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Grid-connected microgrids comprising renewable energy, energy storage systems and local load, play a vital role in decreasing the energy consumption of fossil diesel and greenhouse gas emissions. A distribution power network connecting several microgrids can promote more potent and reliable operations to enhance the security and privacy of the power system. However, the operation control for a multi-microgrid system is a big challenge. To design a multi-microgrid power system, an intelligent multi-microgrids energy management method is proposed based on the preference-based multi-objective reinforcement learning (PMORL) techniques. The power system model can be divided into three layers: the consumer layer, the independent system operator layer, and the power grid layer. Each layer intends to maximize its benefit. The PMORL is proposed to lead to a Pareto optimal set for each object to achieve these objectives. A non-dominated solution is decided to execute a balanced plan not to favor any particular participant. The preference-based results show that the proposed method can effectively learn different preferences. The simulation outcomes confirm the performance of the PMORL and verify the viability of the proposed method.

Preference based multi-objective reinforcement learning for multi-microgrid system optimization problem in smart grid