales to that class is calculated for each rate class at the time of summer and winter system peak, for every historical period analyzed. The trend of this ratio for either a summer or a winter syst'm peak is fairly constant over time. For a given class the trend of this ratio for the time of both summer and winter system peak is projected through time. By applying the appropriate ratios to the predicted annual sales of any future year, that class'ontribution to summer and winter peak is forecasted. The system peak for a specific time period is obtained by adding together the projected class contributions to system peak.
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SUSQU EH ANNA SES-ER-OL contaminated by airborne radioiodine is a potential source of exposure. Samples from milk animals are considered a better indicator of radioiodine in the environment than vegetation.
the census reveals milk animals are not present or are If unavailable for sampling, then veqetation may be sampled.
The 500-sq. ft. garden, considering 20% used for growing green leafy vegetables and a vegetation yield of 2 kg/m> will produce the 26 kq/yr assumed in ~Re ula~tor Guide 1.109 (March 1974) for child consumption of leaf y veqetation. The option to consider the garden to be at the nearest residence is conservative and those locations may be used to calculate doses due to radioactive ef fluent releases in place of the actual locations which would be determined by the census.
The permission of deviations from the sampling schedule is based on the recognition of unavoidable practical difficulties which in the absence of the permitted deviations would result in violation of the specifications.
The requirement for the participation in the EPA cross-check program, or similar program, is based on the need for independent checks on the precision and accuracy of the measurements of radioactive material in environmental monitorinq in order to demonstrate the validity of the results.
~Re oeting Requirement A. Annual Environmental Operatinq Report, Part B, Radiological.
A report on the radiological environmental sur veillance proqram for the previous calender year shall be submitted to the Director of the NRC Regional Office (with a copy to the Director, Office of Nuclear Reactor Requlation) as a separate document in May of each year.
The period of the first report shall begin with the date of initial criticality The report shall include a
. summary (format of Table F-1, Environmental Monitoring Program Summary) interpretations, and statistical evaluation of the results of the radiological environmental surveillance activities for the report period, includinq a comparison with operational, controls, preoperational studies (as appropriate) and previous environmental surveillance reports and an assessment of the observed impacts of the station operation on the environment.
In the event that some results are not available the report shall be submitted noting and explaining the reasons for the missinq results. The missing data shall be submitted as soon as possible in a supplementary report.
S US QU EHA N NA S ES-ER-OL The reports shall include either explicitly or by reference to other documentation, the following: a summary description of the radiological environmental monitorinq program including sampling methods for each sample type, size and physical characteristics of each sample type, sample preparation methods analytical methods, and measurinq equipment used; a map of all sampling locations..the results of the land use censuses and the results of the Applicant's participation in the Environmental Protection Agency's Environmental Radioactivity Laboratory Intercomparisons Studies (Crosscheck) Proqram.
B. Non-routine Radiological Fnvironmental Operatinq Reports If a confirmed measured radionuclide concentration in an environmental sampling medium averaged over any quarter samplinq period exceeds the reporting level given in Table F-4, Reportinq Levels. for Non-Routine Operation, a written report shall be submitted to the Director of the NBC Regional Office (with a copy to the Director, Office of Nuclear Reactor Regulation) within 30 days from the end of the quarter. A confirmatory reanalysis of the original, a duplicate or a new sample may be desirable, as appropriate. The results of the confirmatory analysis shall be completed at the earliest time consistent with the analysis, but in any case within 30 days except in the case of the strontium analysis. Zf it can be demonstrated that the level is not a result of station effluents (i.e., by comparison with control station or preoperational data) a report need not be submitted, but shall be discussed in the annual report.
If radionuclides other than those in Table -F-4 are detected and are due from station effluents, a reporting level is exceeded if the potential annual dose to an individual is equal to or greater than the design objective doses of 10 CFR Part 50, Appendix I This report shall include an evaluation of a release conditions, environmental factors or other aspects necessary to explain the anomalous result
SUSQUEHANNA SFS-ER OL TABLE F-2 SUSQUEHANNA SES OPERATIONAL RADIOLOGICAL ENVIRONMENTAL HONITORING PROGRAH
++ Collection Analyti cal Sam~le T pe Location F~PB UMC * ~Anal sis F~re Cene
- Uni ts Air Particulates SS-AP-551 North of I.A.
SS-AP-1152 SW corner of si te SS-AP-9A1 Near Transmission Field Gross Beta W pCi/m3 SS-AP-12E1 Berwi ck Hospital Galena Emitters QC pCi/m SS-AP-7Hl PPEtL Roof Air Iodine SS-AI-551 North of I.A.
SS-AI-1152 SW corner of site SS-AI-9A1 Near Transmission Field I-131 pCi/m SS-AI-12El Berwick Hospital SS-AI-7H1 PPSL Roof Surface Water SS-SW-5S2 At I.A. Gama Emitters pCi/1 SS-SW-12F1 Berwick Bridge H-3 pCi/1 Dr~inkin Water SS- PWT-12F2 Berwick Water Co.
(treated) Gross Beta H pCi/1 SS-PWT-12H2 Danvi lie Water Co. Gamma Emi tters pCi/1 (treated) H-3 QC pCi/1 Fish**
SS-AQF-6AI Outfall SA Gama Emi tters SA pCi/g(wet)
SS-AQF-2G1 Upstream Sediment SS-AQS-llcl Hess Is, area SA Gamma Emi tters SA PCi/9(dry)
Milk SS-H-5B1 Farm SS-M-12B1 Schultz Farm 2/H I-131 2/H pCi/1 SS-H-1282 Young Farm Gama Emitters 2/H pCi/1 SS-H-7H2 Crytal Springs Dairy
SUSQUEHANNA SES-ER-OL TABLE F-2 (cont.)
SUSQUEHANNA SES OPERATIONAL RADIOLOGICAL ENVIRONMENTAL HONITORING PROGRAH
++ Collection Analytical
~Sam le T e Location ~Fee mene * ~Anal sis ~Fre mene
- Uni ts Food Products SS-FP-5B1 Farm Ganma Emitters pCi/g(wet)
Direct Radiation SS-ID-3S2 Susquehanna River SS- I D-4S1 Susquehanna River SS- ID-551 North of I.A.
SS-ID-7S1 On 230 KV Tower Gama Dose mrem/std.mo SS-ID-11S2 On 230 KV Tower SS- I D-9AI Near Transmission Field SS- ID-12E1 Berwick Hospital SS- ID-7HI PP&L Roof Frequency Codes:
= Weekly; H = Monthly; = Quarterly; SA = Semi-Annual; A = Annual; 2/H = twice each month; C W Q Composite.
Important classes of fish will be analyzed separately. (bottom feeders and game fish)
Hilk collected and analyzed semi-monthly from April through October - monthly during other months.
Shown in Figure.
SUSQUEHANNA SES ER OL TABLE F-3 DETECTION CAPABILITIES FOR ENVIRONMENTAL SAMPLE ANALYSIS MINIMUM DETECTABLE LEVEL (MDL)
Airborne Particulate Water or Gas Fish Mi 1 k Food Products Sediment Analys is (pCi/l) (pCi/m') (pCi/kg-wet) (pCi/1) (pCi/kg-wet) (pCi/kg-dry)
Gross Beta 7 x 10 H-3 200 Mn-54 10 85 Fe-59 20 170 Co-58,60 10 85 Zn-65 20 170 ZrNb-95 I-131 0.3 5x10 5c 16c,d Cs-134,137 10 5 x 10 85 10 50 100 BaLa-140 10 10
SUSQUEHANNA SES-ER-OL Table F-3 (Cont'd) ~
Acceptable detection capabilities for thermoluminescent dosimeters used for environmental measurements are given in Regulatory Guide 4.13, July 1977.
Indicates acceptable detection capabilities for radioactive materials in environmental samples. These detection capabilities are tabulated in terms of the minimum detectable level (MDLs). The NOL is defined, for purposes of this Table, as that concentration of radioactive material in a sample that will yield a net count which is different from the background count by three times the standard deviation after background count.
For a particular measurement system (which may include radiochemical separation):
3 00 Sb MDL =
E x V x 2.22 x Y x exp(-X t)
- where, NOL is minimum detectable level as defined above (as pCi per unit mass or volume)
Sb is the standard deviation of the background counting rate or of the counting rate of a blank sample as appropriate (as counts per minute)
E is the counting efficiency (as counts per disintegration)
V is the sample size (in units of mass of volume) 2e22 is the number of disintegrations per minute per picocurie Y is the fractional radiochemical yield (when applicable) is the radioactive decay constant for the particular radionuclide is the elapsed time between sample collection and counting The value of S used in the calculation of the NOL for a particular measurement system should e based on the actual observed variance of the background counting rate or of the counting rate of the blank samples (as appropriate) rather than on an unverified theoretically predicated variance. In calculating the MDL for a radionuclide determined by gama-ray spectrometry, the background should include the typical contributions of other radionuclides normally present in the sample (e.q., potassium-40 in milk samples). Typical values of E,V,Y, and t should be used in the calculation.
It should be recognized that the NOL is defined as ~a riori (before the fact) limit representing the capability of a measurement system and not as f t f ill i f p
SUSQUEHANNA SES-ER-OL Table F-3 (Cont'd)
- c. MDLs for I-131 in water, milk and other food products correspond to one-quarter of the Appendix I ( 10 CFR Part 50) design objective dose-equivalent of 15 mrem/
year using the assumptions given in Regulatory Guide 1. 109 except for an infant consuming 330 1/yr of drink water.
- d. MDL for leafy vegetables.