Multi-agent generalized cooperative optimization scheduling for multi-energy complementarity in microgrids

In this paper, we study a collaborative optimization scheduling approach for high-proportion renewable energy smart microgrids to achieve multi-energy management in a distributed execution framework with centralized training. First, we construct a multi-agent distributed microgrid optimization model for this optimization problem based on different types of renewable energy sources, energy storage, power exchange with the upper grid, and time-of-use electricity prices. Then, multiple long-term optimization objectives are designed to transform the cooperative optimization scheduling problem into a multi-agent multi-objective optimization problem, addressing the challenges of dynamic optimization. To enhance the correlation of policy sampling, we propose a novel multi-objective generalized normal distribution optimization (MGNDO) algorithm. By updating the covariance matrix, the policy correlations between different agents are better captured, resulting in more cooperative action sequences. Compared to traditional action sampling methods, this approach can better accommodate complex dynamic constraints and multi-objective requirements. Finally, a smart distribution network connected to three microgrids is taken as an example to realize the cooperative optimal scheduling problem by using the proposed algorithm, MADDPG algorithm and PSO algorithm, respectively. Operational cost and new energy consumption are compared separately to further illustrate the effectiveness of the proposed approach.