1. Distributed Generation Projections for High DG Case
October 10, 2014
Arne Olson, Partner
Nick Schlag, Sr. Consultant

2. Background
In a number of past efforts, E3 has worked with LBNL and WECC to establish input assumptions regarding distributed generation in study cycles:
In , E3 worked with LBNL and WECC to develop estimates of DG potential for the SPSC’s 2022 and 2032 High DG/DSM cases
In 2014, E3 & LBNL developed an approach to project “market-driven” distributed generation in the WECC, which was used to inform the 2024 Common Case
WECC has requested projections of distributed generation consistent with High DG futures for the Western Interconnection to use in the SPSC’s 2024 and 2034 High DG/DSM cases
To develop these projections, E3 has used logic from both prior efforts to assess future DG installations

3. Defining “Distributed Generation”
“Distributed” generation means different things to different people:
Behind-the-meter, e.g., customer-owned resource
Small utility or IPP owned resource that is connected at the distribution system and serves load downstream
Small resource that is connected at the distribution system and does not serve load downstream
Small resource that is connected to the sub-transmission system (i.e., low-voltage transmission) near load
Small resource that is located remote from load
Large resource that is located in load pocket and helps defer or avoid transmission investments
This analysis focuses on small scale solar PV installations that individual retail customers would install to avoid purchasing electricity from an electric utility
Does not include “wholesale DG” that a utility might procure to meet state DG targets

4. Approaches to Developing High DG Assumptions
2022/2032 High DG Case assumptions developed in two steps:
Estimate interconnection potential for each state
Make state-specific adjustments to interconnection potential to reflect differences in economic drivers of DG
2024/2034 High DG Case assumptions derived by modeling customer decisions under a scenario favorable to distributed generation adoption
Use E3’s Market Driven DG Model to develop projections of adoption
Rely on estimates of interconnection potential as an upper bound

5. Modeling Market-Driven DG

6. Channels for Distributed Solar PV Adoption Market-Driven Adoption
Background
In prior transmission planning study cycles, WECC has incorporated distributed solar PV assumptions consistent with state policy goals
This framework ignores the potential for market-driven DG
With low PV costs, this could become a large amount of capacity
E3 and LBNL have developed a framework to incorporate market-driven DG into transmission planning studies
Channels for Distributed Solar PV Adoption
Program Goals
(e.g. California Solar Initiative)
Policy-driven DG, modeled in past WECC studies
RPS Set-Asides
(e.g. 30% DG set-aside in Arizona)
New to WECC studies
Market-Driven Adoption

7. E3’s Market Driven DG Model
To provide inputs for WECC’s transmission planning studies, E3 has developed a model of DG deployment throughout the WECC footprint between present day and 2040
Joint funding from WECC and LBNL through DOE’s ARRA grants
Input assumptions capture geographic variations in PV cost-effectiveness and state policy
State-specific PV costs
State-specific net metering policy
Capacity factors at a BA level
Utility-specific retail rates (and incentives where applicable)
The model also captures the changing cost-effectiveness of PV:
Continued declines in PV capital costs
Expiration of incentives & tax credits (e.g. ITC in 2017)
Escalation of retail rates
Expected changes to state net metering policies (e.g. California AB 327)

8. 2024 Common Case Recommendations
The Market-Driven DG Model used to develop preliminary recommendations for customer-sited solar for the 2024 Common Case
For the 2024 Common Case, TAS adjusted the recommended values (shown at left) downward by 20%

9. High DG Case Recommendations

10. Key Drivers of Market Driven DG Model
The main drivers of the modeled customer adoption of solar PV are:
Solar PV capital cost
Customer bill savings
Federal investment tax credit
State-specific incentive programs
State net energy metering caps
Utility system interconnection potential
Changing the assumptions for each of these parameters provides the basis for exploring alternative projections
Affect customer decision to invest in solar PV
Limit total penetration on a utility’s system

11. Overview of Assumptions
High DG projections are developed by relaxing existing NEM caps and assuming achievement of aspirational solar PV cost reductions
Assumption
Reference Case
High DG Case
Net Metering Caps
Current Policy
Current NEM caps remain in place
California cap lifted after 2016
NEM Caps Removed
All NEM caps lifted
Limits associated with interconnection potential enforced
Solar PV Cost Trends
Moderate Reductions
Cost trajectory derived by E3 for TEPPC planning studies
Aspirational Reductions
Sunshot goals achieved by 2020

12. Treatment of NEM Caps
In the Reference Case, each state’s NEM cap was enforced according to current policy
High DG case assumes current NEM caps are removed, allowing installations of DG in each utility’s service territory up to its ‘Interconnection Potential’
State
Current NEM Cap
Arizona
n/a
California
Limits under current NEM rate design established by AB327 (approximately 5% of non-coincident peak); beyond 2017, alternative rate designs will be considered with no associated cap
Colorado
Idaho
New Mexico
Nevada
3% of utility peak
Oregon
0.5% of peak for munis, coops, & PUDs; no cap for IOUs
Utah
0.1% of peak for munis; 20% of peak for IOUs
Washington
0.5% of peak
Wyoming

13. Interconnection Potential Background
To estimate interconnection potential across the WECC, E3 leveraged results from a 2012 analysis, Technical Potential for Local Distributed Photovoltaics in California
Study produced estimates of the amount of “local” distributed PV (LDPV) potential under different interconnection standards in California:
Rule 21 (Current Policy): sum of rated capacity of interconnections on a feeder may not exceed 15% of the feeder’s peak load
30% Rule: same as (1), but with constraint relaxed to 30%
Max w/o Curtailment: the maximum capacity that can be installed on a feeder for which all generation will serve load on that feeder (e.g. no required backflow or curtailment)
In 2012 study cycle, E3 generalized these results to the WECC BAs; the same method is used to determine limits in this study cycle
30% Rule for 2024 High DG projections
Max w/o Curtailment for 2034 High DG projections

14. Capital Cost Trajectories
Reference case cost reduction trajectory derived through application of learning curve approach
20% learning rate on modules; 15% on BOS
IEA medium-term renewable energy outlook
Adopted by TEPPC
Aspirational case cost reduction trajectory assumes achievement of Sunshot goals by 2020
$1.50/W residential
$1.25/W commercial

15. Other Key Assumptions No changes in retail rate design
Surplus NEM generation is compensated at full retail rate
EXCEPTION: in California, after 2017, exports are assumed to be compensated at avoided cost (see Slide 30)
Retail rates escalate at 0.5% per year in real terms
Federal ITC sunsets in 2017
Credit reduces to 10% of capital costs thereafter
Current state incentive programs sunset after current NEM cap is exceeded
e.g. Washington & Oregon (see Slide 31)

16. Incremental Additions Incremental Additions
2024 Projections
Reference Case projections
Incremental Additions
High DG Case projections
Incremental Additions

17. Comparison to 2022 High DG Recommendations
2022 and 2024 High DG projections have similar quantities of distributed generation capacity, but show a regional shift
Relative increases in California, Colorado
Slight decreases in states in the Pacific Northwest
Notable increases from 2022
Notable decreases from 2022

18. 2034 Projections
Reference Case projections
High DG Case projections

19. Detailed Projections by Load Area

20. 2024 High DG Projections
Total capacity: 22,648 MW

21. 2034 High DG Projections
Total capacity: 31,650 MW

22. Thank You!
Energy and Environmental Economics, Inc. (E3) Montgomery Street, Suite San Francisco, CA Tel Web

23. Market-Driven DG: Methodology and Assumptions

24. General Model Logic
E3’s Market-Driven DG model combines a customer decision model with policy targets and NEM caps to provide a comprehensive assessment of behind-the-meter solar PV in the Western Interconnect
Modeling steps:
Assess potential size of distributed solar PV market based on economics
Adjust forecast upward to meet any policy targets
Limit total installations based on state net metering caps

25. Step 1: Market-Driven Adoption
Determine payback period
Determine max market share
Fit logistic curve
Apply to technical potential
Technical potenial MW x
Market penetration at t % =
Installed capacity at t

26. Calculating the Payback Period
The payback period is the first year in which a customer who choose to install solar PV will have a net positive cash flow
To determine the payback period, E3 considers:
System capital costs: costs of purchasing & installing a PV system
Operating & maintenance costs: costs of year-to-year maintenance, including inverter replacement
Federal tax credits: investment tax credit (30% until 2017; 10% thereafter)
State & local incentives: up-front & performance-based incentives, vary by utility & state
Bill savings: reductions monthly energy bills, vary by utility
Green premium: a non-financial value that a customer derives from having invested in solar PV (assumed to be 1 cent/kWh)

27. Solar PV Capital Costs by Installation Vintage
Installed PV cost assumptions based on draft recommendations for PV capital costs developed by E3
Presented to TAS on December 19
Future cost reductions primarily reflect lower balance-of-systems costs

28. Solar PV Costs by State
System average costs are adjusted for each state to capture regional variations in costs
Regional adjustments based on LBNL’s Tracking the Sun VI
Where Tracking the Sun VI did not report PV costs, costs were interpolated based on the Army Corps of Engineer’s Construction Works Cost Index

29. Avoided Energy Cost
All WECC states currently allow net metering, under which a customer is compensated for PV output based on its retail rate
This is the primary economic benefit to a customer who chooses to install distributed PV
Market adoption model includes utility-specific rate information for 30 large utilities in the West (a subset are shown below)
For other smaller utilities, a state-specific average retail rate is used
California: IOUs’ high tiered rates provide strong incentive to customers
Southwest
Rocky Mountains
Northwest: Low-cost hydropower keeps rates low
General trend in retail rates

30. Changes to Avoided Energy Cost over Time
E3 assumes that utilities continue to compensate customers at their full retail rate throughout the analysis horizon with one exception
Real escalation of 0.5% per year is assumed
California’s AB 327 directs the CPUC to implement a standard NEM tariff beginning in July 2017
As this tariff has not yet been defined, E3 has chosen to model it in the following manner:
All generation consumed on-site is compensated at the customer’s retail rate
50% for residential systems, 70% for commercial systems (based on CPUC NEM study)
All generation exported to the grid is compensated at the utility’s long-run avoided cost (based on a CCGT)

31. State-Specific Incentives
Payback period is also heavily influenced by state incentive programs
E3’s model captures the impact of two large incentive programs:
Renewable Energy Cost Recovery Program (WA)
Performance-based incentive capped at $5,000 per year
Program ends in 2020
Residential Energy Tax Credit (OR)
Incentive of $2.1/W-dc, capped at $6,000
Program ends in 2018
Note: incentives linked to specific policy targets (e.g. set-asides, program goals) are not modeled explicitly and are instead accounted for by adjusting market-driven forecast upward to meet policy goals

32. Sample Payback Period Results, 2013 Residential Systems
Payback periods vary widely across the WECC geography as a function of:
System costs
Incentives
Retail rates
Capacity factors

33. Modeling Solar PV Adoption
NREL’s Solar Deployment System (SolarDS) model provides one of the more transparent forecasts of PV adoption:
“…a geospatially rich, bottom-up, market-penetration model that simulates the potential adoption of photovoltaics (PV) on residential and commercial rooftops in the continental United States through 2030”
Much of the logic used in the Adoption Module has been adapted from SolarDS:
Maximum market share as a function of payback period
Logistic curves for adoption
Documentation for SolarDS model:
(Figures taken from this document)

34. Assumed Payback Curves
Payback curves are based on functional forms documented in SolarDS model

35. Assumed Technical Potential
E3 calculates technical potential by specifying:
The percentage of total customers that could feasibly install solar PV (50% for residential and commercial)
The representative system size for a typical install (4 kW for residential; 50 kW for commercial)
Resulting assumed technical potential aligns well with NREL’s assessment of rooftop PV technical potential on a state level:
Total technical potential is approximately 150 GW in 2010
Source: U.S. Renewable Energy Technical
Potentials: A GIS-Based Analysis (NREL)

36. Step 2: Policy Adjustments
A large number of states have enacted policies to encourage the deployment of distributed solar PV
In cases where the market-based adoption forecast falls short of state policy targets, upward adjustments are made to reflect achievement of current policy
Assumes utilities will fund programs to reach targets
State
Policy
Arizona
RPS DG Set-Aside (4.5% of IOU/coop retail sales by 2025)
California
California Solar Initiative (2,300 MW for IOUs; 700 MW for publics)
Colorado
RPS DG set-aside (3% of IOU 2020 retail sales; 1% of public utility 2020 retail sales)
New Mexico
RPS DG set-aside (0.6% of 2020 retail sales)
Nevada
Nevada Solar Incentives Program (36 MW among NVE and SPP)
Oregon
Energy Trust (124 MW among PGE and PacifiCorp)
Solar Volumetric Incentive and Payments Program (27.5 MW among PGE, PacifiCorp, and IPC)

37. Policy Adjustments
For each utility, initial market-driven DG forecast is adjusted upward in each year it is short of policy targets
Illustrative example shown for APS

38. Step 3: Adjust for Net Metering Policy
Common Case projections assume all current NEM caps remain in place
Arizona, Colorado, Montana, New Mexico, Wyoming: no cap
Oregon & Washington: 0.5% of utility peak
Idaho: 0.1% of utility peak
Nevada: 3% of utility peak
California: 5% of noncoincident peak (currently)
Common Case projections assume these caps remain in place throughout the analysis
Exception: California’s AB 327 lifts the existing NEM cap beginning in 2017 with the implementation of a standard NEM tariff

39. NEM Adjustments
For each utility whose installed capacity would be constrained by a NEM cap, installation forecast is adjusted downward to the limit
Illustrative example shown for Puget Sound