Optimal battery storage location and control in distribution network

IF 0.6 Q4 ENGINEERING, ELECTRICAL & ELECTRONIC
Milos Stevanovic, A. Janjic, Sreten Stojanovic, D. Tasic
{"title":"Optimal battery storage location and control in distribution network","authors":"Milos Stevanovic, A. Janjic, Sreten Stojanovic, D. Tasic","doi":"10.2298/fuee2201121s","DOIUrl":null,"url":null,"abstract":"The paper discusses the problem of the energy losses reduction in electrical networks using a battery energy storage system. One of the main research interests is to define the optimal battery location and control, for the given battery characteristics (battery size, maximum charge / discharge power, discharge depth, etc.), network configuration, network load, and daily load diagram. Battery management involves determining the state of the battery over one period (whether charging or discharging) and with what power it operates. Optimization techniques were used, which were applied to the model described in the paper. The model consists of a fitness function and a constraint. The fitness function is the dependence of the power losses in the network on the current battery power, and it is suggested that the function be fit by a n - order power function. The constraints apply to the very characteristics of the battery for storing electricity. At any time interval, the maximum power that the battery can receive or inject must be met. At any time, the stored energy in the battery must not exceed certain limits. The power of losses in the network is represented as the power of injection into the nodes of the network. The optimization problem was successfully solved by applying a genetic algorithm (GA), when determining optimal battery management. Finally, the optimal battery management algorithm is implemented on the test network. The results of the simulations are presented and discussed.","PeriodicalId":44296,"journal":{"name":"Facta Universitatis-Series Electronics and Energetics","volume":"36 1","pages":""},"PeriodicalIF":0.6000,"publicationDate":"2022-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"1","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Facta Universitatis-Series Electronics and Energetics","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2298/fuee2201121s","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 1

Abstract

The paper discusses the problem of the energy losses reduction in electrical networks using a battery energy storage system. One of the main research interests is to define the optimal battery location and control, for the given battery characteristics (battery size, maximum charge / discharge power, discharge depth, etc.), network configuration, network load, and daily load diagram. Battery management involves determining the state of the battery over one period (whether charging or discharging) and with what power it operates. Optimization techniques were used, which were applied to the model described in the paper. The model consists of a fitness function and a constraint. The fitness function is the dependence of the power losses in the network on the current battery power, and it is suggested that the function be fit by a n - order power function. The constraints apply to the very characteristics of the battery for storing electricity. At any time interval, the maximum power that the battery can receive or inject must be met. At any time, the stored energy in the battery must not exceed certain limits. The power of losses in the network is represented as the power of injection into the nodes of the network. The optimization problem was successfully solved by applying a genetic algorithm (GA), when determining optimal battery management. Finally, the optimal battery management algorithm is implemented on the test network. The results of the simulations are presented and discussed.
配电网中蓄电池最优存储位置与控制
本文讨论了利用电池储能系统降低电网能量损耗的问题。主要研究方向之一是在给定电池特性(电池尺寸、最大充放电功率、放电深度等)、网络配置、网络负载和日负载图的情况下,确定最佳电池位置和控制。电池管理包括确定电池在一段时间内的状态(无论是充电还是放电)以及它的运行功率。采用了优化技术,并将其应用于本文所描述的模型。该模型由适应度函数和约束组成。适应度函数是网络中功率损耗对当前电池功率的依赖关系,建议用一个n阶幂函数来拟合。这些限制适用于存储电力的电池的特性。在任何时间间隔,必须满足电池可以接收或注入的最大功率。在任何时候,电池所储存的能量都不能超过一定的限度。网络中损失的功率表示为注入网络节点的功率。在确定最佳电池管理时,应用遗传算法(GA)成功地解决了优化问题。最后,在测试网络上实现了电池最优管理算法。给出了仿真结果并进行了讨论。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 求助全文
来源期刊
Facta Universitatis-Series Electronics and Energetics
Facta Universitatis-Series Electronics and Energetics ENGINEERING, ELECTRICAL & ELECTRONIC-
自引率
16.70%
发文量
10
审稿时长
20 weeks
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
确定
请完成安全验证×
copy
已复制链接
快去分享给好友吧!
我知道了
右上角分享
点击右上角分享
0
联系我们:info@booksci.cn Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。 Copyright © 2023 布克学术 All rights reserved.
京ICP备2023020795号-1
ghs 京公网安备 11010802042870号
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术官方微信