Volume 02: Proceedings of 11th International Conference on Applied Energy, Part 1, Sweden, 2019

Coordinated Scheduling of Multi-Energy Micro-grid Based on Multi-agent Game Theory and Reinforcement Learning Ge Shaoyun, Li Jifeng*, Liu Hong, Lu Zhiying, Yang Zan, Yan Jun


Since the deep integration and close interaction of multiple energy networks have increased the complexity of optimized scheduling of multi-energy microgrids, the present manuscript proposed a coordinated scheduling model in accordance with multiagent game theory and reinforcement learning. First, a multi-agent division was conducted. Second, a multiagent economic decision-making model and a game decision model were constructed. Thus, a coordination scheduling method was proposed in accordance with Nash game theory and the Q-learning algorithm. Finally, the efficiency and effectiveness of the proposed method were validated, which were verified by real-world case studies.

Keywords Multi-energy microgrid, coordinated scheduling, multi-agent, game theory, Nash Q learning

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