Optimal probabilistic scenario-based operation and scheduling of prosumer microgrids considering uncertainties of renewable energy sources

Abstract

Uncertainties of renewable energy sources (RESs) such as wind turbine (WT) and photovoltaic (PV) units are one of the considerable challenges of prosumer microgrids (PMGs) for the optimal day-ahead operation. In this study, a new probabilistic scenario-based method of optimal scheduling and operation of PMGs is developed. In this regard, different scenarios are generated using Monte Carlo Simulations (MCS). Furthermore, k-means, k-medoids, and differential evolution algorithms (DEA) are deployed to cluster the scenarios in the proposed method. A realistic commercial PMG in Iran is selected to apply the introduced method. The validity of the developed probabilistic optimization method for PMG operation is examined by comparing the results under various scenario reduction algorithms and MCS ones. The comparison of the obtained results and those of other existing deterministic methods highlights the advantages of the presented method. Furthermore, the sensitivity analyses are carried out to investigate the robustness of the developed method against the increase in the system uncertainty level. According to the test results, it is concluded that the k-medoids algorithm has the best performance in comparison with the k-means and the DEA-based clustering under various conditions.