大规模电动汽车接入配电网的运行优化研究

发布时间：2018-04-14 15:44

本文选题：电动车 + 峰谷差　；参考：《北方工业大学》2017年硕士论文

【摘要】：随着电动汽车技术的飞速发展,尤其是电池储能技术的突破,以及国家对于电动汽车产业的政策扶持,未来电动汽车势必会规模化运行。大量电动车集群充电会给现有电网运行带来巨大压力,因此研究一种有效的控制或引导策略对电网经济运行具有积极意义。首先分析了各类电动车的行驶习惯,建立了各类电动车用于随机抽样的开始充电时间和初始荷电状态(SOC)的概率模型,采用蒙特卡罗(Monte Carlo)法模拟了某区域各类电动车的日负荷曲线。以私家车为研究对象,仿真了 IEEE33节点系统内不同渗透率电动车充电对配网的影响。结果显示随着渗透率的提高,负荷峰值、峰谷差被逐渐拉大,电压偏移加大,网损率也有小幅度增加。其次分析了模糊聚类算法的原理和实现过程,采用该算法将负荷曲线划分为峰谷两个时段。构建了以用户充电成本最小化为目标的优化模型,各台车的开始充电时间为控制变量,采用多种群遗传算法(MPGA)对模型进行了求解。优化结果显示该策略可以起到良好的削峰填谷作用,改善电压水平、提高电压合格率,降低系统网损率,实现配电网安全经济运行。最后分析了用户在不同电价刺激下对峰谷电价(TOU)的响应比例,统筹考虑供电公司与用户双方的利益,建立了包括最小化峰谷差率、最大化用户用电方式满意度及费用满意度的多目标优化模型。采用NSGA-II算法对谷时段位置及峰谷两时段电价进行了寻优,从最优解集中选取一个折中解进行分析。结果表明所制定的TOU方案可以充分引导用户在谷时段进行充电,实现有效降低充电负荷对配电网影响的目标。
[Abstract]:With the rapid development of electric vehicle technology, especially the breakthrough of battery energy storage technology, as well as the national policy support for the electric vehicle industry, the future electric vehicle is bound to run on a large scale.A large number of electric vehicle cluster charging will bring great pressure to the current power grid operation, so it is of positive significance to study an effective control or guidance strategy for the economic operation of the power grid.Firstly, the driving habits of all kinds of electric vehicles are analyzed, and the probabilistic models of the start charge time and initial charge state of all kinds of electric vehicles for random sampling are established.Monte Carlo Monte Carlo method is used to simulate the daily load curve of various electric vehicles in a certain area.Taking private cars as the research object, the influence of electric vehicle charging with different permeability on distribution network in IEEE33 node system is simulated.The results show that with the increase of permeability, the peak load peak value, peak and valley difference are gradually enlarged, the voltage offset increases, and the loss rate increases slightly.Secondly, the principle and implementation process of fuzzy clustering algorithm are analyzed, and the load curve is divided into peak and valley periods.An optimization model with the aim of minimizing the cost of charging is constructed. The starting charging time of each vehicle is taken as the control variable. The model is solved by multi-population genetic algorithm (MPGA).The optimization results show that the strategy can play a good role in peak cutting and valley filling, improve voltage level, increase the qualified rate of voltage, reduce the rate of system network loss, and realize the safe and economical operation of distribution network.Finally, the paper analyzes the response ratio of customers to peak-valley price under different electricity price stimuli, considering the interests of both power supply companies and customers as a whole, and establishes a minimum peak-valley difference ratio.A multi-objective optimization model to maximize customer satisfaction and cost satisfaction.In this paper, the NSGA-II algorithm is used to optimize the electricity price of the valley and the peak and valley, and a compromise solution is selected from the optimal solution set to analyze it.The results show that the proposed TOU scheme can fully guide users to charge in the valley and achieve the goal of effectively reducing the impact of charging load on distribution network.
【学位授予单位】：北方工业大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM732

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