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

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• 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

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Process

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• 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.

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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.

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Data Limitation

• There are three major reasons why we cannot map DG to the

customer bus:1. It is reported that ISO planning studies may include DG mapping to

customer level.

2. 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.

3. 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).

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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: o For the 2026 Common Case – California DG, we’d used assumptions

developed by the CEC in their 2016 Load forecast.

o For the 2028 ADS PCM case – California DG, it is proposed to update

with assumptions from the CEC’s adopted 2018 Load forecast.

o 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.

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Behind-the-Meter Rooftop DG PV Projections in the Western Interconnection

March 22, 2016

Zach Ming, Consultant

Nick Schlag, Managing Consultant

Arne Olson, Partner

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E3’s Market Driven DG Model

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Previous 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

2026

Market Peak Market

Driven DG Load Driven DG

State (MW) (MW) (% of Peak)

Arizona 3,002 24,068 12.5%

California 17,103 70,686 24.2%

Colorado 1,611 11,959 13.5%

Idaho 79 5,787 1.4%

Montana 93 2,536 3.7%

New Mexico 494 5,288 9.3%

Nevada 246 10,240 2.4%

Oregon 286 10,657 2.7%

Utah 286 5,524 5.2%

Washington 84 21,444 0.4%

Wyoming 94 3,554 2.6%

Total 23,378 171,743 13.6%

12,218 MW CEC

IEPR

Last Week’s Results

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Changes this Week

E3 implemented two changes to the model based on DGW feedback – both result a lower DG forecast

1) Removal of ‘Green Premium’ of $0.01/kWh

o The green premium was a relic from the previous TEPPC case used to represent the preference a customer might have toward solar PV

2) Lower Technical Potential

o NREL released a new study with updated technical potential values for every state

o These values do not take into account home/building ownership or limitations for customers sizing to their own load so E3 applied a

o 67% factor for home/building ownership

o 80% factor for customer sizing mismatch

AZ 18.9

CA 129.0

CO 16.1

ID 4.7

MT 3.1

NM 6.1

NV 6.3

OR 14.1

UT 7.3

WA 22.8

WY 1.7

NREL source: http://www.nrel.gov/docs/fy12osti/51946.pdf

GW

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Updated Results

Updated results reflect the two changes to the model and assumptions

2026

Market Peak Market

Driven DG Load Driven DG

State (MW) (MW) (% of Peak)

Arizona 2,129 24,068 8.8%

California 14,061 70,686 19.9%

Colorado 835 11,959 7.0%

Idaho 33 5,787 0.6%

Montana 33 2,536 1.3%

New Mexico 309 5,288 5.8%

Nevada 91 10,240 0.9%

Oregon 177 10,657 1.7%

Utah 175 5,524 3.2%

Washington 77 21,444 0.4%

Wyoming 29 3,554 0.8%

Total 17,948 171,743 10.5%

12,218 MW CEC

IEPR

2026 Res/Com Breakdown

Res Com Res Com Total

AZ 64% 36% 1,370 760 2,129

CA 67% 33% 9,443 4,618 14,061

CO 66% 34% 552 283 835

ID 85% 15% 28 5 33

MT 57% 43% 19 14 33

NM 68% 32% 210 98 309

NV 14% 86% 12 78 91

OR 87% 13% 153 23 177

UT 79% 21% 138 37 175

WA 95% 5% 73 4 77

WY 66% 34% 19 10 29

Review and compare with EIA 861-2016

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Kevin Harris

PDWG Vice Chair

EIA 861 vs E3

EIA - 861 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 0 2 2 3 4 6 9 33 27%

MT 2 2 4 8 6 7 9 33 29%

NM 20 27 38 62 76 88 111 309 36%

NV 0 28 42 45 59 157 210 91 231%

OR 23 31 43 56 71 87 112 177 63%

UT 3 6 11 17 32 62 143 175 82%

WA 7 11 17 25 36 61 84 77 109%

WY 1 1 1 2 2 2 3 29 9%

Total 1,022 1,493 2,113 2,910 3,936 5,407 7,143 17,949 40%

w/o CA 231 364 576 869 1,144 1,534 1,905 3,888 49%

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EIA 861-2016 Actual BTM-PV by Major Utilities (MW)

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Compare Acutal BTM-PV by Major Utilities to Modeled Values: Unit (MW)

Assuemped Ratio

MarketUtilityNumber Utility Name Actual BTM-PV

Actual BTM-PV Modeled Ratio 66.7% 50.0%

2010 2011 2012 2013 2014 2015 2016 Act 2016/

Modeled

Inland 12825NorthWestern Energy LLC - (MT) 2.0 2.0 3.2 3.9 4.8 6.3 8.2 29 28% 12.2 16.3

Inland 17166 Sierra Pacific Power Co 13.2 20.4 21.5 17.1 36.0 38.8 83 47% 58.3 77.7

Inland 9191 Idaho Power Co 0.2 1.2 1.7 2.1 3.0 4.8 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 0.2 0.3 0.4 0.6 0.7 1.2 7 17% 1.8 2.4

Inalnd PACE PAUT 2.2 4.2 9.1 14.2 27.8 55.3 130.5 169 77% 195.7 261.0

Inalnd PACE PAWY 0.3 0.3 0.4 0.5 0.6 0.7 1.0 12 8% 1.5 2.0

NW 20169 Avista Corp 0.4 0.7 0.8 1.1 1.3 2.0 2.8 12 23% 4.2 5.6

NW 15500 Puget Sound Energy Inc 3.2 4.9 7.6 10.4 15.6 25.9 35.9 24 149% 53.8 71.7

NW 16868 City of Seattle - (WA) 1.2 2.7 4.0 6.0 7.0 11.0 13.5 6 224% 20.2 26.9

NW 18429 City of Tacoma - (WA) 0.2 0.2 0.3 0.4 0.6 1.1 1.8 1 176% 2.6 3.5

NW 15248Portland General Electric Co 12.1 16.0 21.2 25.1 30.7 40.7 53.6 79 68% 80.4 107.2

RM 15466Public 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

SW 803Arizona Public Service Co 59.6 59.6 147.5 307.8 371.5 441.5 560.1 937 60% 840.2 1,120.2

SW 16572 Salt River Project 29.9 22.6 40.5 58.0 91.2 120.8 125.5 438 29% 188.2 250.9

SW 24211Tucson Electric Power Co 21.5 31.7 45.2 56.7 97.5 138.2 135.3 433 31% 202.9 270.6

SW 15473 Public Service Co of NM 17.6 18.8 26.4 41.2 50.5 60.5 78.9 248 32% 118.3 157.7

SW 5701 El Paso Electric Co 0.9 1.8 4.5 8.0 10.0 11.9 14.7 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

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Angela Tanghetti

CEC

Preliminary 2017 CEC Demand Forecast

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Preliminary 2017 CEC Demand Forecast

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Preliminary 2017 CEC Demand Forecast

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Preliminary 2017 CEC Demand Forecast

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Process Review to determine the hourly Demand Response

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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

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DR resource quantities:

How much DR is available to be

dispatched in any given hour for each

load zone?

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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

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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)

DR Program Type

2026 LRS

Forecast

(MW; NCP)

2026 LRS

Adjusted

Forecast

(MW; NCP)

Percent

Change

Interruptible 2,898 2,943 2%

Direct Load Control 950 1,280 35%

Price Responsive - 105 n/a

Load as a Capacity Resource968 543 -44%

Total 4,816 4,871 1%

2026 Common Case Adjusted Non-Firm

Load Forecast by BA and Program Type

1,966

608 506 480

390 281

200 131

60 54 52 45 34 26 20 12 6 0

500

1,000

1,500

2,000

2,5002

02

6 m

axim

um

ava

ilab

le n

on

-fir

m lo

ad

(MW

; N

CP

)

Price Responsive

Direct Load Control

Interruptible

Load as a Capacity Resource

DR dispatch mechanics:

When is the DR dispatched and how

does it affect hourly loads and peak

demand?

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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

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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

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?

Andy Satchwell | asatchwell@lbl.gov | 510-486-6544

Publications:

https://emp.lbl.gov/reports/resp

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2028 ADS Time Line?

Loads and Load Modifiers

2028 PCM ADS

Draft 2028 PCM

ADSFinal 2028 PCM ADS

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Apr-Jun 2018Jan-Mar 2018Nov-Dec 2018