分布式电源定容选址的优化规划

发布时间：2018-06-04 12:49

本文选题：分布式电源 + 粒子群算法　；参考：《昆明理工大学》2017年硕士论文

【摘要】：随着经济的高速发展,对电能的需求也随之日益增大。但是由于传统的发电方式并不利于能源可持续发展,所以分布式发电显得越来越重要。分布式发电不单能充分利用清洁新能源,而且分布式发电具有投资小、机动灵活、适应性强的优点。分布式电源的种类、接入位置以及接入的容量对电网的潮流、电压质量以及电网的各方面经济费用都有一定的影响。对分布式电源种类选择,定容和选址的合理规划,能够改善电力系统的网损,用户侧的电压质量以及各种电网建造费用。因此分布式电源对配电网规划有着极其重要意义。本文首先介绍了分布式电源基本概念,并且介绍了分布式电源接入配电网以后,对配电网电压稳定、网损、继电保护以及系统的可靠性都有一定的影响。综合以上的影响,对分布式电源的接入容量和位置提出了要求。在优化之前,本文对传统潮流算法进行了改良,使其能够适应弱环和分布式电源的配电网情况,也能够更加方便的计算优化的结果。需要本文充分考虑了分布式电源对电力系统可靠性以及经济性的影响,基于电压,电流以及分布式电源容量的限制条件,建立了分布式电源固定投资,网损以及电压稳定指标的多目标函数。借助该目标函数,能够对分布式电源的定容和选址方案进行合理的评估。通过对各类分布式电源定容和选址算法的研究,粒子群算法具有易于实现,高效率等诸多特性,利于此类问题的解决。但是由于本文充分考虑了电力系统的经济性和可靠性,所提出的多目标函数较一般目标函数更为复杂,使用标准的粒子算法容易陷入局部收敛的陷阱。所以本文融入了遗传算法的算子和退火算法思想,利用遗传算法的变异和交叉算子以及退火思想,解决了在实现多目标函数时候,容易陷入局部收敛的情况。改进后的粒子群算法兼具遗传算法、粒子群算法以及退火算法的优点。本文从两方面入手分析,一方面通过比较未接入分布式电源,标准粒子群算法的优化方案以及改进粒子群算法三种情况,论证了改进后的粒子群算法的优良性。另一方面,通过改进后的粒子群算法与其他文献的改进算法的比较,论证本文改进后的粒子群算法的优良性。
[Abstract]:With the rapid development of economy, the demand for electric energy is increasing day by day. But because traditional power generation is not conducive to sustainable development of energy, distributed generation is becoming more and more important. Distributed generation not only makes full use of clean new energy, but also has the advantages of small investment, flexible mobility and strong adaptability. The type, location and capacity of distributed generation have a certain influence on the power flow, voltage quality and the economic cost of the power network. The selection of the type of distributed power supply, the reasonable planning of fixed capacity and location can improve the network loss of power system, the voltage quality of user side and the construction cost of various power network. So distributed generation is of great significance to distribution network planning. In this paper, the basic concept of distributed generation is introduced, and the influence of distributed generation on voltage stability, network loss, relay protection and system reliability is introduced. Combined with the above effects, the access capacity and location of distributed power generation are required. Before the optimization, the traditional power flow algorithm is improved to adapt to the distribution network of weak loop and distributed generation, and the results of optimization can be calculated more conveniently. In this paper, the influence of distributed power supply on the reliability and economy of power system is fully considered. Based on the limitation of voltage, current and capacity of distributed power supply, the fixed investment of distributed power supply is established. Multiobjective function of network loss and voltage stability index. With the help of the objective function, the fixed volume and location scheme of distributed power generation can be evaluated reasonably. Based on the research of fixed volume and location algorithm of distributed power supply, particle swarm optimization has many characteristics, such as easy to implement, high efficiency and so on, which is helpful to solve this kind of problems. However, since the economy and reliability of power system are fully considered in this paper, the proposed multi-objective function is more complex than the general objective function, and the standard particle algorithm is easy to fall into the trap of local convergence. So this paper integrates the operator of genetic algorithm and the idea of annealing algorithm, using the mutation and crossover operator of genetic algorithm and the idea of annealing, to solve the problem that the multi-objective function is easy to fall into local convergence. The improved particle swarm optimization algorithm has the advantages of genetic algorithm, particle swarm optimization algorithm and annealing algorithm. This paper analyzes from two aspects. On the one hand, by comparing the unconnected distributed power supply, the optimization scheme of standard particle swarm optimization algorithm and the improved particle swarm optimization algorithm, the paper proves the superiority of the improved particle swarm optimization algorithm. On the other hand, through the comparison between the improved PSO algorithm and the improved PSO algorithm in other literatures, the improved PSO algorithm is proved to be superior.
【学位授予单位】：昆明理工大学
【学位级别】：硕士
【学位授予年份】：2017
【分类号】：TM715

【参考文献】