论文总字数：22313字

摘 要

ABSTRACT 4

第一章 绪论 1

1.1选题背景与研究意义 1

1.2国内外研究现状 1

1.3常见的无功优化算法 2

1.3.1传统的无功优化方法 2

1.3.2智能优化算法 2

1.4论文的主要工作和安排 3

第二章 分布式发电对配电网的影响 4

2.1分布式发电简介 4

2.2分布式电源类型 5

2.2.1风力发电 5

2.2.2光伏发电 5

2.2.3燃料电池 6

2.2.4微型燃气轮机 6

2.3含分布式电源的配电网潮流计算 6

2.3.1配电网潮流的数学模型 6

2.3.2配电网元件模型 7

2.3.3潮流计算过程 8

2.4含DG的不同节点类型的处理方法 9

2.5潮流计算流程 10

2.6分布式电源对网损的影响 12

2.7分布式电源的并入给电压的影响 13

2.8本章小结 13

第三章 基于协同进化理论在无功优化的研究 14

3.1协同进化理论介绍 14

3.2遗传算法理论基础 14

3.2.1遗传算法理论简介 14

3.2.2遗传算法的特点 14

3.3遗传算法的计算步骤 15

3.4协同进化遗传算法 16

3.4.1协同进化遗传算法的理论 16

3.4.2协同进化遗传算法的步骤 17

3.5无功优化的数学模型 18

3.5.1目标函数 19

3.5.2功率方程约束 19

3.5.3不等式约束 20

3.6本章小结 20

第四章 含DG的配电网无功优化算例分析 21

4.1分布式电源的并入对节点电压和网络损耗的影响 21

4.1.2基于传统遗传算法的研究 21

4.1.2基于协同进化遗传算法的分析 22

4.1.3 对比分析 23

5.2本章小结 23

第五章 结论和展望 25

5.1结论 25

5.2展望 25

参考文献 26

致谢 28

摘要

随着新能源技术的不断发展，人们对于清洁能源的需求越来越高。新能源具有清洁、污染小、效率高等优点，渐渐进入了大众的视野。分布式电源正有这些优点，现在越来越被广泛应用。配电网与分布式发电互联，可以减少网损，因此分布式发电在电力系统中的应用越来越广泛。但是分布式发电的整合需要我们综合考虑，因为分布式电源的并入可能会使系统网络损耗变大，各节点电压也会发生改变，是因为需要确定好分布式电源的个数和并入位置，才能达到最优的效果。如何利用好分布式电源的并入，本文对这一问题进行了研究。由于分布式发电的优点，研究分布式发电的无功优化问题具有重要意义。分布式发电在配电网无功优化中起着非常重要的作用。

本文首先介绍了研究的意义和背景，然后介绍了什么是分布式发电以及分布式发电的几种类型，如风力发电、光伏发电等。介绍了潮流计算的原理和步骤，研究了分布式发电并网对网损和节点电压的影响。介绍了什么是遗传算法和遗传算法的原理，研究了协同进化遗传算法的原理和步骤，建立了基于协同进化遗传算法的无功优化模型。最后，对基于遗传算法的无功优化模型和基于协同进化遗传算法的无功优化模型进行仿真，得到最优无功优化结果。并对两种算法的结论进行了比较，得出了相应的结论。

关键词：分布式电源；无功优化；协同进化遗传算法；配电网；潮流计算；网损

ABSTRACT

With the development of new energy technology, people have higher and higher demand for clean energy. New energy has the advantages of clean, small pollution and high efficiency, and gradually entered the public vision. Distributed power supply is now widely used because of its advantages. The interconnection of distribution network and distributed generation can reduce the loss of the network, so distributed generation is widely used in power system. But the integration of distributed power generation needs our comprehensive consideration, because the integration of distributed power supply may make the network loss of the system larger, and the voltage of each node will change. It is because the number and location of distributed power supply need to be determined to achieve the optimal effect. How to use the integration of distributed power supply, this paper studies this problem. Due to the advantages of distributed generation, it is of great significance to study the reactive power optimization of distributed generation. Distributed generation plays a very important role in reactive power optimization of distribution network.

This paper first introduces the significance and background of the research, then introduces what distributed generation and distributed generation types, such as wind power generation, photovoltaic power generation, etc. The principle and steps of power flow calculation are introduced. The influence of distributed generation grid connection on network loss and node voltage is studied. This paper introduces the principle of genetic algorithm and genetic algorithm, studies the principle and steps of coevolutionary genetic algorithm, and establishes a reactive power optimization model based on coevolutionary genetic algorithm. Finally, the reactive power optimization model based on genetic algorithm and the reactive power optimization model based on coevolutionary genetic algorithm are simulated to get the optimal reactive power optimization results. The results of the two algorithms are compared and the corresponding conclusions are obtained.

Key words: distributed power supply; Reactive power optimization; The genetic algorithm of co evolution; Distribution network; Power flow calculation; Network loss

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