联合分布式电源的变电站选址定容研究

发布时间：2018-03-23 00:32

本文选题：分布式电源　切入点：变电站选址定容　出处：《华北电力大学》2014年硕士论文　论文类型：学位论文

【摘要】：由于能源、环境等问题的制约,火力发电等传统发电技术受到了越来越多的限制。分布式发电技术具有用地省、投资小、资源环境友好等优点,其在电力系统尤其是配电系统中得到了快速推广。然而,随着分布式电源并网比例的大幅增加,其不可避免产生对配电网的电能质量、可靠性、继电保护、规划等诸多方面产生影响。配电网规划是电力系统规划的重要组成部分,对配电系统进行合理规划能有效提高供电的可靠性与经济性,考虑分布式发电源接入下的配电网规划问题具有重要意义。本文从配电网规划的基础即变电站选址定容入手,研究了含分布式电源的配电系统变电站选址定容问题。本文在分析分布式电源主要特点及对配电系统影响的基础上,基于全周期寿命成本理论,建立了含分布式电源的配网变电站选址定容优化模型。该模型综合考虑了变电站上级输电网、下级输电网、变电站设备、分布式电源的初始投资、运行维护成本、故障损失、废弃费用；考虑到分布式电源对配电系统的影响,对变电站上、下级输电网初始投资以及运行维护成本进行了修正,确定了基于全寿命周期的含分布式电源变电站选址定容优化目标函数,并得到了电气约束条件,并将电气约束条件以及地理约束条件作为惩罚因子用以修正目标函数。其次,本文提出采用改进量子遗传算法对优化目标函数进行寻优求解,该算法具有自适应的收缩扩张系数,能根据寻优函数本身自适应调节寻优速度,加速寻优过程,同时能有效避免算法陷入局部最优。选取Voronoi图理论对各变电站供电范围进行自动划分,进而实现含分布式电源的变电站选址定容优化求解。最后,采用具体算例对本文所提方法进行了验证。结果表明,本文提出方法可对的含分布式电源的变电站选址定容问题进行有效优化,含分布式电源的变电站选址定容规划可节省大量投资,具有较高的经济性,可有效缓解经济发达区域变电站站址选择困难的问题。
[Abstract]:Because of the restriction of energy, environment and other problems, traditional power generation technology such as thermal power generation is more and more restricted. Distributed power generation technology has the advantages of saving land, small investment and friendly resources and environment. It has been popularized rapidly in power system, especially in distribution system. However, with the increase of the proportion of distributed power supply, it will inevitably produce power quality, reliability and relay protection for distribution network. Distribution network planning is an important part of power system planning. Reasonable planning of distribution system can effectively improve the reliability and economy of power supply. It is of great significance to consider the problem of distribution network planning under distributed power generation and access. Based on the basis of distribution network planning, that is, substation location and capacity determination, this paper studies the distribution system location and capacity problem with distributed generation. Based on the analysis of the main characteristics of distributed power supply and its influence on distribution system, this paper bases on the theory of full cycle life cost. The optimization model of distribution network location and capacity is established, which considers the initial investment, maintenance cost and fault loss of the superior transmission network, the lower transmission network, the substation equipment and the distributed power supply. Considering the influence of distributed power generation on the distribution system, the initial investment and operation and maintenance cost of the lower level transmission network in substations are corrected. The optimization objective function of substation location with distributed power supply is determined based on the whole life cycle, and the electrical constraint condition is obtained, and the electric constraint condition and geographical constraint condition are used as penalty factors to modify the objective function. In this paper, an improved quantum genetic algorithm (QGA) is proposed to solve the optimization objective function. The algorithm has adaptive shrinkage expansion coefficient, adaptively adjusts the optimization speed according to the optimization function itself, and accelerates the optimization process. At the same time, it can effectively avoid the algorithm falling into local optimum. The Voronoi diagram theory is selected to automatically partition the power supply range of each substation, and then to realize the optimal solution of substation location and capacity optimization with distributed power supply. Finally, A concrete example is used to verify the proposed method. The results show that the proposed method can effectively optimize the location and capacity problem of substations with distributed power supply. The location and capacity planning of substations with distributed generation can save a lot of investment and have a high economy. It can effectively alleviate the problem of difficult selection of substation sites in economically developed regions.
【学位授予单位】：华北电力大学
【学位级别】：硕士
【学位授予年份】：2014
【分类号】：TM63

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