Forecasting Distributed Energy Resources Adoption for Power Systems

Nicholas WIllems, Ashok Sekar, Benjamin Sigrin, Varun Rai

Research output: Contribution to journalArticlepeer-review

4 Scopus Citations

Abstract

Failing to incorporate accurate distributed energy resource penetration forecasts into long-term resource and transmission planning can lead to cost inefficiencies at best and system failures at worst. We have developed an open-source tool that employs an advanced Bass specification to calibrate and forecast technology adoption. The advanced specification includes geographic clustering, exogenously estimated market size, and dynamic time steps. Training on historical adoption of rooftop photovoltaics at the U.S. county-level and using detailed techno-economic estimates, our model achieves a two-year average mean-absolute-percentage-error of 19% in predicting system counts at the county-level, weighted by population. Model error was negatively correlated with market maturity—the error was 12% for counties in states with at least 28 W-per-capita of installed capacity. The advanced specification significantly reduces unweighted forecasting percent error compared to a conventional Bass specification: from 196% to 25% for capacity and from 226% to 22% for system count.

Original languageAmerican English
Article numberArticle No. 104381
Number of pages20
JournaliScience
Volume25
Issue number6
DOIs
StatePublished - 17 Jun 2022

Bibliographical note

Publisher Copyright:
© 2022 The Author(s)

NREL Publication Number

  • NREL/JA-7A40-81379

Keywords

  • Energy management
  • Energy Modeling
  • Energy policy
  • Energy resources
  • Energy Systems

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