Two-stage Adaptive Robust Optimal Scheduling Based on the Interval Probability Uncertainty Set for Microgrids

The randomness of photovoltaic output and the fluctuation of load consumption have a significant impact on the optimal scheduling of microgrids. To this end, a strategic framework of integrated prediction-regulation-decision-making is proposed. Based on Gaussian process regression (GPR), an adaptive robust optimal scheduling model based on the interval probabilistic uncertain sets is established by combining the confidence interval generated adaptively from the historical data of typical days of photovoltaic output and load power consumption with the construction of uncertain sets in robust optimization. First, the fixed term of the uncertain set in the adaptive robust optimal scheduling model is generated by GPR. Then, the fluctuation term in the set is determined by adjusting the risk level considered by the decision-making link, thereby determining the boundary of the uncertain set under different scheduling conservatism. Next, the quality evaluation index of the prediction interval is used to assess the quality of each interval corresponding to each uncertain set. Finally, the test results on the modified IEEE 37-bus microgrid system verifies that the proposed model can effectively resist the fluctuation of photovoltaic output and load power consumption while maintaining low operating costs. © 2024 Chin.Soc.for Elec.Eng.