风电与电动汽车协同并网的电力系统动态环境经济多目标模糊优化调度模型

Multi-objective Fuzzy Optimization Model of Power System Dynamic Environmental and Economic Scheduling for Cooperative Grid-connection of WindPower and Electric Vehicle

为协调电动汽车与风电并网给电力系统经济调度带来的影响,构建了考虑插电式混合电动汽车入网的含风电场电力系统环境经济多目标调度模型。而风力发电和负荷的不确定性因素必将增加调度难度,为此应用模糊理论实现风电与电动汽车协同并网的模糊化建模,利用满意度指标将确定性环境与经济的多目标问题转化为单目标优化问题,在采用线性下降搜寻思路和考虑边界约束的粒子信息分享方法对传统粒子群算法进行改进的基础上,采用改进粒子群优化(particle swarm optimization,PSO)算法求解所提出的动态环境与经济调度模型,从电网运行和车主需求角度出发,考虑了系统旋转备用和车主日常出行等制约关系。经过仿真以及对不同场景下多个调度方案的对比分析,验证了模型的合理性和以及改进PSO算法对解决此类动态调度问题的优越性。

In order to coordinate influence of grid-connection of electric vehicles（EVs）and wind power on economic scheduling of the power system,this paper constructs a multi-objective environmental and economic scheduling model for the power system with wind power considering PHEVs.In view of difficulty in scheduling caused by uncertain factors of wind power generation and load,the paper applies fuzzy theory in realizing fuzzy modeling of cooperative grid-connection of wind power and EVs and uses satisfaction indicators to transform the multi-objective problem of deterministic environment and economy into the single-objective optimization problem.On the basis of improving the traditional particle swarm algorithm by using linear decrease search idea and particle information sharing method considering boundary constraint,it adopts the improved particle swarm optimization（PSO）algorithm to solve the proposed dynamic environmental and economic scheduling model.From points of power grid operation and demands of owners,it also considers restrictive relationships such as system rotational reserves and daily travel of owners,and so on.By means of simulation and comparing and analyzing different scheduling plans in different scenarios,it verifies reasonability of the model and superiority of the improved PSO algorithm in solving this kind of dynamic scheduling problem.