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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。
The mathematic model of electric vehicle participating in load reduction is established. The particle swarm optimization (PSO) is used to solve the model, considering the constraint of electric vehicle charging and discharging power and available capacity. Aiming at the defect that PSO deals with the premature local convergence of high-dimensional problems, this paper proposes an improved PSO algorithm based on sub-vectors. While ensuring that the algorithm searches for each region in space, the search space is decomposed into a number of low-dimensional small spaces to search , To avoid premature convergence of the algorithm. Finally, an example is given to demonstrate the feasibility of rationally arranging the load and discharge of electric vehicles to reduce load. At the same time, the comparison between the performance of two algorithms of basic PSO and PSO shows that the latter is more effective in dealing with high-dimensional problems.