Fully Decentralized Approximate Dynamic Programming for Stochastic Energy Management of a Networked Microgrid System

Abstract

This article develops a fully decentralized approximate dynamic programming (FD-ADP) algorithm for stochastic energy management (SEM) of a networked microgrid (NMG) system. First, considering the ac power flow constraints, an alternating direction method of multipliers (ADMM)-based decentralized SEM framework is proposed for NMG coordination. Then, a transactive energy scheme is introduced to further decouple each microgrid (MG) optimization for privacy enhancement and computation reduction. Next, a FD-ADP algorithm is proposed to cope with the real-time uncertainties. The piecewise linear function (PLF) is employed for value function approximation, and a fully decentralized PLF slope update method based on ADMM framework is designed for decentralized property preservation, which trains the value function just through each MG local information and neighboring communication, thus the well-trained decentralized PLF slopes can help achieve the global optimal SEM strategy for NMG coordination under stochastic environments. Finally, case studies demonstrate the effectiveness of the proposed ADMM-based FD-ADP algorithm in terms of decentralized optimization, decentralized training, and global optimality.