Data-Driven Resource Planning for Virtual Power Plant Integrating Demand Response Customer Selection and Storage

Battery energy storage (BES) and demand response (DR) are two important resources to increase the operational flexibility of a virtual power plant (VPP) and thus reduce its economic risks in the market. This paper develops a data-driven approach for VPP resource planning (VRP), in which BES sizing and DR customer selection are optimized synergistically to maximize VPP profit in the electricity market. Heterogeneity in DR potential across individual customers are considered in the planning framework by utilizing the knowledge learnt from smart meter data. The overall VRP problem is formulated by a risk-managed, multistage stochastic programming framework to address the uncertainties from the intermittent renewable energy sources, load demands, market prices and DR resources. Case studies under two market imbalance settlement schemes demonstrate that jointly optimizing BES and DR customer selection leveraging the smart meter data can improve the VPP expected profit under both market settings