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The integration of individual microgrids (MGs) into Microgrid Clusters (MGCs)significantly improves the reliability and flexibility of energy supply,through resource sharing and ensuring backup during outages. The dispatch ofMGCs is the key challenge to be tackled to ensure their secure and economicoperation. Currently, there is a lack of optimization method that can achieve atrade-off among top-priority requirements of MGCs' dispatch, including fastcomputation speed, optimality, multiple objectives, and risk mitigation againstuncertainty. In this paper, a novel Multi-Objective, Risk-Sensitive, and OnlineTrust Region Policy Optimization (RS-TRPO) Algorithm is proposed to tackle thisproblem. First, a dispatch paradigm for autonomous MGs in the MGC is proposed,enabling them sequentially implement their self-dispatch to mitigate potentialconflicts. This dispatch paradigm is then formulated as a Markov Game model,which is finally solved by the RS-TRPO algorithm. This online algorithm enablesMGs to spontaneously search for the Pareto Frontier considering multipleobjectives and risk mitigation. The outstanding computational performance ofthis algorithm is demonstrated in comparison with mathematical programmingmethods and heuristic algorithms in a modified IEEE 30-Bus Test Systemintegrated with four autonomous MGs.

Cooperative Dispatch of Microgrids Community Using Risk-Sensitive Reinforcement Learning with Monotonously Improved Performance