微网经济运行下电动汽车充放电策略研究

发布时间：2018-05-17 05:32

本文选题：微网 + 电动汽车　；参考：《长沙理工大学》2015年硕士论文

【摘要】：目前全球正面临着电力需求的快速增长、能源短缺和环境恶化之间日益剧烈的矛盾,为此分布式发电技术得到了广泛关注。但分布式发电的多样性,间接性和复杂性限制了其大规模发展。微网作为智能电网建设的重要组成部分,可以有效解决分布式发电大规模接入带来的问题,同时提升其与大电网互联的可靠性和灵活性。此外,随着电动汽车的广泛使用,其充放电行为具有随机性和分散性,给电网的安全稳定运行带来新的挑战。论文首先以家用电动汽车为研究对象,基于传统汽车车主的出行习惯和行驶特征的调查统计,建立了电动汽车接入电网时间和日行驶距离的数学模型。在此基础上,提出了两种电动汽车充放电策略:峰谷模式,指在系统负荷高峰和低谷时控制电动汽车充电和放电;智能模式,指根据各时段微网系统内机组可发最大功率和负荷的差额,控制电动汽车的充放电功率,以提高微网系统内负荷与出力的匹配度。接着,统筹考虑微网运行经济性和电动汽车充放电效益,基于双层优化理论,提出了电动汽车充放电策略双层优化模型,上层模型以微网系统运行成本最小为目标,下层模型以电动汽车充放电效益最大为目标。针对双层优化NP难问题,采用KKT(Karush-Kuhn-Tucker)条件将双层优化问题转换成一个单层带平衡约束的数学优化模型进行求解。算例结果表明论文所提充放电策略能够降低微网的运行成本和电动汽车的充放电成本,验证了论文所提模型的有效性和可行性。在此基础上,引入了车辆路径问题,以物流配送电动汽车为研究对象,提出了考虑微网经济运行的电动汽车行驶路径和充放电策略协同优化模型。该模型根据风电机组各时段的出力预测值、用户负荷预测值以及配送公司的配送任务,对电动汽车行驶路径、快充时长以及各时段的充放电功率进行决策,以实现微网能量交互成本、电动汽车快速充电成本和电池损耗成本的微网综合运行成本最小。采用混合编码的自适应帝国主义竞争算法对模型进行求解,算例结果表明,论文所提方法可以在电动汽车完成配送任务的同时,有效提升微网对可再生能源发电的消纳能力,大幅降低微网运行成本。
[Abstract]:At present, the world is facing the rapid growth of power demand, energy shortage and environmental degradation increasingly fierce contradiction, so distributed generation technology has been widely concerned. However, the diversity, indirectness and complexity of distributed generation limit its large-scale development. As an important part of smart grid construction, microgrid can effectively solve the problem of large-scale access to distributed generation, and improve the reliability and flexibility of interconnection with large power grid. In addition, with the wide use of electric vehicles, their charge-discharge behavior is random and decentralized, which brings new challenges to the safe and stable operation of power grid. Firstly, based on the investigation and statistics of the travel habits and driving characteristics of the traditional vehicle owners, a mathematical model of the time and distance of the electric vehicle connecting to the power grid is established. On this basis, two charging and discharging strategies for electric vehicles are proposed: peak-valley mode, which is used to control the charging and discharging of electric vehicles at peak and low levels of system load, and intelligent mode, According to the difference between the maximum generating power and the load, the charge and discharge power of the electric vehicle is controlled in order to improve the matching degree between the load and the output force in the microgrid system. Then, considering the economics of micro-grid operation and the charging and discharging benefit of electric vehicle, a two-layer optimization model of charge and discharge strategy for electric vehicle is proposed based on the theory of double-layer optimization. The upper layer model aims at minimizing the running cost of micro-grid system. The lower model aims at the maximum benefit of charging and discharging of electric vehicles. For the NP-hard problem of two-layer optimization, the KKTO Karush-Kuhn-Tucker condition is used to transform the two-layer optimization problem into a single-layer mathematical optimization model with equilibrium constraints. The simulation results show that the proposed charging and discharging strategy can reduce the running cost of microgrid and the charge and discharge cost of electric vehicle. The validity and feasibility of the proposed model are verified. On this basis, the vehicle routing problem is introduced. Taking the logistics distribution electric vehicle as the research object, the cooperative optimization model of the driving path and charge / discharge strategy of electric vehicle considering the economic operation of microgrid is proposed. According to the load forecasting value of wind turbine, user load forecasting value and distribution task of distribution company, the model makes decisions on the driving path of electric vehicle, the fast charging time and the charging and discharging power of each period. In order to realize the microgrid energy interaction cost, the cost of fast charging and battery loss of electric vehicle, the integrated operation cost of microgrid is the least. The hybrid coding adaptive imperialist competition algorithm is used to solve the model. The results show that the proposed method can effectively enhance the absorption capacity of the micro grid for renewable energy generation while the electric vehicle completes the distribution task. Greatly reduce the operating costs of microgrids.
【学位授予单位】：长沙理工大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：TM73

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