Receding-Horizon Optimization for Microgrid Energy Management
Taking a certain Sci-tech Park's microgrid in Shanghai as example, an energy source management optimization method, in which three electric power resources such as distributed generation, energy storage system and shiftable load are included, is proposed. In the proposed method firstly the output of renewable energy sources are fully consumed by the load in the park; then the controllable DGs, energy storage system and shiftable load are utilized to perform the second round optimization for the load that has been reduced. Considering the fact that there are a lot of non-linear programmings in the optimization problem, a decomposition iteration algorithm, which independently solves three kinds of controllable resources such as DG, load and energy storage by particle swarm optimization (PSO) to make the solutions of the three kinds of controllable resources closed to globally optimal solution through iterations, is put forward. Besides, in allusion to the difficulty in load prediction due to the randomness of the load in microgrid, a receding horizon optimization method is given to improve the accuracy and real-time of global optimization. The effectiveness of the given method is validated by the results of case calculation.
国家科技期刊平台
NSTL
万方数据
维普
