电池储能系统的智能优化:概述

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Energy and AI Pub Date : 2024-05-22 DOI:10.1016/j.egyai.2024.100378
Hui Song , Chen Liu , Ali Moradi Amani , Mingchen Gu , Mahdi Jalili , Lasantha Meegahapola , Xinghuo Yu , George Dickeson
{"title":"电池储能系统的智能优化:概述","authors":"Hui Song ,&nbsp;Chen Liu ,&nbsp;Ali Moradi Amani ,&nbsp;Mingchen Gu ,&nbsp;Mahdi Jalili ,&nbsp;Lasantha Meegahapola ,&nbsp;Xinghuo Yu ,&nbsp;George Dickeson","doi":"10.1016/j.egyai.2024.100378","DOIUrl":null,"url":null,"abstract":"<div><p>The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.</p></div>","PeriodicalId":34138,"journal":{"name":"Energy and AI","volume":"17 ","pages":"Article 100378"},"PeriodicalIF":9.6000,"publicationDate":"2024-05-22","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666546824000442/pdfft?md5=cb403cbe69b0f705c31ab5b68bcb0bdd&pid=1-s2.0-S2666546824000442-main.pdf","citationCount":"0","resultStr":"{\"title\":\"Smart optimization in battery energy storage systems: An overview\",\"authors\":\"Hui Song ,&nbsp;Chen Liu ,&nbsp;Ali Moradi Amani ,&nbsp;Mingchen Gu ,&nbsp;Mahdi Jalili ,&nbsp;Lasantha Meegahapola ,&nbsp;Xinghuo Yu ,&nbsp;George Dickeson\",\"doi\":\"10.1016/j.egyai.2024.100378\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div><p>The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.</p></div>\",\"PeriodicalId\":34138,\"journal\":{\"name\":\"Energy and AI\",\"volume\":\"17 \",\"pages\":\"Article 100378\"},\"PeriodicalIF\":9.6000,\"publicationDate\":\"2024-05-22\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000442/pdfft?md5=cb403cbe69b0f705c31ab5b68bcb0bdd&pid=1-s2.0-S2666546824000442-main.pdf\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Energy and AI\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://www.sciencedirect.com/science/article/pii/S2666546824000442\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q1\",\"JCRName\":\"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Energy and AI","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666546824000442","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE","Score":null,"Total":0}
引用次数: 0

摘要

对生态友好型环境的日益推动促使人们利用可再生能源(RES)发电。可再生能源在发电中所占比例的不断提高带来了潜在的挑战,包括发电输出的不确定性、频率波动和电压调节能力不足。作为应对这些挑战的解决方案,储能系统(ESS)在根据需要储存和释放电能方面发挥着至关重要的作用。电池储能系统(BESS)在最大限度地提高配电网络能效和不同利益相关者的利益方面具有巨大潜力。这可以通过优化布局、大小、充放电调度和控制来实现,所有这些都有助于提高配电网的整体性能。在本文中,我们将全面概述不同应用中的 BESS 运行、优化和建模,以及基于数学和人工智能(AI)的优化技术如何促进 BESS 充放电调度。我们还讨论了 BESS 运行、BESS 中的人工智能以及新兴技术(如物联网、人工智能和大数据)如何影响 BESS 的发展的一些潜在的未来机遇和挑战。
本文章由计算机程序翻译,如有差异,请以英文原文为准。

摘要图片

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
Smart optimization in battery energy storage systems: An overview

The increasing drive towards eco-friendly environment motivates the generation of energy from renewable energy sources (RESs). The rising share of RESs in power generation poses potential challenges, including uncertainties in generation output, frequency fluctuations, and insufficient voltage regulation capabilities. As a solution to these challenges, energy storage systems (ESSs) play a crucial role in storing and releasing power as needed. Battery energy storage systems (BESSs) provide significant potential to maximize the energy efficiency of a distribution network and the benefits of different stakeholders. This can be achieved through optimizing placement, sizing, charge/discharge scheduling, and control, all of which contribute to enhancing the overall performance of the network. In this paper, we provide a comprehensive overview of BESS operation, optimization, and modeling in different applications, and how mathematical and artificial intelligence (AI)-based optimization techniques contribute to BESS charging and discharging scheduling. We also discuss some potential future opportunities and challenges of the BESS operation, AI in BESSs, and how emerging technologies, such as internet of things, AI, and big data impact the development of BESSs.

求助全文
通过发布文献求助,成功后即可免费获取论文全文。 去求助
来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
期刊最新文献
Predicting the thermal conductivity of polymer composites with one-dimensional oriented fillers using the combination of deep learning and ensemble learning A hybrid wind power prediction model based on seasonal feature decomposition and enhanced feature extraction Integrating local knowledge with ChatGPT-like large-scale language models for enhanced societal comprehension of carbon neutrality Optimization of a Bayesian game for Peer-to-Peer trading among prosumers under incomplete information via a CNN-LSTM-ATT Parameter sensitivity analysis for diesel spray penetration prediction based on GA-BP neural network
×
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
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 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 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1