1. 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

2. 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

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

4. 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

5. 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.

6. 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.

7. 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).

8. 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 E estimate for DG, however, PDWG should work with the regions to validate the forecast and take to DS for final approval.

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

10. E3’s Market Driven DG Model

11. 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

12. 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:

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

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

15. 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%

16. 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

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

18. Preliminary 2017 CEC Demand Forecast

19. Preliminary 2017 CEC Demand Forecast

20. Preliminary 2017 CEC Demand Forecast

21. Preliminary 2017 CEC Demand Forecast

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

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

24. 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

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

26. 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

27. 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)

28. 2026 Common Case Adjusted Non-Firm Load Forecast by BA and Program Type

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

30. 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

31. 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.

32. 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

33. Questions? Publications:
Andy Satchwell | |
Publications:
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.

35. 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