Distributed Solar Adoption in Orlando: A Household-Level Model for Distribution Resource Planning

Samuel Koebrich, Benjamin Sigrin, Robert Spencer, Paul Schwabe, Scott Haase, Justin Kramer, Sam Choi

Research output: NRELPresentation


Potential for rooftop solar in Florida is massive (47% of retail sales, 3rd overall nationally), yet adoption lags (12th nationally). A 2018 Florida Public Service Commission ruling authorizing solar third-party ownership (leasing) has substantially increased attention on distributed solar in the state. The city of Orlando has committed to a 100% clean-energy target by 2050 and deployment of solar and storage are expected to contribute significantly to reaching the goal. Deployment of customer-adopted solar, unlike utility-procured solar, is uncertain, but known to be spatially correlated with demographic factors and existing adoption. We develop a new method to adapt NREL's dGen model in order to represent building-level agents in adoption forecasts for the Orlando Utility Commission (OUC) service territory. Using the agent-based model we develop projections of solar adoption, subject to scenarios varying future solar costs and valuation, and aggregate adoption predictions by OUC distribution feeder. We find substantial spatial heterogeneity in the projected level of adoption by OUC distribution feeder. For instance, 25% of all projected adoption through 2050 would be concentrated on just 5% of feeders and 88% of projected adoption on 50% of feeders. Because of the uncertainty in adoption, bottoms-up solar adoption forecasting methods at the household-level are integral to long-term resource planning by anticipating system needs as customers increasingly adopt distributed solar, storage, electric vehicles, and other distributed energy resources.
Original languageAmerican English
Number of pages48
StatePublished - 2021

NREL Publication Number

  • NREL/PR-6A20-77308


  • agent based modeling
  • customer adoption modeling
  • distributed solar
  • distribution resource planning
  • Florida
  • forecasting
  • integrated resource planning
  • machine learning
  • Orlando Utility Commission


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