Scalable and privacy-preserving distributed energy management for multimicrogrid

Abstract

Distributed microgrids are being deployed into our power grids to form large-scale multimicrogrid systems for utilizing growing renewable energy sources. An effective energy management strategy is fundamental to balancing energy supply and demand alongside maintaining the stability of multimicrogrid. In this article, we propose a scalable, privacy-preserving, distributed energy management approach (SPDEM) for multimicrogrid. Specifically, we first formulate the energy management problem in multimicrogrid as a decentralized partially observable Markov decision process (Dec-POMDP). Next, we develop an intelligent energy management algorithm using mean-field multiagent recurrent reinforcement learning to efficiently solve the Dec-POMDP. This approach incorporates a novel fingerprint-based importance sampling technique to address the obsolete experiences induced by mean field approximation. Extensive experiments on real-world datasets demonstrate that SPDEM can make effective energy management decisions under variable renewable energy generation and load demand. Comparisons with five typical baselines illustrate the superb performance of SPDEM in cost reduction and scalability enhancement.