Multi-time scale energy management of multi-microgrid systems considering energy storage systems: A multi-objective two-stage optimization framework

Abstract

This paper proposes probabilistic energy management for the operation planning of multi-microgrid (MMG) systems. The proposed model considers both day-ahead and real-time scheduling to provide an effective framework in the presence of renewable energy sources. Therefore, a multi-objective two-stage (MOTS) model has been introduced that studies the day-ahead scheduling at the first stage. In this stage, the MMG tries to simultaneously optimize the operating cost, emission pollution, and system reservation. Due to the uncertain behavior of RES, the demand response programs, battery energy storage system, and controllable generation units have been integrated into the model to cover the generation fluctuation. To enhance the flexibility of the MMG system, the proposed model maximizes the stored energy in the battery energy storage system to discharge it when needed. Therefore, the first stage has been modeled as the multi-objective optimization that is handled by the fuzzy-decision-making approach. In the second stage, the first scheduling is updated in real-time every 15 min to reduce the imbalance costs. The performance of the proposed model has been evaluated by several case studies and the results show that the emission of greenhouse gases and MMG reserve have been decreased by 174.12 kg and 543.22 kWh, respectively.