Adaptive Grey Wolf Optimization for Weightage-based Combined Economic Emission Dispatch in Hybrid Renewable Energy Systems

Q2 Social Sciences

New Review of Information Networking Pub Date : 2017-07-03 DOI:10.1080/13614576.2017.1368406

S. Halbhavi, D. Kulkarni, S. K. Ambekar, D. Manjunath

{"title":"Adaptive Grey Wolf Optimization for Weightage-based Combined Economic Emission Dispatch in Hybrid Renewable Energy Systems","authors":"S. Halbhavi, D. Kulkarni, S. K. Ambekar, D. Manjunath","doi":"10.1080/13614576.2017.1368406","DOIUrl":null,"url":null,"abstract":"ABSTRACT Nowadays, the electric power networks comprise diverse renewable energy resources, with the rapid development of technologies. In this scenario, the optimal Economic Dispatch is required by the power system due to the increment of power generation cost and ever growing demand of electrical energy. Thus, the reduction of power generation cost in terms of fuel cost and emission cost has become one of the main challenges in the power system. Accordingly, this article proposes the Grey Wolf Optimization-Extended Searching (GWO-ES) algorithm to provide the excellent solution for the problems regarding Combined Economic and Emission Dispatch (CEED). It validates the robustness of the proposed algorithm in seven Hybrid Renewable Energy Systems (HRES) test bus systems, which combines the wind turbine along with the thermal power plant. Furthermore, it compares the performance of the proposed GWO-ES algorithm with conventional algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and GWO. Next, the article emulates a valuable convergence analysis and justification for the quality of CEED through the GWO-ES algorithm. Finally, the result was compared to four other conventional algorithms to assure the efficiency of the proposed algorithm in terms of fuel cost and emission cost reduction.","PeriodicalId":35726,"journal":{"name":"New Review of Information Networking","volume":null,"pages":null},"PeriodicalIF":0.0000,"publicationDate":"2017-07-03","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://sci-hub-pdf.com/10.1080/13614576.2017.1368406","citationCount":"3","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"New Review of Information Networking","FirstCategoryId":"1085","ListUrlMain":"https://doi.org/10.1080/13614576.2017.1368406","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"Social Sciences","Score":null,"Total":0}

引用次数: 3

Abstract

ABSTRACT Nowadays, the electric power networks comprise diverse renewable energy resources, with the rapid development of technologies. In this scenario, the optimal Economic Dispatch is required by the power system due to the increment of power generation cost and ever growing demand of electrical energy. Thus, the reduction of power generation cost in terms of fuel cost and emission cost has become one of the main challenges in the power system. Accordingly, this article proposes the Grey Wolf Optimization-Extended Searching (GWO-ES) algorithm to provide the excellent solution for the problems regarding Combined Economic and Emission Dispatch (CEED). It validates the robustness of the proposed algorithm in seven Hybrid Renewable Energy Systems (HRES) test bus systems, which combines the wind turbine along with the thermal power plant. Furthermore, it compares the performance of the proposed GWO-ES algorithm with conventional algorithms such as Genetic Algorithm (GA), Particle Swarm Optimization (PSO), Differential Evolution (DE), and GWO. Next, the article emulates a valuable convergence analysis and justification for the quality of CEED through the GWO-ES algorithm. Finally, the result was compared to four other conventional algorithms to assure the efficiency of the proposed algorithm in terms of fuel cost and emission cost reduction.

查看原文本刊更多论文

基于权重的混合可再生能源系统联合经济排放调度自适应灰狼优化

摘要当今，随着技术的飞速发展，电网由多种可再生能源组成。在这种情况下，由于发电成本的增加和电能需求的不断增长，电力系统需要最佳经济调度。因此，降低燃料成本和排放成本方面的发电成本已成为电力系统面临的主要挑战之一。因此，本文提出了灰狼优化扩展搜索（GWO-ES）算法，为经济与排放联合调度（CEED）问题提供了良好的解决方案。它在七个混合可再生能源系统（HRES）测试总线系统中验证了所提出算法的稳健性，该系统将风力涡轮机与火力发电厂结合在一起。此外，将所提出的GWO-ES算法与遗传算法（GA）、粒子群优化算法（PSO）、差分进化算法（DE）和GWO等传统算法的性能进行了比较。接下来，本文通过GWO-ES算法对CEED的质量进行了有价值的收敛性分析和论证。最后，将结果与其他四种传统算法进行了比较，以确保所提出的算法在降低燃料成本和排放成本方面的效率。

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

New Review of Information Networking Social Sciences-Education

CiteScore

2.10

自引率

0.00%

发文量

期刊介绍： Information networking is an enabling technology with the potential to integrate and transform information provision, communication and learning. The New Review of Information Networking, published biannually, provides an expert source on the needs and behaviour of the network user; the role of networks in teaching, learning, research and scholarly communication; the implications of networks for library and information services; the development of campus and other information strategies; the role of information publishers on the networks; policies for funding and charging for network and information services; and standards and protocols for network applications.