基于改进遗传算法的配电网储能优化配置方法研究
Research on Optimal Allocation Method of Energy Storage in Distribution Network Based on Improved Genetic Algorithm
石立桩 1黄继杰 2郝炜 1刘健 1马琳琦1
作者信息
- 1. 国网天津市电力公司培训中心,天津 300181
- 2. 北京科东电力控制系统有限责任公司,北京 100192
- 折叠
摘要
增加储能设备是配电网解决新能源普及所导致的供电负荷剧烈变化的常用方法,需要从定址和定容两方面来优化配置储能设备.通过分析配电系统中馈线负荷的日内变化特性及与阶梯电价的关联关系,以关联关系作为馈线的遗传属性使其成为遗传个体,在对遗传个体进行编码后,找出线路日内负荷变化最小者为遗传个体,该个体将信息遗传给与其日内变化最接近的馈线后死去,由此产生变异并继续遗传下去,直至遗传个体为几条小于最大容量的馈线为止.仿真结果显示,通过对遗传算法中的生物进化机制做相应的改进,可获得配电网储能利用率最高的配置方案.
Abstract
Adding energy storage equipment is a commonly used method for distribution networks to address the drastic changes in power supply loads caused by the popularization of new energy. It is necessary to optimize the configuration of energy storage equipment from two aspects:location and capacity. This article analyzes the intraday variation characteristics of feeder load in distribution systems and its correlation with stepped electricity prices. The correlation relationship is used as the genetic attribute of the feeder to make it a genetic individual. After encoding the genetic individual,the individual with the smallest intraday load change in the line is identified as the genetic individual. The individual inherits information to the feeder closest to its intraday change and dies,resulting in mutation and continues to inherit,Until the genetic individual has several feeders smaller than the maximum capacity. The simulation results show that the configuration scheme with the highest utilization rate of distribution network energy storage can be obtained by making corresponding improvements to the biological evolution mechanism in the genetic algorithm.
关键词
遗传算法/生物进化/储能优化配置/负荷预测/馈线Key words
genetic algorithm/biological evolution/optimal allocation of energy storage/load forecasting/feeder引用本文复制引用
基金项目
国家电网电力科学研究院有限公司科技项目(52467M220059)
国家电网科技项目(5222JZ17002R)
出版年
2024
NETL
NSTL
万方数据