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1、appendix c matrix of assumptions made for 2022 common case draftdraftappendix cmatrix of assumptions 2011 teppc study programthe assumptions used to develop the study data for the 2022 horizon of the 2011 teppc study program are documented in this matrix. the starting case for all subsequent 2022 st

2、udy cases is the teppc common case (portfolio case #1 or pc1) simulating conditions for the year 2022. all of the other 2022 study cases were derived from the common case. data groupingkey assumptionsload forecast· the 2011 l&r load forecast for 2021 was extrapolated to 2022 and was then re

3、duced to reflect incremental energy efficiency savings from federal appliance and lighting standards determined to not be fully reflected in the l&r load forecasts. these resulting forecasts for both peak demand and energy are used in conjunction with 2005 historical load shapes in deriving the

4、2022 load shapes for all the load areas in the teppc topology. the load forecast for each bubble in the teppc topology is distributed to the bus level using the wecc 2020 hs1a power flow case. · california loads and mapping to buses are adjusted to capture the unique characteristics of pumping

5、plants in california. · transmission losses are included in the load forecasts and wecc does not have information to separate losses from the load data. load forecast(continued) network representation and topologynetwork representation: the teppc 2022 common case transmission network is compris

6、ed of two main components: the 2020 hs1a powerflow and the scg 2022 common case transmission assumptions (ccta). existing as well as future transmission additions are brought into the model via the wecc approved 2020 hs1a powerflow. the powerflow is supplemented by the scgs 2022 ccta, which represen

7、t a list of regionally significant transmission projects that have a high probability of being in service by 2022. regionally significant transmission in the 2020 hs1a powerflow that is not on the ccta list is removed from the common case. project on the 2022 ccta that are not in the 2020 hs1a power

8、flow are added to the case. the scg 2022 ccta report, which explains the purpose, process, and projects on the ccta can be found at the following location: link. the projects included in the 2022 ccta are shown in this map: link. in some instances incremental generation added to the case had specifi

9、c integration transmission associated with it. in cases where this detail was provided (mainly in the caiso), the transmission needed for integration was added to the case. topology: the area topology approximately matches the balancing authorities, with exceptions to accommodate variations in load

10、types and shapes as follows: - the caiso is split into pg&e bay, pg&e valley, sce, and sdge bubbles.- the idaho power footprint is divided into three bubbles (far east, magic valley, and treas valley).- the pacificorp east load is divided by state with loads in idaho, utah, and wyoming.with

11、these changes, the 2020 topology includes a total of 39 bubbles.see attachment 1 for the 2022 common case topology diagram transmission path ratings & nomograms· the tas swg started with the 2011 wecc path rating catalog and applied modifications to capture operating limits for a number of

12、key paths and to capture rating changes due to the transmission additions part of the common case transmission assumptions.see attachment 2 for a map of major paths, attachment 3 for a list of path ratings used in 2022 pc1 common case, and attachment 4 for notes on adjustments made to ratings compar

13、ed to the 2011 path rating catalog. transmission forced outagestransmission forced outages are not modeled in this study. reason: transmission maintenance outages typically occur during off peak usage only which has a low impact on production costs and forced transmission outages occur infrequently.

14、 regional hurdle ratesregional transfer rates are included in the 2022 study. the rates listed below were carried over from the 2020 studies. these transfer “tariffs” are designed to act as “hurdle” rates for region to region transfers and model the economic realities of costs for transporting power

15、 over long distances. the objective is to improve the economic dispatch and resulting power flows across the 8 pools modeled in the promod production simulations. the values were derived from the assumed wheeling rates applied in the ssg-wi 2008 base case and are fully incorporated into promods econ

16、omic dispatch algorithm. the alberta rates are designed to model the aeso market and the position within the aeso supply stack in which imports fall.transmission lossessince the load forecasts already include transmission losses, the ac losses are considered in the dispatch, but not realized as addi

17、tional load. in the current version of promod the dc losses do produce additional load, which appears to penalize flows on the dc lines. abb ventyx continues to work to improve the loss modeling within promod. in the meantime, nomograms are employed where applicable to force flows onto the ipp dc li

18、ne and other dc expansion projects.reservesthe reserve assumptions used for the 2010 teppc 2020 studies have reserves modeled at the sub-regional level. the data includes 4% reserves (3% for spinning and 1% for contingency) for each of the 8 regions, which approximates 50% of the wecc reserve requir

19、ement (after forced outages) 7% for thermal and 5% for hydro. reserves are enforced during the dispatch phase of the model and units may be backed down at times to meet reserves.flexibility reservesin addition to the load-based reserve requirement, a new reserve component was implemented to model th

20、e balancing needed for variable generation. the flexibility reserve is calculated using ten minute wind and solar data, and then aggregated to an hourly addition to the operating reserve discussed above.generating resourcesthe generating resources are assigned to bubbles and buses based on the previ

21、ous teppc datasets and updates from various sources. the category assignments are used to group the generators by similar operating characteristics, which are mainly derived from public sources.existing resources· in accordance with the l&r definitions, existing resources are resources assu

22、med to be online by 12/31/2010. these resources were identified through the ssg-wi 2005, wecc l&r, wecc power flow case, eia, utility irps, and other databases. stakeholder comments are included to the extent possible. generating resource capacities are based on the power flow case or other sour

23、ces. thermal unit capacities are net of station service. incremental resources · incremental resource data collected for the l&r process was used to update the resources included in the 2022 pc1 dataset. in some cases supplemental data from utility irps was used to bridge the gap in meeting

24、 2022 policy goals or planning margins.· incremental resources are resources expected to be placed in service between 2011 and the 2022 (inclusive).rps-gap resources· the tas swg determined the required rps generation for each state to meet the respective state rps requirements in 2022. wh

25、ere there were insufficient renewable resources specified from the existing and incremental generation, generic rps resources were added based on input obtained from utility irps, feedback collected from utility resource planners, and by using the wrez peer analysis tool.see attachment 5 for a resou

26、rce summary by fuel type and area showing capacity net of station service. thermal unit operational information· thermal unit commitment is modeled in the study. · data requirements for unit commitment include capacity information, planned and forced outage assumptions, heat rate curves, r

27、amp rates, minimum up/down times, start-up costs, and non-fuel variable o&m costs.· thermal units are broken into categories on the basis of fuel type, technology type, vintage, and capacities. a set of assumptions is developed for each generator category, with more detailed data included f

28、or gas-fired units. · there was an effort to improve and validate the thermal data during 2011, adding to the improvements made earlier. the primary focus was on unit heat rates, capacities, startup costs, variable o&m rates, and capacity factors. data updates included:- data review by the

29、data work group.- data changes from submittals and observations by teppc stakeholders, including implementation of the cycling cost data provided by intertek/aptech.· the updated data is a mixture of unit and category level data designed to avoid the use of confidential data. see attachment 6 f

30、or average thermal unit characteristics by category.thermal forced and scheduled outages· sources such as the nerc gads database were used to develop forced and planned maintenance outages rates.· the forced outage rates are used by the simulation model to force units off using monte carlo

31、 or other probabilistic techniques.· the scheduled maintenance requirements (annual hours) were used to derive scheduled maintenance outages for each wecc sub-region. the referenced data averaged by generator category is listed in attachment 6. thermal heat rates· the unit heat rates are d

32、erived from public data sources unless the generator owners/operators have provided actual heat rate data for use in the teppc public database. generic heat rates are applied to units where data is not available. · the non combined cycle heat rates used in the teppc database were derived from t

33、he heat rates in the ssgwi dataset and the heat rates provided by newenergy associates (newenergy used epa cems data to construct the heat rate curves). both sets of heat rate curves were used because both heat rate datasets were deficient in one way or another. the teppc incremental heat rates for

34、some units were abnormally low and the ssgwi average heat rates at the minimum capacity for some units were unusually small. since the deficiencies in the datasets did not overlap, by combining the two heat rate datasets the deficiencies in both datasets were fixed, resulting in a composite dataset

35、that is much better than either original dataset alone.· the plant level heat rates for the combined cycle (cc) plants were updated using the operations data from the cems databases. where insufficient data was available generic rates were used. cc plants are currently modeled at an aggregated

36、level (i.e. steam generator aggregated with associated combustion turbine generator (s). this is a common practice where specific unit level heat rate data is not publicly available. the steam generators in combined cycle plants use the exhaust heat from one or more combustion turbines to produce st

37、eam that then turns the turbine generator. since the cems data is only provided for units that produce emissions, the heat rate data for combined cycle plants is more difficult to derive. also the multiple operating configurations make cc plants more difficult to model as the heat rate changes with

38、each configuration. the reference data averaged by generator category is listed in attachment 6.thermal start-up costs, minimum up/down time,ramp-rates· start-up costs are based on irp and expert input, and include fuel, o&m and other costs to reach point of synchronization. the minimum up

39、and down times are based on average values reported by a few ssg-wi participants with subsequent updates from new energy and the modeling work group. the reference data averaged by generator category is listed in attachment 6.fuel pricesgas prices:· the base or median henry hub price was $5.43/

40、mmbtu (2012 dollars). the recommended adjustments for low and high sensitivities are -25% for low and +75% for high gas prices.· the nw power and conservation councils methodology (sixth power plan) for using implicit basis differentials to derive the area burner tip prices was used to develop

41、the area prices provided in attachment 7. the differentials are implicit in that the council forecasts burner tip prices for various areas of wecc using econometric equations and the differentials adopted by the dwg were backed out by comparing the burner tip prices with the major trading hub foreca

42、sts. the teppc burner tip prices will differ from those used in the sixth power plan because the dwg uses a different origination price (henry hub) than that used for the sixth power plan.· for intra california transportation rates, the teppc values are consistent with the cec values using util

43、ity tariffs.coal prices:· the coal price forecast was also derived from the nw power and conservation councils locational coal price forecast.other fuels:· the prices for other fuels are based on assumptions used by the california energy commission and the nwpcc.see attachments 7 and 8 for

44、 gas prices and the transportation costs. see attachment 9 for coal and other fuels prices. hydro generation· the following sources of hydro data are used for the study:- nw federal, mid-c nonfederal, other nw nonfederal, and pacificorp: the proportional load following (plf)/ hydrothermal co-op

45、timization (htc) modeling methods were used to model the majority of nw hydro generation in the 2022 pc1 dataset. plf constants were obtained by regressing historical data and loads for federal projects, or were supplied by plant operators for nonfederal projects. for grand coulee, the dalles, chief

46、 joseph, and john day, average k values were calculated using data years 1999, 2001-2003, and 2005-2010. monthly average generation values for both htc and plf plants came from the eia 906 920 data for 2005. smaller plants were modeled using estimated plf constants and eia 906 920 generation values.

47、 plants determined to not follow load historically were modeled using historical hourly shapes. these included bonneville, mcnary, the lower snake river, and federal storage plants. historical data came from the corps of engineers northwestern division website and is for the same year as the eia dat

48、a. wanapum, priest rapids, and rock island data came from the bpa pi system with concurrence from grant county and chelan county puds. for yale and merwin 2002 data day-shifted to 2005 is used. for swift, 2003 data is used due to outages in 2002. plants with nameplate capacities of less than 10 mw w

49、ere rolled up into state “plants” with summed monthly eia averages; these state “plants” were modeled using plf k=0 (flat monthly generation).- california: the california hydro data is from the caiso pi dataset which was aggregated to the river system. “historical” individual plant data was then dis

50、aggregated proportionally to eia 906/920 monthly generation values. a combination of historical shapes, plf, and htc were used to model california hydro generation. for the few plants not in caisos pi system, plf and eia 906 920 data for 2005 were used. california small hydro was disaggregated from

51、the conventional hydro to more accurately track its contribution to rps requirements. plants with nameplate capacities of less than 10 mw were rolled up into operating area “plants” with summed monthly eia averages; these area “plants” were modeled using plf k=0 (flat monthly generation), and contri

52、buted towards rps.- east: wapa plants were modeled using 2005 historical hourly hydro data except hoover, blue mesa, and yellowtail which used plf/htc. nonfederal plants were modeled using plf based on eia 906 920 data for 2005. plants with nameplate capacities of less than 10 mw were rolled up into

53、 state “plants” with summed monthly eia averages; these state “plants” were modeled using plf k=0 (flat monthly generation).- canada: bc hydro generation data are determined by bc hydros gom model using a 2022 load forecast and average inflows (1968 water conditions). the analysis included revelstok

54、e 5 and mica units 5 and 6. gom results were used to calculate plf constants for use by the htc modeling method for the g.m.shrum, peace canyon, site c, revelstoke, mica, and small plants. the arrow plant and ipp rollup are modeled with no flexibility (plf, k=0). alberta electric system operator pro

55、vided 2005 data that was used as historical data for some plants and to calculate plf/htc constants for bighorn, bow river aggregate, and brazeau plants. the historical monthly averages were used as the model energy inputs for the plf/htc plants.renewable generation· hourly wind shapes used to

56、model all wind resources were supplied by 3tier and the national renewable energy laboratory (nrel) as part of the western wind dataset. profiles used for the 2022 dataset reflect the recent three day seams issue fix applied to the western wind dataset. the wind shapes used in the teppc dataset were

57、 derived using historical weather from 2005. wind is treated as a fixed input to the model.· geothermal plants are modeled as base load plants as confirmed by clean and diversified energy initiatives geothermal task force. data to model specific plants in ca is provided by caiso.· the incr

58、emental renewable resources from previous studies were replaced by data provided by the tas studies work group.· solar production profiles (from 2005) were generated by nrel as part of the profiles that were created for the western wind and solar integration phase ii study. · both solar an

59、d wind shapes used in the teppc datasets consist of aggregates of the nrel profiles. a number of nrel profiles needed to represent the approximate capacity of wind/solar in a given geographic vicinity are aggregated to produce a single aggregate teppc wind profile. all plants within the same geographic vicinity, then, are applie