Probabilistic Planning of Renewable Energy Source with Different Load Models

Probabilistic Planning of Renewable Energy Source with Different Load Models | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article Probabilistic Planning of Renewable Energy Source with Different Load Models ayat ali saleh This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4451256/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 3 You are reading this latest preprint version Abstract this paper presents an optimization algorithm, named Non dominated Sorting Genetic Algorithm III (NSGA-III) method to identify the optimal allocation of hybrid distributed energy resources (DERs) including photovoltaic system (PV), micro-turbine (MT), and fuel cell (FC) in distribution system considering four different load models such as industrial, residential, commercial and constant load model. With increasing the integration of renewable energy resources, the uncertainties resulting from these sources posed a serious challenge to the operation of the network. To be closer to the reality, the network performance is analyzed considering the influence of the uncertainties produced from RES. Point Estimate Method (PEM) is used to model the uncertainties generated from renewable energy resources. The main aim of this paper is to maximize technical and economic benefits of DERs by minimizing various objective functions such as the total power loss, cost, and voltage deviation subject to different power system constraints. Multi-objective planning framework is appraised using two standards IEEE networks with various scenarios. Comparative analyses are conducted on standard distribution systems under different load model and mix of different types of DERs. Level diagram is implemented to analysis and compares the influence of different combinations of load models on the system performance. The obtained results show that, the system performance is greatly influenced by uncertainties accompanied with DER and power system itself. The suggested multi-objective planning frameworks shows high accuracy compared to other techniques applied in previous researches. Non dominated sorting genetic algorithm III Load models Distributed generation Vower loss reduction Voltage deviation cost Planning under uncertainty Level diagram. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editor assigned by journal 10 Jun, 2024 Submission checks completed at journal 27 May, 2024 First submitted to journal 20 May, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4451256","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":307299636,"identity":"484eeecb-ffc9-42ca-821e-9e5551a2ea6e","order_by":0,"name":"ayat ali saleh","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA6UlEQVRIiWNgGAWjYDADfvnHBxgYG0jQISHZkJZAohaDAzkGxGnhl26/wPCzza5OsuHMN4mfO2zkGNgPH92AT4vknDMFjL1tyRL8jL3bJHvPpBkz8KSl3cCnxeBGTgIDbxuzhGQz7zYJ3rbDiQ0SPGZ4tdgDtTD+bauXMDjG80zyLzFaDCTSDzADDZcwOMPDJk2ULRI3chiYZc4dl5w5g83YWrYtzZiNkF/4Z6Q/YHxTVs3PL8H88ObbNhs5fvbDx/BqYWDgMf/ByAZmsUiASDb8ykGA/QEDwx8wi/kDYdWjYBSMglEwEgEAA5lIoDYEYP0AAAAASUVORK5CYII=","orcid":"","institution":"Aswan University","correspondingAuthor":true,"prefix":"","firstName":"ayat","middleName":"ali","lastName":"saleh","suffix":""}],"badges":[],"createdAt":"2024-05-20 23:23:21","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4451256/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4451256/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":57937619,"identity":"77fd30e3-6270-4439-a7bd-8e78402e743e","added_by":"auto","created_at":"2024-06-07 17:42:49","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1480918,"visible":true,"origin":"","legend":"","description":"","filename":"ProbabilisticPlanningofRenewableEnergySourcewithDifferentLoadModels.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4451256/v1_covered_662db159-6358-41ae-a818-bc8c5cfeaa37.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Probabilistic Planning of Renewable Energy Source with Different Load Models","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"energy-sustainability-and-society","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"esso","sideBox":"Learn more about [Energy, Sustainability and Society](https://energsustainsoc.biomedcentral.com/)","snPcode":"13705","submissionUrl":"https://submission.nature.com/new-submission/13705/3","title":"Energy, Sustainability and Society","twitterHandle":"@OpenEnviron","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"em","reportingPortfolio":"BMC/SO AJ","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Non dominated sorting genetic algorithm III, Load models, Distributed generation, Vower loss reduction, Voltage deviation, cost, Planning under uncertainty, Level diagram.","lastPublishedDoi":"10.21203/rs.3.rs-4451256/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4451256/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003ethis paper presents an optimization algorithm, named Non dominated Sorting Genetic Algorithm III (NSGA-III) method to identify the optimal allocation of hybrid distributed energy resources (DERs) including photovoltaic system (PV), micro-turbine (MT), and fuel cell (FC) in distribution system considering four different load models such as industrial, residential, commercial and constant load model. \u0026nbsp;With increasing the integration of renewable energy resources, the uncertainties resulting from these sources posed a serious challenge to the operation of the network. 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