A Novel Data-Driven Method for Behind-the-Meter Solar Generation Disaggregation With Cross-Iteration Refinement
Keda Pan, Zhaohua Chen, Chun Sing Lai, Changhong Xie, Dongxiao Wang, Zhuoli Zhao, Xiaomei Wu, Ning Tong, Loi Lei Lai, Nikos Hatziargyriou
Abstract
Photovoltaic (PV) generation is increasing in distribution systems following policies and incentives to promote zero-carbon emission societies. Most residential PV systems are installed behind-the-meter (BTM). Due to single meter deployment that measures the net load only, this PV generation is invisible to distribution system operators causing a negative impact on the distribution system planning and local supply and demand balance. This paper proposes a novel data-driven BTM PV generation disaggregation method using only net load and weather data, without relying on other PV proxies and PV panels’ physical models. Long Short-Term Memory (LSTM) is employed to build a generation difference fitted model (GDFM) and a consumption difference fitted model (CDFM) derived from weather data. Both difference fitted models are refined by a cross-iteration with mutual output. Finally, considering the photoelectric conversion properties, the disaggregated generation results are acquired by the refined GDFM of changing input. The proposed method has been tested with actual smart meter data of Austin, Texas and proves to increase the disaggregated accuracy as compared to current state-of-the-art methods. The proposed method is also applicable to disaggregate BTM PV systems of different manufacturing processes and types.