Anchor Data Set (ADS) 10 Year Database Loads and Load Modifiers 2 _ PCM 2028 ADS Case Jamie Austin, PacifiCorp Production Cost Modeling Data Work Group (PDWG)- Chair November 14, 2017

Overview Development of Loads and Load Modifiers assumptions and data – PCM ADS 2028 The California Energy Commission (CEC) involvement in producing the California Demand Forecast Determine how much DG (BTM-PV) to model in the 2028 ADS PCM? Process Review to determine the hourly Demand Response

Development of Loads and Load Modifiers assumptions and data – PCM ADS 2028

Process Start with the monthly peak and energy forecast, submitted to LAR in March, 2017 – Exception: For California use the CEC 2018 Adapted Load Forecast (consistent with the March 2018 submittal; however to be ready early December 2017). Determine if LBNL is going to adjusting EE assumptions in the forecast? Develop assumptions for Load Modifiers and work with relevant parties to agree on what should be included and where? (e.g., distribution of Area EE, AEE, DR to the bus; how and where to model BTM-PV_ associated NREL shapes?) Confirm a methodology for extrapolating the 2027 load forecast to develop the 2028 forecast The staff to build the 2028 hourly loads, using FERC Form 714 hourly profiles from year 2009. PDWG to validate the Hourly forecast, checking load shape distortion (Compression/Expansion) Work with CAISO to back out California Pumping Loads; model pumps as stand alone

Energy Efficiencies Energy Efficiency (EE) – Last year, LBNL helped with vetting efficiency assumptions and more significantly, BTM-PV assumptions embedded in the load forecast. Regarding EE, Galen from LBNL found IPC to be an exception. IPC develops their load forecast through an econometric regression that implicitly captures some future energy efficiency program activity, but not all of it. Working with IPC, Galen submitted a multiplier to account for the balance. Given that the rest of the BAs were in compliance, Galen thinks if we work with the CEC on determining what should be modeled for Additional Energy Efficiencies (AEE) in California, the rest of EEs will be netted from loads.

BTM-PV Relative to models, the ideal would be to have consistent data, assumptions and representation in both power flow and in the production cost model to facilitate implementation of the round trip. Relative to data, the challenge is twofold: What estimates to use for distributed generation? Where to place them? It was resolved in past DWG discussions that DG should be represented more explicitly to allow for proper load scaling, and to allow planners the ability to account for existing and emerging performance standards applicable to DG. Issue: if DG is modeled on the supply side, this may lead to over stating the reserves beyond what is required.

Data Limitation There are three major reasons why we cannot map DG to the customer bus: It is reported that ISO planning studies may include DG mapping to customer level. The CEC nets DG from the local load in Plexos when determining demand and supply assumptions that feeds into NAMgas--the model that produces the gas price forecast. The States keep track of DG customers by state and zip code. PDWG cannot possibly use the states’ data to map DG resources to the bus in the 2028 ADS PCM dataset without involving major players (e.g., CASIO, CEC, and other Regions).

2028 ADS PCM Distributed Generation Model DG as explicit generators, one per BA, using the generator distribution factor to map to busses. DG distribution was prorated, based on the largest load busses in the BA such that DG load does not exceed 50% of bus loads. To quantify how much DG (BTM-PV) to model: For the 2026 Common Case – California DG, we’d used assumptions developed by the CEC in their 2016 Load forecast. For the 2028 ADS PCM case – California DG, it is proposed to update with assumptions from the CEC’s adopted 2018 Load forecast. For other BAAs: Start with the E3 2016 estimate for DG, however, PDWG should work with the regions to validate the forecast and take to DS for final approval.

Behind-the-Meter Rooftop DG PV Projections in the Western Interconnection March 22, 2016 Zach Ming, Consultant Nick Schlag, Managing Consultant Arne Olson, Partner

E3’s Market Driven DG Model

Previous Results Last Week’s Results E3 presented draft results Concerns from DWG about high penetration in some states Questions about methodology taking into account certain factors Concerns about implications for percentage of households adopting 12,218 MW CEC IEPR

Changes this Week E3 implemented two changes to the model based on DGW feedback – both result a lower DG forecast Removal of ‘Green Premium’ of $0.01/kWh The green premium was a relic from the previous TEPPC case used to represent the preference a customer might have toward solar PV Lower Technical Potential NREL released a new study with updated technical potential values for every state These values do not take into account home/building ownership or limitations for customers sizing to their own load so E3 applied a 67% factor for home/building ownership 80% factor for customer sizing mismatch GW NREL source: http://www.nrel.gov/docs/fy12osti/51946.pdf

Updated Results Updated results reflect the two changes to the model and assumptions 12,218 MW CEC IEPR

Review and compare with EIA 861-2016 Kevin Harris PDWG Vice Chair

EIA 861 vs E3 EIA - 861 E3 Actual BTM-PV Ratio 2010 2011 2012 2013   Actual BTM-PV E3 Ratio 2010 2011 2012 2013 2014 2015 2016 Act 2016/ Modeled Net BTM PV AZ 121 127 253 448 602 760 887 2,129 42% CA 791 1,129 1,537 2,041 2,792 3,873 5,239 14,061 37% CO 53 130 166 205 258 304 337 835 40% ID 2 3 4 6 9 33 27% MT 8 7 29% NM 20 27 38 62 76 88 111 309 36% NV 28 42 45 59 157 210 91 231% OR 23 31 43 56 71 87 112 177 63% UT 11 17 32 143 175 82% WA 25 36 61 84 77 109% WY 1 29 9% Total 1,022 1,493 2,113 2,910 3,936 5,407 7,143 17,949 w/o CA 231 364 576 869 1,144 1,534 1,905 3,888 49%

EIA 861-2016 Actual BTM-PV by Major Utilities (MW) Compare Acutal BTM-PV by Major Utilities to Modeled Values: Unit (MW) Assuemped Ratio Market Utility Number Utility Name Actual BTM-PV   Modeled Ratio 66.7% 50.0% 2010 2011 2012 2013 2014 2015 2016 Act 2016/ Inland 12825 NorthWestern Energy LLC - (MT) 2.0 3.2 3.9 4.8 6.3 8.2 29 28% 12.2 16.3 17166 Sierra Pacific Power Co 13.2 20.4 21.5 17.1 36.0 38.8 83 47% 58.3 77.7 9191 Idaho Power Co 0.2 1.2 1.7 2.1 3.0 7.7 39 20% 11.5 15.4 NW/Inland 14354 PacifiCorp 9.9 17.6 29.3 45.1 67.7 102.7 192.8 261 74% 289.2 385.5 NW PACW 7.4 12.9 19.5 30.0 38.7 45.9 60.1 73 82% 90.2 120.2 Inalnd PACE PAID 0.1 0.3 0.4 0.6 0.7 7 17% 1.8 2.4 PAUT 2.2 4.2 9.1 14.2 27.8 55.3 130.5 169 77% 195.7 261.0 PAWY 0.5 1.0 12 8% 1.5 20169 Avista Corp 0.8 1.1 1.3 2.8 23% 5.6 15500 Puget Sound Energy Inc 4.9 7.6 10.4 15.6 25.9 35.9 24 149% 53.8 71.7 16868 City of Seattle - (WA) 2.7 4.0 6.0 7.0 11.0 13.5 6 224% 20.2 26.9 18429 City of Tacoma - (WA) 1 176% 2.6 3.5 15248 Portland General Electric Co 12.1 16.0 21.2 25.1 30.7 40.7 53.6 79 68% 80.4 107.2 RM 15466 Public Service Co of Colorado 36.4 109.5 140.8 173.2 219.8 248.1 266.7 500 53% 400.1 533.4 SW 13407 Nevada Power Co 0.0 14.7 20.5 22.2 40.8 118.8 168.7 67 252% 253.1 337.5 803 Arizona Public Service Co 59.6 147.5 307.8 371.5 441.5 560.1 937 60% 840.2 1,120.2 16572 Salt River Project 29.9 22.6 40.5 58.0 91.2 120.8 125.5 438 29% 188.2 250.9 24211 Tucson Electric Power Co 31.7 45.2 56.7 97.5 138.2 135.3 433 31% 202.9 270.6 15473 Public Service Co of NM 18.8 26.4 41.2 50.5 60.5 78.9 248 32% 118.3 157.7 5701 El Paso Electric Co 0.9 4.5 8.0 10.0 11.9 44 34% 22.1 29.5 Total 3,462 2,846 3,795

The California Energy Commission (CEC) involvement in producing the California Demand Forecast Angela Tanghetti CEC

Preliminary 2017 CEC Demand Forecast

Preliminary 2017 CEC Demand Forecast

Preliminary 2017 CEC Demand Forecast

Preliminary 2017 CEC Demand Forecast

Process Review to determine the hourly Demand Response Andy Satchwell Lawrence Berkeley National Laboratory (LBNL)

Developing Demand Response Assumptions in the 2028 ADS Case Andy Satchwell Berkeley Lab WECC Data Work Group November 14, 2017

DSM Inputs to Western Regional Planning LBNL has worked with WECC staff and the State and Provincial Steering Committee (SPSC) over the past seven years to develop DSM-related assumptions and modeling inputs for WECC’s regional transmission planning studies Two types of demand response (DR) modeling assumptions required for each study case: DR resource quantities: How much DR is available to be dispatched in any given hour for each load zone? DR dispatch mechanics: When is the DR dispatched and how does it affect hourly loads and peak demand? DR resource quantities are based on non-firm load forecasts reported by balancing authorities to WECC

DR resource quantities: How much DR is available to be dispatched in any given hour for each load zone?

Developing DR Resource Quantities DR resource quantities are based on non-firm load forecasts reported by balancing authorities to WECC Four categories of non-firm load (i.e., DR program types): Interruptible, Direct Load Control, Pricing, and Load as a Capacity Resource Berkeley Lab compares and validates non-firm load forecasts against utility IRPs, regulatory filings, and other public sources Adjustments, as necessary, confirmed with utility staff The process yields an adjusted non-firm load forecast

Summary of DR Adjustments to 2026 Common Case Across the WECC footprint, adjustments made to DR programs resulted in a small overall change to the maximum potential load impact of DR programs in 2026 However, there were substantial changes in the types of DR programs identified (as well as their locations)

2026 Common Case Adjusted Non-Firm Load Forecast by BA and Program Type

DR dispatch mechanics: When is the DR dispatched and how does it affect hourly loads and peak demand?

DR Modeling Approach The goal is to realistically model DR resources within the constraints of WECC’s production cost models DR programs are used for reliability and economic purposes, limited by tariff provisions specifying maximum number of events per month or year Tariffs also specify multiple, sequential blocks (e.g., 4 to 6 hours) for events Approach and assumptions were vetted with WECC DSM Task Force and Modeling Work Group

Berkeley Lab’s DR Dispatch Tool Inputs Hourly Load Hourly LMPs Maximum Available Monthly DR Program constraints Resource Availability Calculate “hourly shaping factors” to scale maximum available DR to hourly load Simulated Dispatch Identify top-LMP hours to act as dispatch trigger Dispatch DR over top-LMP hours, subject to program constraints Output 8760 load-modifying profile of DR used in production cost model as static profile Note that hourly load and LMPS come from “No DR” run of production cost model, maximum available DR comes from LRS non-firm load forecasts, and program constraints are developed based on historical utility DR program dispatch information.

Next Steps Gather validation sources (e.g., utility IRPs, regulatory filings) Review non-firm load forecasts for all BAs except CA We will use CEC forecasted DR Contact BA staff as necessary to confirm adjustments

Questions? Publications: Andy Satchwell | asatchwell@lbl.gov | 510-486-6544 Publications: https://emp.lbl.gov/reports/resp That concludes the material I wanted to present today. This slide has our project team’s contact information if you have questions or want to follow up with further discussion. With that, I think we can move to a discussion and questions.

Loads and Load Modifiers 2028 ADS Time Line? Loads and Load Modifiers 2028 PCM ADS Draft 2028 PCM ADS Final 2028 PCM ADS Nov-Dec 2018 Jan-Mar 2018 Apr-Jun 2018