利用电力市场模型改进和分析工业用户峰值转移需求响应方案

IF 2 4区 计算机科学 Q3 COMPUTER SCIENCE, HARDWARE & ARCHITECTURE New Generation Computing Pub Date : 2024-09-16 DOI:10.1007/s00354-024-00282-1
Long Cheng, Kiyoshi Izumi, Masanori Hirano
{"title":"利用电力市场模型改进和分析工业用户峰值转移需求响应方案","authors":"Long Cheng, Kiyoshi Izumi, Masanori Hirano","doi":"10.1007/s00354-024-00282-1","DOIUrl":null,"url":null,"abstract":"<p>Electricity procurement of industrial consumers is becoming more and more complicated, involving a combination of various procurement methods due to electricity liberalization and decarbonization trends. This study analyzed and improved power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the electricity market and introduced a factory agent using various procurement methods including PV, FC, storage batteries (SB), and DR. Firstly, we created a new procurement strategy utilizing all methods. Then, by using the simulation model, we assessed the effectiveness of the existing peak shift DR scenarios in terms of cost reduction efficiency. Results revealed that the introduction of PV has led to a counterproductive outcome for DR. Based on the results, we proposed two methods to improve the effectiveness of DR, namely considering the operation of PV in the DR scenario and expanding the range of optional time periods for DR activation. Finally, we made three new DR scenarios based on our proposal. Through experiment, the new scenarios were confirmed to be effective in cost-effectiveness for decarbonization.</p>","PeriodicalId":54726,"journal":{"name":"New Generation Computing","volume":"18 1","pages":""},"PeriodicalIF":2.0000,"publicationDate":"2024-09-16","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model\",\"authors\":\"Long Cheng, Kiyoshi Izumi, Masanori Hirano\",\"doi\":\"10.1007/s00354-024-00282-1\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p>Electricity procurement of industrial consumers is becoming more and more complicated, involving a combination of various procurement methods due to electricity liberalization and decarbonization trends. This study analyzed and improved power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the electricity market and introduced a factory agent using various procurement methods including PV, FC, storage batteries (SB), and DR. Firstly, we created a new procurement strategy utilizing all methods. Then, by using the simulation model, we assessed the effectiveness of the existing peak shift DR scenarios in terms of cost reduction efficiency. Results revealed that the introduction of PV has led to a counterproductive outcome for DR. Based on the results, we proposed two methods to improve the effectiveness of DR, namely considering the operation of PV in the DR scenario and expanding the range of optional time periods for DR activation. Finally, we made three new DR scenarios based on our proposal. Through experiment, the new scenarios were confirmed to be effective in cost-effectiveness for decarbonization.</p>\",\"PeriodicalId\":54726,\"journal\":{\"name\":\"New Generation Computing\",\"volume\":\"18 1\",\"pages\":\"\"},\"PeriodicalIF\":2.0000,\"publicationDate\":\"2024-09-16\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"New Generation Computing\",\"FirstCategoryId\":\"94\",\"ListUrlMain\":\"https://doi.org/10.1007/s00354-024-00282-1\",\"RegionNum\":4,\"RegionCategory\":\"计算机科学\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q3\",\"JCRName\":\"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"New Generation Computing","FirstCategoryId":"94","ListUrlMain":"https://doi.org/10.1007/s00354-024-00282-1","RegionNum":4,"RegionCategory":"计算机科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"COMPUTER SCIENCE, HARDWARE & ARCHITECTURE","Score":null,"Total":0}
引用次数: 0

摘要

在电力自由化和去碳化的趋势下,工业用户的电力采购变得越来越复杂,涉及多种采购方式的组合。本研究通过模拟电力市场的多代理模型,分析并改进了一家工厂的电力采购策略,以实现碳中和,并引入了一家工厂代理,使用光伏、FC、蓄电池(SB)和 DR 等多种采购方法。首先,我们利用所有方法创建了新的采购策略。然后,我们利用仿真模型评估了现有调峰 DR 方案在降低成本效率方面的有效性。结果显示,光伏的引入导致了 DR 的反效果。在此基础上,我们提出了两种改善 DR 效果的方法,即在 DR 方案中考虑光伏的运行和扩大 DR 启动的可选时段范围。最后,我们在此基础上提出了三种新的 DR 方案。通过实验,证实了新方案在去碳化成本效益方面的有效性。
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
Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model

Electricity procurement of industrial consumers is becoming more and more complicated, involving a combination of various procurement methods due to electricity liberalization and decarbonization trends. This study analyzed and improved power procurement strategies for a factory to achieve carbon neutralization through a multi-agent model simulating the electricity market and introduced a factory agent using various procurement methods including PV, FC, storage batteries (SB), and DR. Firstly, we created a new procurement strategy utilizing all methods. Then, by using the simulation model, we assessed the effectiveness of the existing peak shift DR scenarios in terms of cost reduction efficiency. Results revealed that the introduction of PV has led to a counterproductive outcome for DR. Based on the results, we proposed two methods to improve the effectiveness of DR, namely considering the operation of PV in the DR scenario and expanding the range of optional time periods for DR activation. Finally, we made three new DR scenarios based on our proposal. Through experiment, the new scenarios were confirmed to be effective in cost-effectiveness for decarbonization.

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
New Generation Computing
New Generation Computing 工程技术-计算机:理论方法
CiteScore
5.90
自引率
15.40%
发文量
47
审稿时长
>12 weeks
期刊介绍: The journal is specially intended to support the development of new computational and cognitive paradigms stemming from the cross-fertilization of various research fields. These fields include, but are not limited to, programming (logic, constraint, functional, object-oriented), distributed/parallel computing, knowledge-based systems, agent-oriented systems, and cognitive aspects of human embodied knowledge. It also encourages theoretical and/or practical papers concerning all types of learning, knowledge discovery, evolutionary mechanisms, human cognition and learning, and emergent systems that can lead to key technologies enabling us to build more complex and intelligent systems. The editorial board hopes that New Generation Computing will work as a catalyst among active researchers with broad interests by ensuring a smooth publication process.
期刊最新文献
Infant Walking and Everyday Experience: Unraveling the Development of Behavior from Motor Development Improvement and Analysis of Peak Shift Demand Response Scenarios of Industrial Consumers Using an Electricity Market Model Chaotic Satin Bowerbird Optimizer Based Advanced AI Techniques for Detection of COVID-19 Diseases from CT Scans Images Dance Information Processing: Computational Approaches for Assisting Dance Composition Intelligent Bayesian Inference for Multiclass Lung Infection Diagnosis: Network Analysis of Ranked Gray Level Co-occurrence (GLCM) Features
×
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