Disaggregating Customer-Level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars

Bu, Fankun; Dehghanpour, Kaveh; Yuan, Yuxuan; Wang, Zhaoyu; Guo, Yifei

doi:10.1109/tpwrs.2021.3074614

Disaggregating Customer-Level Behind-the-Meter PV Generation Using Smart Meter Data and Solar Exemplars

Journal Article · Wed Apr 21 00:00:00 EDT 2021 · IEEE Transactions on Power Systems

DOI:https://doi.org/10.1109/tpwrs.2021.3074614· OSTI ID:1961203

^[1]; ^[2]; ^[2]; ^[2]; Guo, Yifei ^[2]

Iowa State Univ., Ames, IA (United States); Iowa State University
Iowa State Univ., Ames, IA (United States)

Customer-level rooftop photovoltaic (PV) has been widely integrated into distribution systems. In most cases, PVs are installed behind-the-meter (BTM), and only the net demand is recorded. Therefore, the native demand and PV generation are unknown to utilities. Separating native demand and solar generation from net demand is critical for improving grid-edge observability. In this paper, a novel approach is proposed for disaggregating customer-level BTM PV generation using low-resolution but widely available hourly smart meter data. The proposed approach exploits the strong correlation between monthly nocturnal and diurnal native demands and the high similarity among PV generation profiles. First, a joint probability density function (PDF) of monthly nocturnal and diurnal native demands is constructed for customers without PVs, using Gaussian mixture modeling (GMM). Deviation from the constructed PDF is utilized to probabilistically assess the monthly solar generation of customers with PVs. Then, to identify hourly BTM solar generation for these customers, their estimated monthly solar generation is decomposed into an hourly timescale; to do this, we have proposed a maximum likelihood estimation (MLE)-based technique that utilizes hourly typical solar exemplars. Leveraging the strong monthly native demand correlation and high PV generation similarity enhances our approach's robustness against the volatility of customers’ hourly load and enables highly-accurate disaggregation. Furthermore, the proposed approach has been verified using real native demand and PV generation data.

View Accepted Manuscript (DOE)

Research Organization:: Iowa State Univ., Ames, IA (United States)

Sponsoring Organization:: National Science Foundation (NSF); USDOE Office of Electricity (OE)

Grant/Contract Number:: OE0000875

OSTI ID:: 1961203

Journal Information:: IEEE Transactions on Power Systems, Journal Name: IEEE Transactions on Power Systems Journal Issue: 6 Vol. 36; ISSN 0885-8950

Publisher:: IEEECopyright Statement

Country of Publication:: United States

Language:: English

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