Multi-objective Optimal Scheduling of Microgrid Integrated Energy Storage under Uncertain Scenarios

The development of microgrids aims to meet the basic requirements of power supply while improving economic and environmental protection benefits. Energy storage devices play an important role in balancing power fluctuations of microgrids caused by renewable energy sources and load uncertainty. This paper proposed a multi-objective scheduling method for microgrids contain energy storage to minimize both operation and maintenance costs as well as environmental pollution costs. An improved non-dominated sorting genetic algorithm II (NSGA-II) is proposed to solve the model. A penalty ranking mechanism is introduced in the improved NSGA-II algorithm, which is able to reach the optimal solution faster. Meanwhile, a fast forward elimination way based on probabilistic distance is used to obtain renewable energy typical scenarios. Simulation results verify the viability of the proposed method.