Economic optimization of microgrid with demand response under source-load uncertainty

The installed capacity of renewable energy producers has increased, which has accelerated the development of the microgrid system. One of the most significant and difficult issues in the field of microgrids is economic optimization. The reliability of the microgrid is threatened by the unpredictability of renewable energy and the variety of load types. In this study, a two-layer microgrid demand response optimization model that takes into account source-load uncertainty. To address the instability of renewable energy and load demand, this study introduces a hybrid scenario reduction strategy that combines Latin Hypercube sampling and probability distance. Then, a two-layer model is developed an upper optimization model that aims to minimize the demand response cost and a lower optimization model that aims to minimize the overall system cost. Finally, the paper then suggests a teaching-learning crow search algorithm that combines the teaching and learning optimization algorithm with the crow search method to resolve the two-layer optimization model. The simulation results demonstrate the effectiveness of the two-layer optimization model and the superiority of the suggested algorithm in optimizing the economic operation of microgrids.