Optimal Scheduling Strategy for Multiple Virtual Power Plants with Shared Energy Storage

Abstract

The growing penetration of renewable and distributed energy resources increases the need for coordinated scheduling of shared energy storage among multiple virtual power plants (VPPs). This paper proposes a distributed scheduling strategy for shared energy storage among multiple VPPs considering risk-responsibility allocation. A multi-period optimization model is formulated by incorporating local power balance, charging/discharging limits, state-of-charge bounds, and terminal energy requirements. The alternating direction method of multipliers coordinates local storage requests from individual VPPs with the global constraints of the shared energy storage system. Conditional value-at-risk (CVaR) is used to assess tail operational risk caused by net-load forecast errors, and a Shapley-value-based mechanism allocates risk responsibility among VPPs. Peak discharge capacity is used to determine capacity service charges. Case studies show that the proposed method achieves feasible coordinated dispatch, preserves ADMM convergence, and aligns risk charges with VPP-specific risk contributions, thereby supporting the economic and equitable utilization of shared energy storage.