A Data-Driven Game-Theoretic Approach for Behind-the-Meter PV Generation Disaggregation

Fankun Bu, Kaveh Dehghanpour, Yuxuan Yuan, Zhaoyu Wang, Yingchen Zhang

Research output: Contribution to journalArticlepeer-review

73 Scopus Citations

Abstract

Rooftop solar photovoltaic (PV) power generator is a widely used distributed energy resource (DER) in distribution systems. Currently, the majority of PVs are installed behind-the-meter (BTM), where only customers’ net demand is recorded by smart meters. Disaggregating BTM PV generation from net demand is critical to utilities for enhancing grid-edge observability. In this paper, a data-driven approach is proposed for BTM PV generation disaggregation using solar and demand exemplars. First, a data clustering procedure is developed to construct a library of candidate load/solar exemplars. To handle the volatility of BTM resources, a novel game-theoretic learning process is proposed to adaptively generate optimal composite exemplars using the constructed library of candidate exemplars, through repeated evaluation of disaggregation residuals. Finally, the composite native demand and solar exemplars are employed to disaggregate solar generation from net demand using a semi-supervised source separator. The proposed methodology has been verified using real smart meter data and feeder models.
Original languageAmerican English
Pages (from-to)3133-3144
Number of pages12
JournalIEEE Transactions on Power Systems
Volume35
Issue number4
DOIs
StatePublished - 2020

NREL Publication Number

  • NREL/JA-5D00-77312

Keywords

  • distribution system
  • game theory
  • rooftop solar photovoltaic
  • source disaggregation

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