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

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Presentation on theme: "Distributed Generation Projections for High DG Case October 10, 2014 Arne Olson, Partner Nick Schlag, Sr. Consultant."— Presentation transcript:

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

2 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 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 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 adoptionMarket Driven DG Model Rely on estimates of interconnection potential as an upper bound

5 MODELING MARKET- DRIVEN DG

6 6 Channels for Distributed Solar PV 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 Program Goals (e.g. California Solar Initiative) RPS Set-Asides (e.g. 30% DG set-aside in Arizona) Market-Driven Adoption Policy-driven DG, modeled in past WECC studies New to WECC studies

7 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 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 10 Key Drivers of Market Driven DG Model The main drivers of the modeled customer adoption of solar PV are: 1.Solar PV capital cost 2.Customer bill savings 3.Federal investment tax credit 4.State-specific incentive programs 5.State net energy metering caps 6.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 11 High DG projections are developed by relaxing existing NEM caps and assuming achievement of aspirational solar PV cost reductions Overview of Assumptions AssumptionReference CaseHigh 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 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’ StateCurrent NEM Cap Arizonan/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 Coloradon/a Idahon/a New Mexicon/a Nevada3% of utility peak Oregon0.5% of peak for munis, coops, & PUDs; no cap for IOUs Utah0.1% of peak for munis; 20% of peak for IOUs Washington0.5% of peak Wyomingn/a

13 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 CaliforniaTechnical 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: 1.Rule 21 (Current Policy): sum of rated capacity of interconnections on a feeder may not exceed 15% of the feeder’s peak load 2.30% Rule: same as (1), but with constraint relaxed to 30% 3.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 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 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)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)Slide 31

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

17 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 decreases from 2022 Notable increases from 2022

18 18 High DG Case projections 2034 Projections Reference Case projections

19 DETAILED PROJECTIONS BY LOAD AREA

20 High DG Projections Total capacity: 22,648 MW

21 High DG Projections Total capacity: 31,650 MW

22 Thank You! Energy and Environmental Economics, Inc. (E3) 101 Montgomery Street, Suite 1600 San Francisco, CA Tel Web

23 MARKET-DRIVEN DG: METHODOLOGY AND ASSUMPTIONS

24 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: 1.Assess potential size of distributed solar PV market based on economics 2.Adjust forecast upward to meet any policy targets 3.Limit total installations based on state net metering caps

25 25 Step 1: Market-Driven Adoption 1.Determine payback period 2.Determine max market share 3.Fit logistic curve4.Apply to technical potential Technical potenialMW xMarket penetration at t% = Installed capacity at tMW

26 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 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 19Presented to TAS Future cost reductions primarily reflect lower balance- of-systems costs

28 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 29 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 Avoided Energy Cost California: IOUs’ high tiered rates provide strong incentive to customers Northwest: Low-cost hydropower keeps rates low SouthwestRocky Mountains General trend in retail rates

30 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 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 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 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 34 Assumed Payback Curves Payback curves are based on functional forms documented in SolarDS model

35 35 Assumed Technical Potential E3 calculates technical potential by specifying: 1.The percentage of total customers that could feasibly install solar PV (50% for residential and commercial) 2.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 TechnicalU.S. Renewable Energy Technical Potentials: A GIS- Based Analysis Potentials: A GIS- Based Analysis (NREL)

36 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 StatePolicy ArizonaRPS DG Set-Aside (4.5% of IOU/coop retail sales by 2025) CaliforniaCalifornia Solar Initiative (2,300 MW for IOUs; 700 MW for publics) ColoradoRPS DG set-aside (3% of IOU 2020 retail sales; 1% of public utility 2020 retail sales) New MexicoRPS DG set-aside (0.6% of 2020 retail sales) NevadaNevada Solar Incentives Program (36 MW among NVE and SPP) OregonEnergy Trust (124 MW among PGE and PacifiCorp) Solar Volumetric Incentive and Payments Program (27.5 MW among PGE, PacifiCorp, and IPC)

37 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 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 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


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