数学优化
控制重构
分布式发电
最优化问题
电容器
进化算法
交流电源
计算机科学
节点(物理)
还原(数学)
可靠性(半导体)
电压
人工蜂群算法
工程类
可靠性工程
功率(物理)
数学
电气工程
可再生能源
嵌入式系统
物理
几何学
结构工程
量子力学
标识
DOI:10.1080/15325008.2022.2049661
摘要
Distribution network reconfiguration (DNR) can be defined as an optimization problem in the power system that is done through changing the switching status to reduce loss and operating cost. Moreover, optimal capacitor allocation in the distribution network provides benefits such as loss reduction and improved voltage profile by reactive power compensation. Thus, this study provides a multi-objective framework for DNR and capacitor allocation problem in the presence of distributed generation (DG) sources. In the common DNR studies, the requirement of reliability is not usually fulfilled, usually loss and voltage deviation are often chosen as the objective functions. In this paper, the multi-objective problem is considered as a combination of energy not supplied (ENS) and emission produced by DG units and grid, moreover network loss is defined as the other objective function. The simultaneous presence of DG units and capacitors has made the problem more complicated, and there will be a demand for a precise approach to solve the multi-objective optimization problem. Hence, an enhanced artificial bee colony optimization (EABCO) method is proposed to tackle the complexity of the optimization problem. In order to shown the efficacy of the proposed approach, it is tested on a 33 and 86- node systems, the obtained results of EABCO method are compared with common evolutionary, hybrid and state-of–the-art algorithms. By implementing the proposed approach in two small-scale test systems, approximately 74% and 15% reduction in ENS are achieved compared to the initial value before distribution feeder reconfiguration.
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