A Novel Approach for Modeling Spatial and Temporal Correlations of PV Generation Applied to Prosumer Virtual Power Plants

Virtual Power Plants (VPPs) play a crucial role in the increasing grid and market integration of distributed energy resources. However, as VPP complexity and geographic scale increase, the variable nature of PV generation makes accurate prediction of the VPP aggregate output challenging. The problem is further exacerbated by the paucity of smart meter data, making some prosumers unobservable to the VPP aggregator. To address these challenges, we propose a Bayesian Vector Autoregression approach to quantify the generation variance of a VPP consisting of an aggregation of residential rooftop PV systems spread over a large geographical area. The key idea is to generate synthetic data for unobserved prosumers using observed prosumers' local correlation microstructure and temporal cross-correlation, allowing the simulation of the VPP output. We demonstrate the efficacy of the proposed approach by using two different Australian data sets. We study two use cases, considering the impact of the level of prosumer observability and the VPP size on the variance of the aggregate VPP output. Compared to the existing approaches, the proposed nonparametric approach provides a more accurate forecast of VPP output variance, allowing the VPP operator to manage market risk better.