Best Response Learning Assisted Asynchronous ADMM for Real-Time Energy Sharing Under Communication Delay

Energy sharing can activate prosumer flexibility to support power system operations. However, in practice, communication delays are inevitable and seriously threaten the real-time energy sharing equilibrium. While many asynchronous methods aim to address this, they ignore the learnability of prosumer’s response and often struggle to balance convergence speed and stability. To tackle this issue, this paper proposes a novel asynchronous algorithm based on best response (BR) learning. First, we demonstrate that the prosumer’s best response in ADMM-based energy sharing can be represented as the piecewise linear function. Besides, we propose a batch-fitting method to learn the BR function with high accuracy and low computational cost. Building on this, we develop an asynchronous ADMM algorithm with a practical error criterion to ensure strict convergence of energy sharing under severe delays. Numerical studies indicate that the proposed algorithm can achieve faster convergence with fewer iterations and greater reliability across various energy sharing scales and asyn-chronous rates than existing asynchronous algorithms.