空间粒子群优化算法及其在电力系统环保经济负荷分配中的应用  

Space particle swarm optimization algorithm and its application in environmental& economic load distribution of power system

作　　者：周任军[1] 李绍金[1] 李红英[1] 康信文 刘乐平[1] 周胜瑜[1] 

机构地区：[1]长沙理工大学智能电网运行与控制湖南省重点实验室,湖南长沙410114

出　　处：《电力自动化设备》2014年第9期7-12,共6页Electric Power Automation Equipment

基　　金：国家自然科学基金资助项目(51277016);湖南省高校创新平台开放基金资助项目(12K074)~~

摘　　要：针对粒子群优化算法易陷入局部最优、收敛过早的缺陷,提出一种空间粒子群优化算法,通过附加一类高度参数,使粒子移动的方向和距离由单一速度决定转变成还受高度作用,构成位置、速度、高度三维参数空间,从而降低了计算结果的随机性。将该算法用于求解电力系统经济负荷分配问题,在传统经济负荷分配考虑燃料成本的基础上,综合考虑由机组排放污染气体所产生的环境成本。仿真结果表明,相比经典粒子群优化算法和改进粒子群优化算法,空间粒子群优化算法有较强的全局搜索能力和更可靠的优化计算结果,在解决非线性、非凸性、不连续优化问题中具有有效性和优越性。Aiming at the local optimum and premature convergence of PSO（Particle Swarm Optimization） algorithm,a kind of SPS0（Space Particle Swarm Optimization） algorithm is proposed,which,with an additional parameter kind of height ,makes the moving direction and distance of particle not only depend on the speed but also the height. A three-dimensional parameter space of position,speed and height is thus built to decrease the randomness of calculative result. Based on the traditional economic load distribution,the proposed algorithm is applied to the economic load distribution of power system,which comprehensively considers the fuel cost,as well as the environmental cost caused by the gas pollutant discharge of unit. Simulative results show that,compared with the classic PSO algorithm and improved PSO algorithm,the SPSO algorithm has better global searching ability and more reliable optimization results,verifying its effectiveness and superiority in the nonlinear,non-convex and discrete optimization.

关 键 词：粒子群优化算法 寻优空间 高度参数 收敛性能 环保经济负荷分配 负荷管理 优化 模型 

分 类 号：TM73[电气工程—电力系统及自动化]