Market Clearing Model for Energy-Constrained Virtual Power Plants with Uncertainty Based on Distributionally Robust Chance-Constrained Optimization

With the increasing number of distributed flexible resources with energy storage capabilities in virtual power plants (VPPs), the traditional market clearing model that only includes quantity and price bids cannot fully unlock their potential flexibility. In light of this, we propose a market clearing model for energy-constrained virtual power plants (EC-VPPs) based on distributionally robust chance-constrained optimization (DRCCO) with moment information. Furthermore, to address the uncertainty of EC-VPPs in the electricity market, a pricing strategy for EC-VPPs is proposed. This strategy helps quantify the impact of uncertainty in EC-VPPs on the system economy. The proposed market clearing model is reformulated as a tractable mixed-integer second-order cone programming (MISOCP) problem via a two-sided distributionally robust chance-constrained convex reformulation method. Numerical simulations verify that the proposed pricing strategy offers fair incentives for both reserve providers and uncertain sources, and delivers an effective market mechanism for the EC-VPPs.