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

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 ne...

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Bibliographic Details
Published in:IEEE transactions on power systems 2020-07, Vol.35 (4), p.3133-3144
Main Authors: Bu, Fankun, Dehghanpour, Kaveh, Yuan, Yuxuan, Wang, Zhaoyu, Zhang, Yingchen
Format: Article
Language:English
Subjects:
Clustering
Clustering algorithms
Correlation
Current distribution
Demand
Distributed generation
distribution system
Energy distribution
Energy sources
Game theory
Libraries
Optimization
Photovoltaic cells
Roofs
Rooftop solar photovoltaic
Separators
Smart meters
SOLAR ENERGY
Solar power generation
source disaggregation
Utilities
Volatility
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Summary: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.
ISSN:0885-8950
1558-0679
DOI:10.1109/TPWRS.2020.2966732