A microgrids energy management model based on multi-agent system using adaptive weight and chaotic search particle swarm optimization considering demand response

With the deepening of China's low-carbon energy transformation, microgrids are an essential means to promote the consumption of renewable energy. Therefore, improving the operational efficiency of microgrids is the key to promote the development of renewable energy. This paper establishes a three-layer Multi-Agent system model considering the energy storage system and power-heat load demand response based on the actual situation of China to solve the problem of microgrids energy management. In order to verify the effect of the energy storage system and demand response in microgrids, this paper designs three simulation cases, namely the underlying case, energy storage case and demand response case. In order to prove the efficiency of the proposed method, this paper applies the proposed method to solve three cases and compare the result with other meta-heuristic algorithms. The comparison results show that: (1) Multi-Agent system model can realize the collaborative optimization of 'source, grid, load, and storage.' (2) The introduction of the energy storage system and demand response in microgrids can stabilize the output of renewable energy units, promote renewable energy consumption and reduce the overall operating cost of microgrids. (3) The proposed particle swarm optimization can effectively reduce the cost and the number of iterations. (C) 2020 Elsevier Ltd. All rights reserved.