采用多目标随机黑洞粒子群优化算法的环境经济发电调度

Environmental Economic Dispatching Adopting Multiobjective Random Black-hole Particle Swarm Optimization Algorithm

将多目标随机黑洞粒子群优化(multiobjective random black-hole particle-swarm optimization,MORBHPSO)算法用于解决环境经济发电调度问题,对燃料发电机组相互冲突的燃料费用函数和污染气体排放量函数同时进行优化。提出带等式约束的帕累托占优条件,使生成的帕累托(Pareto)最优解集在解的可行区域,并采用新的"聚类技术"减少解集中解的个数以加快寻优速度。通过变异操作改善解的多样性,并根据"距离评价指标"从帕累托最优前沿(Pareto optimal front,POF)中选择折衷最优解。对IEEE 30节点的标准测试系统进行仿真计算,结果表明该算法在解决环境经济调度问题方面的可行性和有效性,减少了迭代次数,而且在不增加污染气体排放量的同时降低了燃料费用。

Multiobjective random black-hole particle- swarm optimization （MORBHPSO） algorithm is proposed to solve environmental economic dispatching （EED） problems. The conflicting fuel cost and pollutants emissions objective functions of the generation unit are optimized simultaneously. The Pareto dominance condition with the equality constraint is presented to guarantee the feasibility of solutions in the Pareto-optimal set. Due to using a novel clustering technique, the size of the Pareto-optimal set decreases so as to converge fast. Mutation was applied to increase the diversity of the solutions, and the distance evaluation index was developed to select the best compromise solution from the Pareto optimal front （POF）. The MORBHPSO algorithm was carried out on a standard IEEE 30-bus test system. The results demonstrate the feasibility and effectiveness of the algorithm for solving EED problems with less iteration, and the fuel cost can be decreased without increasing the pollutants emissions.