NewEnergyNews: TODAY’S STUDY: How To Know When And Where Distributed Solar Is Coming


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    Tuesday, February 06, 2018

    TODAY’S STUDY: How To Know When And Where Distributed Solar Is Coming

    Forecasting load on the distribution and transmission system with distributed energy resources

    Andrew D. Mills, January 12, 2018 (Grid Modernization Laboratory Consortium/U.S. Department of Energy)

    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)…

    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…

    DPV Deployment Drivers ▶ DPV economics:

    ◼ DPV technology cost and performance

    ◼ Federal and State incentives

    ◼ New business models (e.g., third party ownership)

    ◼ Electricity prices

    ◼ Rate design (including the availability of Net Energy Metering)

    ▶ Public policy:

    ◼ Renewables Portfolio Standards and environmental requirements

    ◼ CO2 regulation

    ▶ Customer preferences:

    ◼ DPV deployment may be shaped by interest in increased customer choice

    ▶ Macro factors:

    ◼ Economic growth, load growth, oil prices, and cost and availability of complementary technologies (e.g. storage and electric vehicles)…

    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…

    Advances in Customer Adoption Modeling

    ▶ Agent based models simulate actions and interactions of agents to assess their individual effects on a larger system.

    ◼ Allows for better representation of heterogeneity of customers and more complex decision-making criteria

    ▶ Discrete choice models have a well defined methodology for soliciting customer preferences and can model competition between several options

    ◼ Provides framework for empirically derived forecasts

    ▶ Some open questions:

    ◼ How might consumption change after adoption of DPV: is there a rebound effect?

    ◼ How does the willingness-toadopt curve vary across customer segments?

    ◼ How does customer adoption of DPV compare to customer demand for community solar? Do these two options compete directly for market share or are they complementary?

    Additional Challenges: Removing DER from Historical Load to Create Accurate Load Forecasts

    PJM recently adjusted load forecasting methodology to better account for behind-the-meter PV

    ▶ Original approach used the observed load to forecast future load, without adjusting for effect of behind-the-meter DPV on the observed load

    ◼ Load reductions from behind-themeter DPV were being attributed to new end uses in the load forecasting model

    ▶ Revised approach removes estimate of historical PV before forecasting load, then adds back in forecast of DPV to new net load forecast

    More Examples of DER in Transmission Plans

    ▶ Evaluating DPV as a resource option:

    ◼ CAISO transmission planning process identifies transmission needs to meet reliability criteria, then examines feasibility of meeting needs with DPV.

    ◼ If CAISO finds it is feasible to meet needs with increased DPV, information is passed onto CPUC and utilities to determine if programs to encourage additional DPV would be cost-effective.

    ▶ Locating DPV within the system:

    ◼ ISO-NE and NYISO use the load-zone-level DPV forecast in their capacity markets and transmission planning. PJM adjusts the load-zone peak demand by the on-peak contribution of DPV for its capacity market and transmission planning.

    ▶ Peak demand reduction (i.e. transmission level capacity credit):

    ◼ ISO-NE and PJM use a stricter definition of peaks in transmission planning than for the capacity market.

    ▶ Consistent scenarios across planning forums:

    ◼ CAISO/CPUC/CEC coordination, NYISO Gold Book, ISO-NE 10-year regional planning process to coordinate assumptions

    Forecasting Other Distributed Energy Resources

    ▶ Some DER are similar to DPV :

    ◼ Systems can be installed either in-front-of- or behind-the-meter

    ◼ Adoption can occur for residential, commercial, or industrial customers

    ▶ These technologies have yet to see significant adoption due to higher cost or other barriers, but adoption might increase in the future. Similar forecasting tools and models can be used for these emerging technologies.

    ▶ Other DER systems are different in that the system cost, performance, and design are specific to individual customers and systems tend to be larger (e.g., CHP units)

    ▶ In these cases, local knowledge from distribution planners might be more useful than the top-down methods described here…

    Key Questions for Regulators About DER Forecasts

    ▶ What are the primary factors that drive your forecast of DER adoption? How do you consider customer economics and factors that might affect customer economics within the forecasting horizon?

    ▶ How do you account for the tendency for adoption of technologies to follow an S-shaped curve?

    ▶ How does your forecast compare to forecasts from third parties for the same region?

    ▶ How do you account for factors that might be uncertain such as availability of future incentives, technology cost, or customer choice?

    ▶ Do you use a top-down method to forecast DER adoption at the system level? If so, how do you allocate that forecast down to the distribution level? Do you account for differences in customer demographics?

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