Distributed Power Generation Siting and Sizing Method Based on Improved SCDBO Algorithm

周武定; 张小兵; 曾进辉; 刘颉; 陈泽文; 贺宇轩

doi:10.3969/j.issn.1008-0198.2025.05.017

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Distributed Power Generation Siting and Sizing Method Based on Improved SCDBO Algorithm

更新时间：2026-01-10

- Distributed Power Generation Siting and Sizing Method Based on Improved SCDBO Algorithm
- Vol. 45, Issue 5, Pages: 124-132(2025)
- 作者机构：
  
  1. 湖南工业大学交通与电气工程学院,湖南,株洲,412007
  2. 国网湖南省电力有限公司株洲供电分公司,湖南,株洲,412000
  3. 株洲电力勘测设计科研有限公司,湖南,株洲,412000
- 作者简介：
- 基金信息：
- DOI：10.3969/j.issn.1008-0198.2025.05.017
  CLC： TM715
- Published Online：11 November 2025，
  
  Published：11 November 2025
- 稿件说明：
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周武定, 张小兵, 曾进辉, 刘颉, 陈泽文, 贺宇轩. 基于改进蜣螂算法的分布式电源选址定容方法[J]. 湖南电力, 2025, 45(5): 124-132.

周武定, 张小兵, 曾进辉, et al. Distributed Power Generation Siting and Sizing Method Based on Improved SCDBO Algorithm[J]. 2025, 45(5): 124-132.
周武定, 张小兵, 曾进辉, 刘颉, 陈泽文, 贺宇轩. 基于改进蜣螂算法的分布式电源选址定容方法[J]. 湖南电力, 2025, 45(5): 124-132. DOI： 10.3969/j.issn.1008-0198.2025.05.017.

周武定, 张小兵, 曾进辉, et al. Distributed Power Generation Siting and Sizing Method Based on Improved SCDBO Algorithm[J]. 2025, 45(5): 124-132. DOI： 10.3969/j.issn.1008-0198.2025.05.017.

摘要

针对传统分布式电源选址定容规划中高维非线性问题及备选节点策略难以充分反映配电网实际结构需求的难点

提出一种基于改进蜣螂优化算法的分布式电源选址定容方法。首先

构建融合节点负荷敏感系数的加权有功损耗目标函数

并与电压偏差共同组成双目标优化模型。其次

设计融合Piecewise混沌初始化、随机游走扰动与纵横交叉策略的改进蜣螂优化算法

提升全局搜索能力与复杂环境下的适应性。最后

引用IEEE-33节点配电系统进行算例分析

并引入全生命周期视角

从净收益、投资成本与碳减排效益等方面对优化方案进行经济评估。结果表明

所提方法不仅在降低网损与电压偏差方面优于多种传统算法

而且具备显著的经济优势

验证了所提方法在技术性能与经济可行性方面的综合优越性。

Abstract

In response to the challenges of high-dimensional nonlinear problems and alternative node strategies that struggle to accurately reflect the actual structural requirements of the distribution network in traditional distributed generation (DG) siting and sizing planning

a DG siting and sizing method based on an improved strategy combined dung beetle optimization algorithm is proposed. Firstly

the weighted active loss objective function incorporating the load sensitivity factor(LSF) of the fusion node is constructed

and the dual-objective optimization model is formed together with the voltage deviation. Secondly

an improved dung beetle optimization algorithm that integrates Piecewise chaotic initialization

random walk perturbation

and cross strategies is designed to enhance global search capabilities and adaptability in complex environments. Finally

through the analysis of the IEEE-33 node distribution system example

a comprehensive evaluation of the optimization scheme is conducted from the perspective of the entire lifecycle

including aspects such as net benefits

investment costs

and carbon reduction benefits. The results indicate that the proposed method not only outperforms various traditional algorithms in reducing network losses and voltage deviations

but also demonstrates significant economic advantages

which validates its comprehensive superiority in both technical performance and economic feasibility.

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