Reliability and Uncertainty-Aware Optimization of Capacitors and DSTATCOM Using Improved Exponential Distribution Optimizer

Reliability and Uncertainty-Aware Optimization of Capacitors and DSTATCOM Using Improved Exponential Distribution Optimizer | 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 Article Reliability and Uncertainty-Aware Optimization of Capacitors and DSTATCOM Using Improved Exponential Distribution Optimizer Abdulaziz Alanazi, Mohana Alanazi, Zulfiqar Ali Memon, Ahmed Bilal Awan, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4796149/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 16 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted 12 You are reading this latest preprint version Abstract In this paper, the simultaneous optimization of capacitors and DSTATCOM in the radial distribution system is performed for minimizing the cost of network active losses along with the cost of installation and investment of reactive power, considering the reliability of compensators and incorporating the network load uncertainty. The decision variables include the installation location and the capacity of compensators, which is defined by a novel meta-heuristic algorithm termed the improved exponential distribution optimizer (IEDO). The conventional exponential distribution optimizer (EDO) is inspired by exponential distribution theory, which uses the spiral motion strategy in the EDO to improve optimization performance and prevent it from getting trapped in local optima. Simulation scenarios are implemented in three cases: I) capacitor optimization, II) DSTATCOM optimization, and III) simultaneous optimization of capacitor and DSTATCOM in the network without (scenario I) and considering the compensator's reliability and also the load uncertainty using the unscented transformation (scenario II). The simulation results of IEDO showed that Case III has the best performance by achieving the lowest cost, the highest percentage of net savings, and the most favorable voltage profile in comparison to other scenarios. The superiority of the IEDO has also been confirmed in contrast to widely recognized optimization methodologies. In addition, the results of Scenario II are clear: the system cost has increased by 8.76%, 8.79%, and 8.72%, and the net savings have decreased to 6.48%, 6.62%, and 6.42%, compared to Scenario I for cases I–III, respectively. Physical sciences/Mathematics and computing/Computational science Physical sciences/Mathematics and computing/Computer science Physical sciences/Engineering/Electrical and electronic engineering Physical sciences/Engineering/Energy infrastructure Distribution Networks Stochastic Optimization of Capacitors and DSTATCOM Uncertainty Unscented Transformation Improved Exponential Distribution Optimizer Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 16 Apr, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 03 Sep, 2024 Reviews received at journal 03 Sep, 2024 Reviews received at journal 30 Aug, 2024 Reviews received at journal 28 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers agreed at journal 14 Aug, 2024 Reviewers agreed at journal 13 Aug, 2024 Reviewers invited by journal 12 Aug, 2024 Editor assigned by journal 12 Aug, 2024 Editor invited by journal 06 Aug, 2024 Submission checks completed at journal 06 Aug, 2024 First submitted to journal 24 Jul, 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. 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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-4796149","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":347068291,"identity":"63d163eb-102a-46d8-a316-aa7db09fece0","order_by":0,"name":"Abdulaziz Alanazi","email":"","orcid":"","institution":"Northern Border University","correspondingAuthor":false,"prefix":"","firstName":"Abdulaziz","middleName":"","lastName":"Alanazi","suffix":""},{"id":347068292,"identity":"581c766e-a000-40ef-bd21-cd4976f877d7","order_by":1,"name":"Mohana Alanazi","email":"","orcid":"","institution":"Jouf 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Stochastic Optimization of Capacitors and DSTATCOM, Uncertainty, Unscented Transformation, Improved Exponential Distribution Optimizer","lastPublishedDoi":"10.21203/rs.3.rs-4796149/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4796149/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eIn this paper, the simultaneous optimization of capacitors and DSTATCOM in the radial distribution system is performed for minimizing the cost of network active losses along with the cost of installation and investment of reactive power, considering the reliability of compensators and incorporating the network load uncertainty. The decision variables include the installation location and the capacity of compensators, which is defined by a novel meta-heuristic algorithm termed the improved exponential distribution optimizer (IEDO). The conventional exponential distribution optimizer (EDO) is inspired by exponential distribution theory, which uses the spiral motion strategy in the EDO to improve optimization performance and prevent it from getting trapped in local optima. Simulation scenarios are implemented in three cases: I) capacitor optimization, II) DSTATCOM optimization, and III) simultaneous optimization of capacitor and DSTATCOM in the network without (scenario I) and considering the compensator's reliability and also the load uncertainty using the unscented transformation (scenario II). The simulation results of IEDO showed that Case III has the best performance by achieving the lowest cost, the highest percentage of net savings, and the most favorable voltage profile in comparison to other scenarios. 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