计及分布式发电的配电网规划研究

发布时间：2018-01-20 04:36

本文关键词： 分布式发电配电网规划多目标优化粒子群算法　出处：《山东大学》2008年硕士论文　论文类型：学位论文

【摘要】： 分布式发电(DG)技术是近几年兴起的一种新型发电技术,它能充分利用可再生洁净新能源,投资节省,与环境兼容灵活高效地进行发电。分布式发电已经被世界各国所重视,将成为电力系统供电总量中重要的组成部分。因此对分布式发电技术进行研究有着重大的理论价值和现实意义。随着负荷增长和分布式电源大量接入配电网,研究计及分布式发电的配电网规划成为重要课题。配电网规划是一个多变量、多约束的混合非线性规划问题,其优化过程十分复杂。针对配电网规划和分布式发电的特点,本文提出了应用粒子群算法(PSO)进行计及分布式发电的配电网规划。本文就这一课题展开,通过粒子群算法解决计及分布式发电规划中的多目标问题。本文首先介绍了分布式发电的分类以及其对电力系统造成的影响。这些影响包括:分布式发电对电力系统安全和可靠性、电能质量、继电保护、网损等方面。分布式电源的嵌入打破了传统电力系统的格局,原有的配电网规划方法在计及分布式发电后不能很好地发挥作用。本文介绍了配电网规划的一般思路和简要步骤,并通过配电网扩展规划和计及分布式发电后的规划方法循序渐进地进行了研究。无论是在自然学科领域还是在社会学科领域,以最小的成本获取最大的效益,始终是人类追求的目标。最小化成本的同时最大化效益,将这一对矛盾的两个方面同时考虑,就构成了一个典型的多目标优化问题。本文对多目标问题的产生背景,粒子群算法的基本概念、数学模型以及算法步骤、应用领域等进行了简单介绍。本文通过对粒子群优化算法的深入研究,进一步扩展了该算法的应用领域,为解决多目标优化问题提供了新的理论依据和高效的解决方案。本文建立了以网损最小、分布式电源运行成本最小、分布式电源安装容量最大为子目标的计及分布式发电的配电网规划多目标优化模型。对于多目标优化模型,本文通过不同量纲函数的归一化加权,把多目标优化问题转化为单目标优化求解。最后,介绍了粒子群优化算法在一个实际配电网规划的具体应用实现,并进行了仿真计算。运算结果显示了该算法应用于这一领域的可行性和有效性。通过粒子群算法进行多目标问题的优化计算,并对不含有和含有分布式电源的配电网规划结果作了比较分析。算例结果表明,引入分布式电源后对配电网优化带来了一定的合理性和优越性。也从一个侧面印证了分布式发电技术进入电力系统后会带来较有利的影响,同时也印证了分布式发电技术有着广阔的应用前景。
[Abstract]:Distributed power generation (DG) technology is a new generation technology developed in recent years. It can make full use of renewable and clean new energy and save investment. Distributed generation has been paid more attention to by many countries all over the world. It will become an important part of the total power supply in power system, so it is of great theoretical value and practical significance to study the distributed generation technology. With the increase of load and the large amount of distributed generation connected to the distribution network, it becomes an important topic to study the distribution network planning, which is a multi-variable, multi-constraint hybrid nonlinear planning problem. The optimization process is very complex. According to the characteristics of distribution network planning and distributed generation, a particle swarm optimization algorithm (PSO) is proposed to plan the distribution network with distributed generation. Particle swarm optimization (PSO) is used to solve the multi-objective problem in distributed generation planning. This paper first introduces the classification of distributed generation and its impact on the power system, including: distributed generation on the security and reliability of the power system, power quality, relay protection. The embedding of distributed power supply breaks the pattern of traditional power system. The original distribution network planning method can not play a good role in the consideration of distributed generation. This paper introduces the general ideas and brief steps of distribution network planning. The distribution network expansion planning and the planning method after considering distributed generation are studied step by step. Whether in the field of natural science or in the field of social sciences, it is always the goal that human beings pursue to obtain the maximum benefit with the minimum cost, while minimizing the cost and maximizing the benefit at the same time. A typical multi-objective optimization problem is formed by considering the two aspects of the contradiction at the same time. In this paper, the background of the multi-objective problem, the basic concept of particle swarm optimization algorithm, the mathematical model and the steps of the algorithm are discussed. The application fields are briefly introduced. The application field of PSO is further expanded by the in-depth study of PSO. It provides a new theoretical basis and an efficient solution for solving multi-objective optimization problems. In this paper, the network loss is minimum and the operation cost of distributed power is minimum. The distribution network planning multi-objective optimization model with the maximum installed capacity of distributed generation is considered. For the multi-objective optimization model, the normalized weighting of different dimensionality functions is adopted in this paper. The multiobjective optimization problem is transformed into a single objective optimization solution. Finally, the application of particle swarm optimization algorithm in a practical distribution network planning is introduced. The simulation results show that the algorithm is feasible and effective in this field. The particle swarm optimization algorithm is used to optimize the multi-objective problem. The results of distribution network planning without and with distributed generation are compared and analyzed. The introduction of distributed generation brings some rationality and superiority to the optimization of distribution network, and it also proves that the distributed generation technology will bring more favorable influence when it enters the power system. At the same time, it also proves that distributed generation technology has a broad application prospect.
【学位授予单位】：山东大学
【学位级别】：硕士
【学位授予年份】：2008
【分类号】：TM715

【引证文献】