Abstract: To address the uncertainties in renewable energy and load within islanded microgrids and to reduce carbon emissions, this paper proposes a data-driven and temporal correlation robust optimization algorithm for microgrids. Typical scenarios are generated by clustering method, and then a hybrid scenario set including typical, extreme, and predicted scenarios is constructed. In the day-ahead stage, the hybrid scenario set is added with constraints, and the probability-weighted index of the microgrid operation cost corresponding to the hybrid scenario set is used as the objective function, and a stepwise carbon trading model is introduced into the cost to obtain the pre-dispatch solution.Temporal correlation is introduced in the robust testing phase to speed up its testing as well as efficiency to ensure that all scenarios are feasible.In the intraday stage, the measured new energy and load data are used to optimize and adjust the optimization solution in the preday stage to obtain the optimal output of the microgrid equipment, so as to improve the economy and robustness of the microgrid, and the simulation results verify the effectiveness of the method.

    Key words: data-driven; hybrid scenario set; temporal correlation; robust optimization