电网技术2011，Vol.35Issue(7)：139-144,6.
采用改进型多目标粒子群算法的电力系统环境经济调度
Economic-Environmental Dispatch Using Improved Multi-Objective Particle Swarm Optimization
摘要
Abstract
Multi-objective economic\environmental dispatch demands that the pollutant emission of power plants should reach minimum while the condition of least generation cost should be satisfied. According to this demand, this multi-objective problem is solved by improved particle swarm optimization (PSO) algorithm based on Pareto Dominant strategy and crowding distance ordering. Current Pareto optimal solution is stored in external achieve assembly with dynamic adjustable capacity, and the optimal position of individual is determined by Pareto dominant strategy, and then according to the value of crowding distances of each particle the global optimal position is determined, besides by means of setting dynamic inertia weight and leading in small probability of mutation the searching ability of the proposed algorithm is enhanced. The effectiveness of the proposed algorithrm is verified by calculation example.关键词
环境经济调度/多目标粒子群/拥挤距离/Pareto 最优前沿/小概率变异Key words
economic environmental dispatch/ multi-objective particle swarm optimization (PSO)/ crowding distance/ Pareto optimal front/ mutation with small probability分类
信息技术与安全科学引用本文复制引用
刘刚,彭春华,相龙阳..采用改进型多目标粒子群算法的电力系统环境经济调度[J].电网技术,2011,35(7):139-144,6.基金项目
江西省自然科学基金资助项目(2009GZS0016) （2009GZS0016）
江西省教育厅科技基金资助项目(GJJ10455). （GJJ10455）
