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Optimization Method for Multi-Reactive Power Regulation Resources of Wind Power-Containing Systems Based on Particle Swarm Algorithm
DOI: 10.12677/SG.2022.123008, PDF, HTML, XML, 下载: 174  浏览: 267  科研立项经费支持

Abstract: As a high-quality renewable energy power generation technology, wind power generation can not only alleviate energy shortage, but also improve environmental pollution. However, with the increase of the proportion of wind power connected to the grid, the network loss of wind power system increases sharply. In this paper, taking the minimum active power network loss of the system as the objective function and the conventional generator set, wind turbine set, static reactive power compensator, capacitor reactor and transformer tap as the reactive power control resources, the mathematical model of reactive power optimization control of wind power system is established, and the particle swarm optimization algorithm is used to solve the model. Finally, based on MATLAB platform, the established mathematical model is analyzed in the IEEE30 node system after wind power grid connection. The results show that this method is effective and feasible in reducing the network loss of wind power system.

1. 引言

2. 多无功调控资源的优化控制模型

2.1. 多无功调控资源能力分析

Table 1. Comparison of multi-reactive power control resource performance

2.2. 多无功源优化控制模型

2.2.1. 目标函数

$\mathrm{min}\Delta {P}_{\sum }=\underset{i=1}{\overset{i=N}{\sum }}{P}_{Gi}+\underset{i=1}{\overset{i=M}{\sum }}{P}_{Wi}-\underset{i=1}{\overset{i=K}{\sum }}{P}_{Li}$ (1)

2.2.2. 约束条件

${U}_{i}\underset{j=1}{\overset{j=n}{\sum }}{U}_{j}\left({G}_{ij}\mathrm{cos}{\delta }_{ij}+{B}_{ij}\mathrm{sin}{\delta }_{ij}\right)={P}_{i}$ (2)

${U}_{i}\underset{j=1}{\overset{j=n}{\sum }}{U}_{j}\left({G}_{ij}\mathrm{sin}{\delta }_{ij}+{B}_{ij}\mathrm{cos}{\delta }_{ij}\right)={Q}_{i}$ (3)

$\left\{\begin{array}{c}{P}_{Gi\mathrm{min}}\le {P}_{Gi}\le {P}_{Gi\mathrm{max}}\\ {Q}_{Gi\mathrm{min}}\le {Q}_{Gi}\le {Q}_{Gi\mathrm{max}}\\ {P}_{Wi\mathrm{min}}\le {P}_{Wi}\le {P}_{Wi\mathrm{max}}\\ {Q}_{Wi\mathrm{min}}\le {Q}_{Wi}\le {Q}_{Wi\mathrm{max}}\\ {U}_{i\mathrm{min}}\le {U}_{i}\le {U}_{i\mathrm{max}}\\ {T}_{h\mathrm{min}}\le {T}_{h}\le {T}_{h\mathrm{max}}\\ {Q}_{Ci\mathrm{min}}\le {Q}_{Ci}\le {Q}_{Ci\mathrm{max}}\\ {Q}_{SVCi\mathrm{min}}\le {Q}_{SVCi}\le {Q}_{SVCi\mathrm{max}}\end{array}$ (4)

$|{\delta }_{i}-{\delta }_{j}|<\mathrm{max}|{\delta }_{i}-{\delta }_{j}|$

3. 基于粒子群算法的多无功调控资源优化控制方法

3.1. 粒子群算法基本原理

Figure 1. Optimization control flowchart of multi-reactive power control resource based on particle swarm algorithm

${v}_{id}^{t+1}=\omega {v}_{id}^{t}+{c}_{1}{r}_{1}\left({P}_{best}-{x}_{id}^{t}\right)+{c}_{2}{r}_{2}\left({G}_{best}-{x}_{id}^{t}\right)$

${x}_{id}^{t+1}={x}_{id}^{t}+{v}_{id}^{t+1}$

3.2. 多无功调控资源优化控制方法

4. 算例分析

4.1. 算例设置

Figure 2. IEEE30 node diagram

4.2. 算例结果分析

Table 2. Power plant node parameters

Table 3. Control variable data

Table 4. Power plant node optimization results (Mvar)

Table 5. Optimization results of ordinary nodes (nominal values)

Table 6. Active network loss optimization results (MW)

Figure 3. Fitness curve chart

5. 总结

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