混合储能微电网并网调度多目标灰熵烟花算法

Multi-Objective Grey Entropy Fireworks Algorithm for Grid-Connected Scheduling of Hybrid Energy Storage Microgrid

针对混合储能微电网调度优化问题,建立并网状态下经济收益、污染处理费用的混合储能微电网多目标优化模型.以基本烟花算法为框架,结合灰熵并行分析理论,提出一种多目标灰熵烟花算法.所提算法通过分配给模型的两个目标不同的熵值权重,有效处理不同目标间的冲突性.以灰熵并行关联度作为烟花算法的适应度选择优秀烟花个体,引导其向更优区域进化搜索.仿真结果表明,所提多目标灰熵烟花算法的性能要优于基于随机权重和基于Pareto支配的烟花算法,且优于经典的NSGA-Ⅱ多目标算法,验证了所建多目标模型及所提多目标算法的有效性.

Aiming at the scheduling optimization problem of hybrid energy storage microgrid, a multi-objective optimization model with economic benefit and pollution treatment cost under grid-connected state is established. Based on the basic fireworks algorithm and the grey entropy parallel analysis theory, a multi-objective grey entropy fireworks algorithm is proposed. The proposed algorithm can effectively handle the conflict relationship between different objectives by assigning different entropy weights to the two studied objectives. The grey entropy parallel correlation degree is adopted as the fitness of fireworks algorithm to select excellent individuals and guide the algorithm to better search region. Simulation results show that the performance of the proposed multi-objective grey entropy fireworks algorithm is significantly better than that of the random weight-based and Pareto-based fireworks algorithm, and better than that of the classical NSGA-Ⅱ algorithm, which verifies the effectiveness of the established multi-objective model and proposed multi-objective algorithm.