systems with distributed energy resources Benjamin Sigrin 1 & - PowerPoint PPT Presentation

Forecasting load on distribution systems with distributed energy resources Benjamin Sigrin 1 & Andrew Mills 2 National Renewable Energy Laboratory (NREL); 1 Lawrence Berkeley National Laboratory (LBNL) 2 Oregon PUC Webinar on Load Forecasting in Distribution System Planning May 14th, 2020 May 12, 2020 May 12, 2020 1 1 Benjamin.Sigrin@NREL.gov

Importance of Including Distributed Energy Resources in Load Forecasts ► Distribution system investments: replacing aging infrastructure and distribution expansion ► Procurement of generating capacity to meet peak demand ► Proactive investments to increase hosting capacity ► Evaluating the costs and benefits of incentives or policies to promote distributed energy resources (DER) May 12, 2020 May 12, 2020 2 2

Impact of DPV on T&D Investments: Potential Deferral Value Source: Adapted from Cohen et al. 2016 May 12, 2020 May 12, 2020 3 3

Increasing Adoption of DER Increases the Importance of Accurate Forecasts in Planning — — Regardless of misforecast severity all plans are updated every 5 years Costs of roughly $70 million from severe underforecasting and $20 million from severe overforecasting for a utility with sales >10TWh/yr and with up to 8.5% of sales from DPV by the end of a 15-year – Source: Gagnon et al. (2018) period May 12, 2020 May 12, 2020 4 4 –

Planning for a Distributed Disruption: Innovative Practices for Incorporating Distributed Solar into Utility Planning Context • Analysts project that distributed solar photovoltaics (DPV) will continue growing rapidly across the United States. • Growth in DPV has critical implications for utility planning processes, potentially affecting future infrastructure needs. • Appropriate techniques to incorporate DPV into utility planning are essential to ensuring reliable operation of the electric system and realizing the full value of DPV. Approach • Comparative analysis and evaluation of roughly 30 recent planning studies, identifying innovative practices, lessons learned, and state-of-the-art tools. Scope • Electric infrastructure planning (IRPs, transmission, distribution). • Focus on the treatment of DPV, with emphasis on how DPV growth is accounted for within planning studies. May 12, 2020 May 12, 2020 5 5

Key Findings ► Forecasting load with DER is often “top - down”: separately forecast load and quantity of DER at the system level, allocate that system forecast down to more granular levels. ► Many factors affect customer decisions to adopt DER, including the cost and performance of DER, incentives, customer retail rates, peer-effects, and customer demographics. Customer-adoption models can help account for many of these factors. ► Forecasts are uncertain: It may be valuable to combine various approaches and to benchmark against third-party forecasts. May 12, 2020 May 12, 2020 6 6

High End of 3 rd Party Forecasts Suggests More DPV Than Considered By Utilities May 12, 2020 May 12, 2020 7 7

A Variety of Methods Are Used to Develop DPV Forecasts 30%� Near-term� (~2020)� Long-term� (~2030)� � sales)� 20%� retail� of� (%� penetra; on� 10%� DPV� 0%� HECO� ELA� DEI� PNM� FP&L� IPC� PG&E� NVP� LADWP� APS� TEP� ISO-NE� NYISO� NSP� GPC� DOM� WECC� PAC� NWPCC� PSE� TVA� S; pulated� Historical� � Program-Based� Adop; on� Modeling� Other� Stipulated Adoption Modeling Trend� May 12, 2020 May 12, 2020 8 8 ’

Customer-adoption Modeling Brings Customer Decisions Into DPV Forecast Explanatory Factors Used Recent Incentive Method Description Technical PV End-user installation program potential economics behaviors rates targets Stipulated Assumes end-point Forecast DPV deployment Extrapolates future Historical X deployment from Trend historical data Program- Assumes program X Based deployment targets Approach reached Customer- Uses adoption models X X X X Adoption that represent end- Modeling user decision making May 12, 2020 May 12, 2020 9 9

Some Planners Use Customer-adoption Models for DPV Forecasting Technical Adapted from: Potential Gagnon et al. 2016 Willingness- to-adopt *illustrative Diffusion *illustrative May 12, 2020 May 12, 2020 10 10

Technical Potential Estimates Are Typically Based on Customer Count and Rooftops ► Technical potential studies used by utilities in our sample of studies were based primarily on customer counts and floor space surveys ◼ Rooftop space is based on average number of floors and assumptions about the density of PV arrays ► New emerging tools like Light Detection and Ranging (LiDAR) imaging can refine technical potential estimates: ◼ Infer shading, tilt, and azimuth from rooftop images ◼ Apply availability constraints to exclude unsuitable orientations or insufficiently large contiguous areas ► Can also refine with permitting and zoning restrictions, if applicable ► May overestimate suitability without consideration of roof condition, building age, electric code compliance, and building ownership May 12, 2020 May 12, 2020 11 11

Economic Factors, Especially Rate Design, Significantly Affect Adoption Projections ’ 180 decreased increased deployment deployment Customer 160 Flat rate $10 / month Charge 140 Higher feed- $50 / month US DPV Deployment (GW) in tariff 120 Reference Flat rate 100 $10 fixed charge Time-varying rate 80 Time- varying rate Partial net metering 60 Partial net metering 40 $50 fixed Lower Feed-in charge Tariff 20 lower FIT Higher 0 -100% -80% -60% -40% -20% 0% 20% 2014 2020 2030 2040 2050 Change in Deployment from Reference Scenario (%) Figure 7. National distributed PV deployment by scenario (with rate feedbac Source: Darghouth et al. 2016 May 12, 2020 May 12, 2020 12 12 ’

’s ’s Forecasters Tend to Rely on Similar Willingness-to-adopt Curves PacifiCorp� -� Residen, al� (%)� 100%� PacifiCorp� -� Commercial� Share� PacifiCorp� -� Industrial� 80%� PG&E� -� Residen, al� Market� PSE� -� Res.� and� Com.� 60%� WECC� -� Residen, al� 40%� WECC� -� Commercial� Ul, mate� 20%� 0%� 0� 5� 10� 15� Payback� Period� (Years)� Note: Dashed gray lines (WECC) are for existing buildings, and dotted gray lin Note: Dashed gray lines (WECC) are for existing buildings, and dotted gray lines are for new buildings. May 12, 2020 May 12, 2020 13 13 ’s

Diffusion of Technology Impacts: Time to Achieve Ultimate Market Share Source: Meade and Islam (2006) ► The Bass diffusion model and Fisher-Pry model are two common choices that produce the characteristic “S - Curve” in adoption. May 12, 2020 May 12, 2020 14 14

Diffusion Curves for DPV Forecasts Are Often Based on Fits to Data, and Can Vary Widely Stove Market Penetration of Selected Technologies 1900 - 2008 Telephone 100 Electricity 90 Auto Radio 80 Refrigerator % of Households 70 Clothes Washer 60 Clothes Dryer 50 Dishwasher Air Conditioning 40 Color TV 30 Microwave Source: Federal 20 Reserves of San VCR Francisco and 10 Computer Dallas (Sean Ong, Cell Phone 0 NREL) Internet 1900 1910 1920 1930 1940 1950 1960 1970 1980 1990 2000 2010 ► Precedent for S-curve in diffusion of other technologies ► Highly variable time to saturation, but typically measured in decades. ► Parameter fit (time-to-saturation) is sensitive to observed data; initial studies typically benchmarked to other regions/technologies May 12, 2020 May 12, 2020 15 15

Propensity to Adopt Accounts for Factors Like Customer Demographics Predictive Factors Used Location of Detailed Method Description Location of existing load or customer existing DPV population characteristics Assumes DPV is distributed in Proportional to X proportion to load or Load population Proportional to Assumes DPV grows in X Existing DPV proportion to existing DPV Predicts customer adoption Propensity to based on factors like customer X X X Adopt demographics or customer load May 12, 2020 May 12, 2020 16 16

Predicting the Location of DPV Adoption Using Propensity to Adopt Source: PG&E 2015 DRP May 12, 2020 May 12, 2020 17 17 –

Factors Considered in PG&E’s Propensity to Adopt Metric ► Residential Customers: ► Non-Residential Customers: ◼ Home ownership ◼ Property ownership ◼ Electricity usage ◼ Electricity usage ◼ Income ◼ Retail rate ◼ Credit ◼ Business type (NAICS) ◼ Building characteristics (area, ◼ Building characteristics (area, number of stories) number of stories) ► Propensity to adopt metric is then used to allocate system forecast down to customers. Source: PG&E presentation to DRPWG (4/2017) May 12, 2020 May 12, 2020 18 18