规模化电动汽车充放电优化控制研究

发布时间：2018-04-22 23:21

  本文选题：电动汽车 + V2G技术　； 参考：《湖南大学》2016年硕士论文

【摘要】：当今环境日益污染、能源日益缺乏,电动汽车(Electric Vehicles, EVs)作为一种清洁、环保的交通工具,对保护环境,缓解能源短缺,调整能源结构具有重要作用。电动汽车行业具有广阔的发展前景,若大量电动汽车涌入市场,其充电负荷将对配电网的电能质量、经济运行等方面产生不可忽视的影响。因此,研究电动汽车的规模化接入对配电网的影响,提出优化方案对充电负荷进行控制管理具有重要的现实意义。为使电动汽车能更好的融入电力系统,本论文对规模化的电动汽车充放电控制进行研究。首先研究了电动汽车充电负荷的计算方法。在确定了充电负荷的主要影响因素后,建立了充电负荷模型,采用蒙特卡洛法对电动汽车充电负荷进行计算,并对电动汽车充电行为进行模拟。在此基础上,分析了不同情景下电动汽车充电负荷对配电网网损、电压偏移的影响。仿真结果表明,大规模电动汽车的随机接入会造成负荷“峰上加峰”,并且随着渗透率提高,配电网电压偏移明显变大。为了对电动汽车实行较为有效的控制管理,提出了基于聚合商分层管理控制结构的智能充电方法,以电网峰值负荷最小为优化目标建立了智能充电策略模型。研究了不同充电策略下评估电动汽车充电对配电网影响的方法并给出具体步骤和算法流程,通过算例评估无序充电、分时电价充电和智能充电三种充电策略下电动汽车接入后对电网的影响。仿真结果表明,智能充电策略更能有效降低负荷的峰荷,同时获得更好的电压分布和更低的电能损耗,有利于电网的安全经济运行。在地区电网含有风电和光伏机组出力的情形下,探讨了电动汽车平抑负荷波动的充放电策略。提出了基于V2G模式情况下电动汽车与可再生能源的多目标协同调度模型,以合理安排电动汽车的充放电行为。该模型以地区电网等效负荷波动最小和用户充电费用最低为目标函数,兼顾了降低电网负荷峰谷差、促进可再生能源吸纳和提高电动汽车用户相应积极性等方面的需求。通过对目标函数的模糊处理,并应用自适应粒子群优化算法进行求解,得到最优充放电策略。仿真结果验证了模型的有效性和求解方法的可行性。
[Abstract]:Nowadays, the environment is becoming increasingly polluted and energy is increasingly lacking. As a clean and environmentally friendly vehicle, electric vehicles (EVs) play an important role in protecting the environment, alleviating the energy shortage and adjusting the energy structure. The electric vehicle industry has a broad prospect of development. If a large number of electric vehicles pour into the market, its charging load will have a significant impact on the power quality and economic operation of the distribution network. Therefore, it is of great practical significance to study the influence of the large-scale access of electric vehicles on the distribution network and to put forward an optimized scheme to control and manage the charging load. In order to better integrate electric vehicle into power system, this paper studies the charge and discharge control of large-scale electric vehicle. Firstly, the calculation method of electric vehicle charging load is studied. After determining the main influencing factors of charging load, the charging load model is established, and the charging load of electric vehicle is calculated by Monte Carlo method, and the charging behavior of electric vehicle is simulated. On this basis, the influence of electric vehicle charging load on distribution network loss and voltage offset is analyzed. The simulation results show that the random access of large scale electric vehicles will result in the load "peak plus peak", and with the increase of permeability, the distribution network voltage offset is obviously increased. In order to implement more effective control management for electric vehicles, an intelligent charging method based on the hierarchical management control structure of aggregators is proposed. The intelligent charging strategy model is established with the minimum peak load as the optimization objective. The methods of evaluating the influence of electric vehicle charging on distribution network under different charging strategies are studied, and the concrete steps and algorithm flow are given, and the disordered charging is evaluated by an example. The influence of electric vehicle access on power grid under three charging strategies of time-sharing pricing and intelligent charging. The simulation results show that the intelligent charging strategy can effectively reduce the peak load and obtain better voltage distribution and lower power loss, which is beneficial to the safe and economical operation of the power grid. The charging and discharging strategies for suppressing load fluctuation of electric vehicles are discussed in the case of local power grid with wind power and photovoltaic unit output. A multi-objective cooperative scheduling model of electric vehicle and renewable energy based on V2G mode is proposed to reasonably arrange the charging and discharging behavior of electric vehicle. The model takes the minimum fluctuation of equivalent load and the lowest charge cost as the objective function to reduce the peak and valley difference of power grid load, to promote renewable energy absorption and to improve the enthusiasm of electric vehicle users. The optimal charging and discharging strategy is obtained by fuzzy processing of objective function and application of adaptive particle swarm optimization (APSO) algorithm. The simulation results verify the validity of the model and the feasibility of the solution method.
【学位授予单位】：湖南大学
【学位级别】：硕士
【学位授予年份】：2016
【分类号】：TM73
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本文编号：1789409