Optimization Study of Carbon Emissions in Wind Power Integrated Systems Based on Optimal Dispatch Algorithm

IF 1.4 Q4 GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY
Xiaohui Zhu, Lisan Zhao
{"title":"Optimization Study of Carbon Emissions in Wind Power Integrated Systems Based on Optimal Dispatch Algorithm","authors":"Xiaohui Zhu, Lisan Zhao","doi":"10.2478/rtuect-2024-0010","DOIUrl":null,"url":null,"abstract":"\n With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable. This paper focuses on optimizing carbon emissions in wind farm generation while considering issues related to wind power integration and carbon trading. An optimal dispatch algorithm was designed with the objective of minimizing total costs, which was then solved using the cuckoo search (CS) algorithm. Additionally, an adaptive improvement was made to the CS algorithm to obtain the improved cuckoo search (ICS) algorithm. An analysis was conducted on a case study with 10 units. The ICS algorithm obtained higher quality solutions, with a total cost of $ 632 719 and a calculation time of 0.51 minutes, which was superior to the solutions obtained by the particle swarm optimization and CS algorithms. Fluctuations in the confidence level of system rotation reserve capacity could lead to variations in the final system cost, which needs to be adjusted according to actual conditions. The dispatch scheme obtained by the ICS algorithm showed reduced carbon emissions, total costs, and better performance when compared with the optimal dispatch algorithm in different scenarios. The results show that the proposed methods are reliable and practical.","PeriodicalId":46053,"journal":{"name":"Environmental and Climate Technologies","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-01-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Environmental and Climate Technologies","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.2478/rtuect-2024-0010","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY","Score":null,"Total":0}
引用次数: 0

Abstract

With the integration of wind power into the power system, dispatch becomes more complex and existing algorithms are no longer applicable. This paper focuses on optimizing carbon emissions in wind farm generation while considering issues related to wind power integration and carbon trading. An optimal dispatch algorithm was designed with the objective of minimizing total costs, which was then solved using the cuckoo search (CS) algorithm. Additionally, an adaptive improvement was made to the CS algorithm to obtain the improved cuckoo search (ICS) algorithm. An analysis was conducted on a case study with 10 units. The ICS algorithm obtained higher quality solutions, with a total cost of $ 632 719 and a calculation time of 0.51 minutes, which was superior to the solutions obtained by the particle swarm optimization and CS algorithms. Fluctuations in the confidence level of system rotation reserve capacity could lead to variations in the final system cost, which needs to be adjusted according to actual conditions. The dispatch scheme obtained by the ICS algorithm showed reduced carbon emissions, total costs, and better performance when compared with the optimal dispatch algorithm in different scenarios. The results show that the proposed methods are reliable and practical.
基于优化调度算法的风电集成系统碳排放优化研究
随着风电并入电力系统,调度变得更加复杂,现有算法已不再适用。本文的重点是优化风力发电场发电过程中的碳排放,同时考虑与风电集成和碳交易相关的问题。本文以总成本最小化为目标设计了一种优化调度算法,然后使用布谷鸟搜索(CS)算法对其进行求解。此外,还对 CS 算法进行了自适应改进,得到了改进的布谷鸟搜索(ICS)算法。对一个包含 10 个单元的案例研究进行了分析。ICS 算法获得了更高质量的解决方案,总成本为 632 719 美元,计算时间为 0.51 分钟,优于粒子群优化算法和 CS 算法获得的解决方案。系统旋转备用容量置信度的波动会导致最终系统成本的变化,需要根据实际情况进行调整。在不同情况下,ICS 算法得到的调度方案与最优调度算法相比,碳排放量减少,总成本降低,性能更好。结果表明,所提出的方法是可靠和实用的。
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
Environmental and Climate Technologies
Environmental and Climate Technologies GREEN & SUSTAINABLE SCIENCE & TECHNOLOGY-
CiteScore
3.10
自引率
28.60%
发文量
0
审稿时长
16 weeks
期刊介绍: Environmental and Climate Technologies provides a forum for information on innovation, research and development in the areas of environmental science, energy resources and processes, innovative technologies and energy efficiency. Authors are encouraged to submit manuscripts which cover the range from bioeconomy, sustainable technology development, life cycle analysis, eco-design, climate change mitigation, innovative solutions for pollution reduction to resilience, the energy efficiency of buildings, secure and sustainable energy supplies. The Journal ensures international publicity for original research and innovative work. A variety of themes are covered through a multi-disciplinary approach, one which integrates all aspects of environmental science: -Sustainability of technology development- Bioeconomy- Cleaner production, end of pipe production- Zero emission technologies- Eco-design- Life cycle analysis- Eco-efficiency- Environmental impact assessment- Environmental management systems- Resilience- Energy and carbon markets- Greenhouse gas emission reduction and climate technologies- Methodologies for the evaluation of sustainability- Renewable energy resources- Solar, wind, geothermal, hydro energy, biomass sources: algae, wood, straw, biogas, energetic plants and organic waste- Waste management- Quality of outdoor and indoor environment- Environmental monitoring and evaluation- Heat and power generation, including district heating and/or cooling- Energy efficiency.
×
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