考虑V2G下电动汽车与风电协同调度的多目标优化策略  

Multi-objective optimization strategy for cooperative scheduling of electric vehicles and wind farms under V2G

作　　者：张娜 唐忠 Zhang Na;Tang Zhong(School of Electrical Engineering,Shanghai University of Electric Power,Shanghai 200082,Chin)

机构地区：[1]上海电力学院电气工程学院,上海200082

出　　处：《电测与仪表》2018年第12期54-59,87,共7页Electrical Measurement & Instrumentation

基　　金：国家自然科学基金资助项目(51607111);中国工程院2016年国家战略咨询项目(2016-XZ-29-02)

摘　　要：通过建立电动汽车及风电参与的负荷平抑、负荷峰谷差和电动汽车充放电费用的多目标模型,考虑电动汽车电池的可用容量和充放电功率等约束条件的情况,采用基本遗传算法和非线性规划遗传算法这两种不同算法,分析考虑负荷峰谷差对平抑负荷波动和提高电动汽车用户收益产生的影响,并分别对所产生结果进行对比。最后,通过算例分析验证结果表明,通过在分时电价合理的安排电动汽车充放电下采用非线性规划遗传算法并考虑负荷峰谷差可使多目标模型更加优化,并给出非线性遗传算法求解多目标模型时的结果曲线图。The multi-objective model of the electric vehicle and wind power in the load control is established,which stabilizes the peak and valley difference of load and electric vehicle charging and discharging cost. Considering the electric vehicle battery available capacity and charge discharge power constraints,as well as the basic genetic algorithm and genetic algorithm for nonlinear programming of the two different algorithms,this paper analyzes the influence of peak load difference on stabilizing the load fluctuation and improving the return of electric vehicle users,and the generated results are compared respectively. Finally,through the example analysis results show that the multiobjective model can be more optimized through the reasonable price in the electric vehicle charging and discharging and genetic algorithm for nonlinear programming and considering the peak and valley load difference,and the results curve of a genetic algorithm for solving multi-objective nonlinear model is presented.

关 键 词：电动汽车 风电并网 协同调度 多目标优化 非线性遗传算法 

分 类 号：TM93[电气工程—电力电子与电力传动]