电动汽车充电站选址问题研究

发布时间：2018-02-02 22:24

本文关键词： 电动汽车充电站选址多等级选址模型禁忌搜索算法　出处：《大连海事大学》2015年硕士论文　论文类型：学位论文

【摘要】：近年来,能源短缺、环境污染问题日益突出,面临能源和环境的巨大压力。我国正处于能源消费结构转型的阶段,一些传统能源生产行业正走在不断革新的道路上,例如：钢铁产业、煤炭产业和石油产业等, 《穹顶之下》惊人的点击率更是给我们人类对于未来可持续发展一个巨大的冲击与反思。在能源消耗中占据很大比例的汽车产业不得不进行一次脱胎换骨,目前各国政府以及汽车生产商开始把目光聚焦在电动汽车的发展上在电动汽车发展上走在最前沿的当属美国特斯拉汽车(Tesla Motors)公司。根据国家科技部《电动汽车科技发展“十二五”专项计划》,到2050年我国的纯电动汽车数量将会达到400多万辆。随着电动汽车数量的不断增多,充电站同时也步入了一个规模化、网络化的规划和建设进程中。目前,无论国产的电动汽车还是在技术上已相当成熟的特斯拉汽车市场前景并不乐观,主要原因既不是价格的高昂更不是技术的不成熟,造成这种现象的是很多城市呈现出电动汽车充电困难的问题,如何保证电动汽车电站网络的运行,实现交通的畅通、用户的出行以及充电站的效益,对于规划者和管理者来说,如何实现充电站选址的合理规划,这是一个亟待解决的问题。通过查阅国内外文献资料,对电动汽车充电站选址理论进行了相关的分析和研究,参考国内外充电站发展现状,对充电站的建设进行深入研究、总结经验,并针对充电站选址影响因素、选址原则、选址流程等做出了相关的定性分析。通过前述理论的分析,本文提出了一个多等级电动汽车充电站选址模型,考虑到用户分布的特点,该模型以充电站建设成本与用户充电总成本最小为目标并采用改进的禁忌搜索算法对该模型进行求解。针对用户分配和站址等级的两个决策变量设计了初始解和禁忌编码相结合的设计方案,并且采用了可以同时解决充电站建设等级、位置、用户分配问题的2-opt邻域搜索方法。最后选取北京朝阳区为实例,利用本文所构建的多等级选址模型以及算法进行相关的实例验证,结果表明所构建的模型是一个很有效的充电站选址模型,算法也具有很好的站址寻找能力和收敛性能。
[Abstract]:In recent years, the energy shortage, environmental pollution problem is increasingly prominent, facing the tremendous pressure of energy and environment. China is in the stage of energy consumption structure transformation. Some traditional energy production industries are on the path of continuous innovation, such as steel industry, coal industry and oil industry. Under the Dome, the amazing click rate gives us a great impact and reflection on the sustainable development in the future. The automobile industry, which accounts for a large proportion of energy consumption, has to undergo a radical transformation. At present, governments and auto manufacturers are beginning to focus on the development of electric vehicles, and the most advanced ones in the development of electric vehicles are Tesla Motors of the United States. Company. According to the national science and technology ministry "electric vehicle science and technology development" "12th five-year plan" In 2050, the number of pure electric vehicles in China will reach more than 4 million. With the increasing number of electric vehicles, charging stations have also stepped into a large scale. Network planning and construction process. At present, whether domestic electric vehicles or in the technology has been quite mature Tesla car market prospects are not optimistic. The main reason is neither the high price nor the immaturity of technology, which is caused by the problem of electric vehicle charging difficulty in many cities, how to ensure the operation of electric vehicle power station network. To realize the smooth traffic, the travel of users and the benefit of charging station, for planners and managers, how to realize the reasonable planning of charging station location. This is a problem to be solved urgently. Through consulting the domestic and foreign literature, this paper analyzes and studies the theory of charging station location of electric vehicle, and refers to the current situation of charging station development at home and abroad. The construction of charging station in-depth research, summing up experience, and in view of charging station location factors, location principles, site selection process and other related qualitative analysis. Through the aforementioned theoretical analysis. In this paper, a charging station location model for multi-class electric vehicles is proposed, which takes into account the characteristics of user distribution. The model aims at minimizing the cost of building the charging station and the total charge cost of the user. An improved Tabu search algorithm is used to solve the model. The initial solution is designed for the two decision variables of user allocation and site level. Tabu coding combined with the design. The 2-opt neighborhood search method is used to solve the problem of grade, location and user allocation of charging station at the same time. Finally, Chaoyang District in Beijing is selected as an example. The multi-level location model and the algorithm are used to verify the model. The results show that the model is a very effective charging station location model. The algorithm also has good site searching ability and convergence performance.
【学位授予单位】：大连海事大学
【学位级别】：硕士
【学位授予年份】：2015
【分类号】：U491.8

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