Joint flexibility-risk managed distributed energy trading considering network constraints and uncertainty

This paper addresses the operational challenges in distribution systems caused by the integration of fluctuating renewable energy sources and rising peak loads from widespread electrification. We propose a new distributed energy coordination framework that facilitates seamless interoperation between peer-to-peer (P2P) energy trading and the operation of distributed flexibility resources. This framework ensures network constraints are respected while promoting Energy Trading Consistency (ETC). Our approach utilizes a two-stage dynamic energy and flexibility sharing mechanism. It achieves energy efficiency through demand-management and P2P energy trading in the day-ahead (DA) stage, followed by adjustments of flexible resources and flexibility sharing through a receding-horizon feedback mechanism inspired by model predictive control (MPC) in the real-time (RT) stage. High-risk scenarios and computational overhead are managed by employing conditional value-at-risk (CVaR) measures, as well as approximation of constraints through dual robust reformulation and linear decision rules. To uphold the autonomy and privacy of prosumers, we propose distributed algorithms based on the fast consensus alternating direction method of multipliers (ADMM). Numerical results demonstrate that our framework achieves a 20% reduction in social cost while providing a coherent and interactive P2P energy trading environment, facilitating reliable network operation under high penetration of renewable energy sources without third-party intervention.