| 基于BFOA算法的配电网DG选址定容方法 |
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| 引用本文： | 刘可,王昕,刘冬平,郭财,张启晟,闫涵,王轩,马恒瑞.基于BFOA算法的配电网DG选址定容方法[J].陕西电力,2022,0(9):90-96. |
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| 作者姓名： | 刘可 王昕 刘冬平 郭财 张启晟 闫涵 王轩 马恒瑞 |
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| 作者单位： | （1.国网青海省电力公司电力科学研究院，青海西宁 810000；2.深圳市中电电力技术股份有限公司，广州深圳 518000；3.国网青海省电力公司检修公司，青海西宁 810000；4.青海大学新能源（光伏）产业研究中心，青海西宁 810016） |
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| 摘 要： | 发展分布式能源系统对于实现的“碳达峰”和“碳中和”，提升可再生能源的开发利用具有重要意义。提出一种基于细菌觅食优化算法（BFOA）的配电网分布式电源（DG）选址定容方法。建立以配电网的功率损耗指数、电压偏差以及安装分布式电源所降低的净运行成本最小为目标的数学模型及约束条件，提出损耗敏感系数（LSF）来确定DG安装位置，并引用BFOA算法求解DG的最佳容量。仿真表明，相对于传统优化算法，BFOA算法在模型求解时间和收敛速度上具有明显优势，所提规划方法能够最大限度地降低功率损耗和运行成本，并提高系统的电压稳定性。
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| 关 键 词： | 配电网 细菌觅食优化算法 分布式电源 容量规划 |
DG Site Selection and Capacity Planning Method in Distribution Network Based on BFOA Algorithm |
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| LIU Ke,WANG Xin,LIU Dongping,GUO Cai,ZHANG Qisheng,YAN Han,WANG Xuan,MA Hengrui.DG Site Selection and Capacity Planning Method in Distribution Network Based on BFOA Algorithm[J].Shanxi Electric Power,2022,0(9):90-96. |
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| Authors: | LIU Ke WANG Xin LIU Dongping GUO Cai ZHANG Qisheng YAN Han WANG Xuan MA Hengrui |
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| Affiliation: | （1. State Grid Qinghai Electric Power Research Institute，Xining 810000，China； 2. CET Electric Technology Inc.， Shenzhen 518000， China；3. State Grid Qinghai Electric Power Company Overhaul Company，Xining 810000，China； 4. New Energy （Photovoltaic） Industry Research Center，Qinghai University，Xining 810016， China） |
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| Abstract: | The development of distributed energy systems is of great significance for the realization of carbon peak and carbon neutrality， for the promotion of development and utilization of renewable energy. A method for DG site selection and capacity determination in distribution networks is proposed based on bacterial foraging optimization algorithm（BFOA）. Mathematical model and constraints are established to minimize the power loss index， voltage deviation and net operating cost of distribution network. Loss sensitivity factor （LSF） is proposed to determine the DG installation location. BFOA algorithm is used to solve the optimal capacity of DG. Simulation shows that BFOA algorithm has obvious advantages in model solution time and convergence speed compared with traditional optimization algorithms. The proposed planning method can minimize power loss and operating cost and improve the system voltage stability. |
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| Keywords: | distribution network bacterial foraging optimization algorithm distributed generation capacity planning |
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