Simulation on Supplier Side Bidding Strategy at Day-ahead Electricity Market Using Ant Lion Optimizer | Journal of Computational and Cognitive Engineering

<!DOCTYPE html> <html lang="en" xml:lang="en"> <head> <meta charset="utf-8"> <meta name="viewport" content="width=device-width, initial-scale=1.0"> <title> Simulation on Supplier Side Bidding Strategy at Day-ahead Electricity Market Using Ant Lion Optimizer | Journal of Computational and Cognitive Engineering </title> <link rel="icon" href="https://ojs.bonviewpress.com/public/journals/4/favicon_en_US.png"> <meta name="generator" content="Open Journal Systems 3.4.0.7"> <meta name="gs_meta_revision" content="1.1"/> <meta name="citation_journal_title" content="Journal of Computational and Cognitive Engineering"/> <meta name="citation_journal_abbrev" content="JCCE"/> <meta name="citation_issn" content="2810-9503"/> <meta name="citation_author" content="Kavita Jain"/> <meta name="citation_author_institution" content="Department of Electrical Engineering, Swami Keshvannad Institute of Technology, India"/> <meta name="citation_author" content="Akash Saxena"/> <meta name="citation_author_institution" content="Department of Electrical Engineering, Swami Keshvannad Institute of Technology, India"/> <meta name="citation_title" content="Simulation on Supplier Side Bidding Strategy at Day-ahead Electricity Market Using Ant Lion Optimizer"/> <meta name="citation_language" content="en"/> <meta name="citation_date" content="2023"/> <meta name="citation_volume" content="2"/> <meta name="citation_issue" content="1"/> <meta name="citation_firstpage" content="17"/> <meta name="citation_lastpage" content="27"/> <meta name="citation_doi" content="10.47852/bonviewJCCE2202160"/> <meta name="citation_abstract_html_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/160"/> <meta name="citation_abstract" xml:lang="en" content="In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding strategy in a day-ahead Electricity Market (EM). Optimal bidding is one of the major challenges of EM after deregulation. Deregulation is nothing but abolishing the market rules and unbundling the vertically integrated utilities. In EM, the main objective of Generating Companies (GenCos) is to bid optimally that maximizes its profit. Thus, for attaining maximum profit every supplier makes a strategy for acquiring the profitable bids. The strategic bidding technique for a GenCo in a day-ahead market for multi-hour selling is developed. The challenge of determining the market clearing pricing, load dispatch, and bid cost under three distinct capacities and price blocks is handled by the algorithm using this procedure. In this model, different probability distribution functions are used to explain rivals bidding behavior: normal, lognormal, gamma, and Weibull. Monte Carlo simulations are also carried out. The ALO is applied to maximize the profit of GenCos. The described method was implemented in MATLAB (2019) and evaluated using a standard test case from the literature. The numerical simulations are also displayed and contrasted. It is worth noting that the offered strategy produces the best profit outcomes. 聽 Received: 16 January 2022 | Revised: 12 March 2022 | Accepted: 17 March 2022 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work."/> <meta name="citation_keywords" xml:lang="en" content="electricity market"/> <meta name="citation_keywords" xml:lang="en" content="bidding strategy"/> <meta name="citation_keywords" xml:lang="en" content="Ant Lion Optimizer"/> <meta name="citation_keywords" xml:lang="en" content=" probability distribution functions"/> <meta name="citation_pdf_url" content="https://ojs.bonviewpress.com/index.php/JCCE/article/download/160/107"/> <link rel="schema.DC" href="http://purl.org/dc/elements/1.1/" /> <meta name="DC.Creator.PersonalName" content="Kavita Jain"/> <meta name="DC.Creator.PersonalName" content="Akash Saxena"/> <meta name="DC.Date.created" scheme="ISO8601" content="2022-03-21"/> <meta name="DC.Date.dateSubmitted" scheme="ISO8601" content="2022-01-16"/> <meta name="DC.Date.issued" scheme="ISO8601" content="2023-02-22"/> <meta name="DC.Date.modified" scheme="ISO8601" content="2024-01-10"/> <meta name="DC.Description" xml:lang="en" content="In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding strategy in a day-ahead Electricity Market (EM). Optimal bidding is one of the major challenges of EM after deregulation. Deregulation is nothing but abolishing the market rules and unbundling the vertically integrated utilities. In EM, the main objective of Generating Companies (GenCos) is to bid optimally that maximizes its profit. Thus, for attaining maximum profit every supplier makes a strategy for acquiring the profitable bids. The strategic bidding technique for a GenCo in a day-ahead market for multi-hour selling is developed. The challenge of determining the market clearing pricing, load dispatch, and bid cost under three distinct capacities and price blocks is handled by the algorithm using this procedure. In this model, different probability distribution functions are used to explain rivals bidding behavior: normal, lognormal, gamma, and Weibull. Monte Carlo simulations are also carried out. The ALO is applied to maximize the profit of GenCos. The described method was implemented in MATLAB (2019) and evaluated using a standard test case from the literature. The numerical simulations are also displayed and contrasted. It is worth noting that the offered strategy produces the best profit outcomes. 聽 Received: 16 January 2022 | Revised: 12 March 2022 | Accepted: 17 March 2022 聽 Conflicts of Interest The authors declare that they have no conflicts of interest to this work."/> <meta name="DC.Format" scheme="IMT" content="application/pdf"/> <meta name="DC.Identifier" content="160"/> <meta name="DC.Identifier.pageNumber" content="17-27"/> <meta name="DC.Identifier.DOI" content="10.47852/bonviewJCCE2202160"/> <meta name="DC.Identifier.URI" content="https://ojs.bonviewpress.com/index.php/JCCE/article/view/160"/> <meta name="DC.Language" scheme="ISO639-1" content="en"/> <meta name="DC.Rights" content="Copyright (c) 2022 Authors"/> <meta name="DC.Rights" content="https://creativecommons.org/licenses/by/4.0/"/> <meta name="DC.Source" content="Journal of Computational and Cognitive Engineering"/> <meta name="DC.Source.ISSN" content="2810-9503"/> <meta name="DC.Source.Issue" content="1"/> <meta 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class="pkp_screen_reader">Authors</h2> <ul class="authors"> <li> <span class="name"> Kavita Jain </span> <span class="affiliation"> Department of Electrical Engineering, Swami Keshvannad Institute of Technology, India </span> </li> <li> <span class="name"> Akash Saxena </span> <span class="affiliation"> Department of Electrical Engineering, Swami Keshvannad Institute of Technology, India </span> <span class="orcid"> <svg class="orcid_icon" viewBox="0 0 256 256" aria-hidden="true"> <style type="text/css"> .st0{fill:#A6CE39;} .st1{fill:#FFFFFF;} </style> <path class="st0" d="M256,128c0,70.7-57.3,128-128,128C57.3,256,0,198.7,0,128C0,57.3,57.3,0,128,0C198.7,0,256,57.3,256,128z"/> <g> <path class="st1" d="M86.3,186.2H70.9V79.1h15.4v48.4V186.2z"/> <path class="st1" d="M108.9,79.1h41.6c39.6,0,57,28.3,57,53.6c0,27.5-21.5,53.6-56.8,53.6h-41.8V79.1z M124.3,172.4h24.5 c34.9,0,42.9-26.5,42.9-39.7c0-21.5-13.7-39.7-43.7-39.7h-23.7V172.4z"/> <path class="st1" d="M88.7,56.8c0,5.5-4.5,10.1-10.1,10.1c-5.6,0-10.1-4.6-10.1-10.1c0-5.6,4.5-10.1,10.1-10.1 C84.2,46.7,88.7,51.3,88.7,56.8z"/> </g> </svg> <a href="https://orcid.org/0000-0002-1820-8024" target="_blank"> https://orcid.org/0000-0002-1820-8024 </a> </span> </li> </ul> </section> <section class="item doi"> <h2 class="label"> DOI: </h2> <span class="value"> <a href="https://doi.org/10.47852/bonviewJCCE2202160"> https://doi.org/10.47852/bonviewJCCE2202160 </a> </span> </section> <section class="item keywords"> <h2 class="label"> Keywords: </h2> <span class="value"> electricity market, bidding strategy, Ant Lion Optimizer, probability distribution functions </span> </section> <section class="item abstract"> <h2 class="label">Abstract</h2> <p>In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding strategy in a day-ahead Electricity Market (EM). Optimal bidding is one of the major challenges of EM after deregulation. Deregulation is nothing but abolishing the market rules and unbundling the vertically integrated utilities. In EM, the main objective of Generating Companies (GenCos) is to bid optimally that maximizes its profit. Thus, for attaining maximum profit every supplier makes a strategy for acquiring the profitable bids. The strategic bidding technique for a GenCo in a day-ahead market for multi-hour selling is developed. The challenge of determining the market clearing pricing, load dispatch, and bid cost under three distinct capacities and price blocks is handled by the algorithm using this procedure. In this model, different probability distribution functions are used to explain rivals bidding behavior: normal, lognormal, gamma, and Weibull. Monte Carlo simulations are also carried out. The ALO is applied to maximize the profit of GenCos. The described method was implemented in MATLAB (2019) and evaluated using a standard test case from the literature. The numerical simulations are also displayed and contrasted. It is worth noting that the offered strategy produces the best profit outcomes.</p> <p>聽</p> <p><strong>Received</strong>: 16 January 2022 | <strong>Revised</strong>: 12 March 2022 | <strong>Accepted</strong>: 17 March 2022</p> <p>聽</p> <p><strong>Conflicts of Interest</strong></p> <p>The authors declare that they have no conflicts of interest to this work.</p> </section> <br /><div class="separator"></div><div class="item abstract" id="trendmd-suggestions"></div><script defer src='//js.trendmd.com/trendmd.min.js' data-trendmdconfig='{"website_id":"89267", "element":"#trendmd-suggestions"}'></script><div class="item downloads_chart"> <h3 class="label"> Metrics </h3> <div id="paperbuzz"><div id="loading">Metrics Loading ...</div></div> <script type="text/javascript"> window.onload = function () { var options = { paperbuzzStatsJson: JSON.parse('{\"altmetrics_sources\":[{\"events\":null,\"events_count\":426,\"events_count_by_day\":[{\"count\":\"4\",\"date\":\"2022-03-21\"},{\"count\":\"2\",\"date\":\"2022-03-22\"},{\"count\":\"4\",\"date\":\"2022-03-26\"},{\"count\":\"2\",\"date\":\"2022-03-27\"},{\"count\":\"4\",\"date\":\"2022-03-28\"},{\"count\":\"2\",\"date\":\"2022-03-30\"},{\"count\":\"2\",\"date\":\"2022-03-31\"},{\"count\":\"1\",\"date\":\"2022-04-04\"},{\"count\":\"5\",\"date\":\"2022-04-05\"},{\"count\":\"1\",\"date\":\"2022-04-06\"},{\"count\":\"14\",\"date\":\"2022-04-08\"},{\"count\":\"8\",\"date\":\"2022-04-09\"},{\"count\":\"4\",\"date\":\"2022-04-12\"},{\"count\":\"3\",\"date\":\"2022-04-14\"},{\"count\":\"1\",\"date\":\"2022-04-18\"},{\"count\":\"1\",\"date\":\"2022-04-19\"}],\"events_count_by_month\":[{\"count\":\"20\",\"date\":\"2022-03\"},{\"count\":\"39\",\"date\":\"2022-04\"},{\"count\":\"13\",\"date\":\"2022-05\"},{\"count\":\"13\",\"date\":\"2022-06\"},{\"count\":\"5\",\"date\":\"2022-07\"},{\"count\":\"12\",\"date\":\"2022-08\"},{\"count\":\"10\",\"date\":\"2022-09\"},{\"count\":\"6\",\"date\":\"2022-10\"},{\"count\":\"3\",\"date\":\"2022-11\"},{\"count\":\"4\",\"date\":\"2022-12\"},{\"count\":\"5\",\"date\":\"2023-01\"},{\"count\":\"18\",\"date\":\"2023-02\"},{\"count\":\"15\",\"date\":\"2023-03\"},{\"count\":\"20\",\"date\":\"2023-04\"},{\"count\":\"28\",\"date\":\"2023-05\"},{\"count\":\"11\",\"date\":\"2023-06\"},{\"count\":\"11\",\"date\":\"2023-07\"},{\"count\":\"7\",\"date\":\"2023-08\"},{\"count\":\"13\",\"date\":\"2023-09\"},{\"count\":\"23\",\"date\":\"2023-10\"},{\"count\":\"7\",\"date\":\"2023-11\"},{\"count\":\"19\",\"date\":\"2023-12\"},{\"count\":\"11\",\"date\":\"2024-01\"},{\"count\":\"9\",\"date\":\"2024-02\"},{\"count\":\"13\",\"date\":\"2024-03\"},{\"count\":\"11\",\"date\":\"2024-04\"},{\"count\":\"10\",\"date\":\"2024-05\"},{\"count\":\"14\",\"date\":\"2024-06\"},{\"count\":\"9\",\"date\":\"2024-07\"},{\"count\":\"11\",\"date\":\"2024-08\"},{\"count\":\"19\",\"date\":\"2024-09\"},{\"count\":\"11\",\"date\":\"2024-10\"},{\"count\":\"6\",\"date\":\"2024-11\"}],\"events_count_by_year\":[{\"count\":\"125\",\"date\":\"2022\"},{\"count\":\"177\",\"date\":\"2023\"},{\"count\":\"124\",\"date\":\"2024\"}],\"source\":{\"display_name\":\"File downloads\"},\"source_id\":\"fileDownloads\"}],\"crossref_event_data_url\":\"https:\\/\\/api.eventdata.crossref.org\\/v1\\/events?rows=1000&filter=from-collected-date:1990-01-01,until-collected-date:2099-01-01,obj-id:10.47852\\/bonviewjcce2202160\",\"doi\":\"10.47852\\/bonviewjcce2202160\",\"metadata\":{\"DOI\":\"10.47852\\/bonviewjcce2202160\",\"ISSN\":[\"2810-9503\"],\"URL\":\"http:\\/\\/dx.doi.org\\/10.47852\\/bonviewjcce2202160\",\"abstract\":\"<jats:p>In this article, Ant Lion Optimizer (ALO) is used for supplier side optimal bidding strategy in a day-ahead Electricity Market (EM). 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