基于改进粒子群算法的电动车参与负荷平抑策略  

An Improved Particle Swarm Optimization-Based Load Response Strategy With Participation of Vehicle to Grid

作　　者：韩海英[1] 和敬涵[1] 王小君[1] 姜久春[1] 田文奇[1] 

机构地区：[1]北京交通大学电气工程学院,北京市海淀区100044

出　　处：《电网技术》2011年第10期165-169,共5页Power System Technology

摘　　要：建立了电动车参与负荷平抑的数学模型,在考虑电动车充放电功率及可用容量等约束条件的前提下,应用粒子群优化算法(particle swarm optimization,PSO)对模型进行了求解。针对PSO处理高维问题过早局部收敛的缺陷,提出了基于子向量的改进型PSO算法,在保证算法搜索到空间中的每个区域的同时,将搜索空间分解为若干低维小空间进行搜索,避免了算法过早局部收敛。最后,文章通过算例验证了合理安排电动车充放电平抑负荷的可行性,同时通过基本PSO与改进型PSO 2种算法性能的对比,证明了后者在处理高维问题时更有效。Along with application and dissemination of electric vehicles and the construction of charging stations, as a movable and distributed energy storage unit of power grid special attentions are paid to the electric vehicle. A mathematical model, in which the electric vehicle participates load response of power grid, is built. Considering the constraints such as charging and discharging power of the electric vehicle and its available capacity, the proposed mathematical model is solved by particle swarm optimization （PSO） algorithm. In view of the defect of traditional PSO algorithm that it converges prematurely and locally while it is used to deal with high-dimensional problems, an improved PSO algorithm based on sub-vector is put forward; to ensure the ergodic search in the space to be searched, the space is divided into several small low-dimensional spaces to avoid the premature convergence of the algorithm. Finally, the feasibility of reasonably arranging the load response with participation of vehicle to grid （V2G） is verified by calculation example; meanwhile the performance contrast between traditional PSO algorithm and improved PSO algorithm, it is proved that the latter is more effective for dealing with high-dimensional problems.

关 键 词：电动车-电网互动技术 粒子群优化算法 负荷平抑 电动汽车 

分 类 号：TM712[电气工程—电力系统及自动化]