The application of equilibrium optimizer for solving modern economic load dispatch problem considering renewable energies and multiple-fuel thermal units

H. Nguyen, K. H. Truong, N. A. Le
{"title":"The application of equilibrium optimizer for solving modern economic load dispatch problem considering renewable energies and multiple-fuel thermal units","authors":"H. Nguyen, K. H. Truong, N. A. Le","doi":"10.14710/ijred.2023.52835","DOIUrl":null,"url":null,"abstract":"This study presents a modern version of the economic load dispatch (MELD) problem with the contribution of renewable energies and conventional energy, including wind, solar and thermal power plants. In the study, reduction of electricity generation cost is the first priority, while the use of multiple fuels in the thermal power plant is considered in addition to the consideration of all constraints of power plants. Two meta-heuristic algorithms, one conventional and one recently published, including Particle swarm optimization (PSO) and Equilibrium optimizer (EO), are applied to determine the optimal solutions for MELD. A power system with ten thermal power plants using multiple fossil fuels, one wind power plant, and three solar power plants is utilized to evaluate the performance of both PSO and EO. Unlike other previous studies, this paper considers the MELD problem with the change of load demands over one day with 24 periods as a real power system. In addition, the power generated by both wind and solar power plants varies at each period. The results obtained by applying the two algorithms indicate that EO is completely superior to PSO, and the solutions found by EO can satisfy all constraints. Particularly in Case 1 with different load demand values, EO achieves better total electricity production cost (TEGC) than PSO by 0.75%, 0.87%, 0.13%, and 0.45% for the loads of 2400 MW, 2500 MW, 2600 MW and 2700 MW. Moreover, EO also provides a faster response capability over PSO through the four subcases although EO and PSO are run by the same selection of control parameters. In Case 2, the high efficiency provided by EO is still maintained, though the scale of the considered problem has been substantially enlarged. Specifically, EO can save $51.2 compared to PSO for the minimum TEGC. The savings cost is equal to 0.33% for the whole schedule of 24 hours. With these results, EO is acknowledged as a favourable search method for dealing with the MELD problem. Besides, this study also points out the difference in performance between a modern meta-heuristic algorithm (EO) and the classical one (PSO). The modern metaheuristic algorithm with special structure is highly valuable for complicated problem as MELD.","PeriodicalId":44938,"journal":{"name":"International Journal of Renewable Energy Development-IJRED","volume":null,"pages":null},"PeriodicalIF":2.4000,"publicationDate":"2023-04-26","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"International Journal of Renewable Energy Development-IJRED","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.14710/ijred.2023.52835","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0

Abstract

This study presents a modern version of the economic load dispatch (MELD) problem with the contribution of renewable energies and conventional energy, including wind, solar and thermal power plants. In the study, reduction of electricity generation cost is the first priority, while the use of multiple fuels in the thermal power plant is considered in addition to the consideration of all constraints of power plants. Two meta-heuristic algorithms, one conventional and one recently published, including Particle swarm optimization (PSO) and Equilibrium optimizer (EO), are applied to determine the optimal solutions for MELD. A power system with ten thermal power plants using multiple fossil fuels, one wind power plant, and three solar power plants is utilized to evaluate the performance of both PSO and EO. Unlike other previous studies, this paper considers the MELD problem with the change of load demands over one day with 24 periods as a real power system. In addition, the power generated by both wind and solar power plants varies at each period. The results obtained by applying the two algorithms indicate that EO is completely superior to PSO, and the solutions found by EO can satisfy all constraints. Particularly in Case 1 with different load demand values, EO achieves better total electricity production cost (TEGC) than PSO by 0.75%, 0.87%, 0.13%, and 0.45% for the loads of 2400 MW, 2500 MW, 2600 MW and 2700 MW. Moreover, EO also provides a faster response capability over PSO through the four subcases although EO and PSO are run by the same selection of control parameters. In Case 2, the high efficiency provided by EO is still maintained, though the scale of the considered problem has been substantially enlarged. Specifically, EO can save $51.2 compared to PSO for the minimum TEGC. The savings cost is equal to 0.33% for the whole schedule of 24 hours. With these results, EO is acknowledged as a favourable search method for dealing with the MELD problem. Besides, this study also points out the difference in performance between a modern meta-heuristic algorithm (EO) and the classical one (PSO). The modern metaheuristic algorithm with special structure is highly valuable for complicated problem as MELD.
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
平衡优化器在考虑可再生能源和多燃料机组的现代经济负荷调度问题中的应用
本研究提出了一个现代版本的经济负荷调度(MELD)问题,其中包括可再生能源和传统能源的贡献,包括风能、太阳能和火力发电厂。本研究以降低发电成本为第一要务,在考虑电厂各方面约束条件的基础上,考虑了火电厂多种燃料的使用。采用粒子群算法(PSO)和平衡优化算法(EO)两种传统的和最近发表的元启发式算法来确定MELD的最优解。利用一个由10个使用多种化石燃料的火力发电厂、1个风力发电厂和3个太阳能发电厂组成的电力系统来评估PSO和EO的性能。与以往的研究不同,本文考虑的是一个真实的电力系统,即24个时段的一天负荷需求变化的MELD问题。此外,风能和太阳能发电厂的发电量在每个时期都是不同的。两种算法的应用结果表明,蚁群算法完全优于粒子群算法,且蚁群算法的解能够满足所有约束条件。尤其在Case 1不同负荷需求值的情况下,在2400 MW、2500 MW、2600 MW和2700 MW负荷下,EO比PSO实现的总电力生产成本(TEGC)分别高0.75%、0.87%、0.13%和0.45%。此外,尽管EO和PSO在相同的控制参数选择下运行,但EO通过四个子案例也提供了比PSO更快的响应能力。在案例2中,尽管所考虑问题的规模已大大扩大,但EO提供的高效率仍然保持不变。具体来说,与PSO相比,EO可以为最低TEGC节省51.2美元。节约成本为24小时全程0.33%。基于这些结果,EO被认为是处理MELD问题的一种有利的搜索方法。此外,本文还指出了现代元启发式算法(EO)与经典元启发式算法(PSO)在性能上的差异。具有特殊结构的现代元启发式算法对MELD等复杂问题具有很高的应用价值。
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 去求助
来源期刊
CiteScore
4.50
自引率
16.00%
发文量
83
审稿时长
8 weeks
期刊最新文献
Performance characterization of a novel PV/T panel with nanofluids under the climatic conditions of Muscat, Oman Solid waste management by RDF production from landfilled waste to renewable fuel of Nonthaburi Computational prediction of green fuels from crude palm oil in fluid catalytic cracking riser Energy performance evaluation of a photovoltaic thermal phase change material (PVT-PCM) using a spiral flow configuration Exploring the link between green energy, CO2 emissions, exchange rate and economic growth: Perspective from emerging South Asian countries
×
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