Stochastic Optimization of Renewable Energy Integration in Nigeria's Power Grid: A Bayesian Approach

Stochastic Optimization of Renewable Energy Integration in Nigeria's Power Grid: A Bayesian Approach | 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 Stochastic Optimization of Renewable Energy Integration in Nigeria's Power Grid: A Bayesian Approach Tayo P. Ogundunmade This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9254501/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract In Nigeria, the electric power system struggles with consistently low generation, unreliability, and increased load demand. Despite small increments in installed generation capacity between 2015 and 2025, availability capacity stayed around 5000 MW, which is below 40% of installed capacity. In this study, the renewable penetration into the Nigerian electric power system has been determined using an integrated Bayesian-stochastic optimisation method. These distributions were optimized in the Bayesian inference stage, where their uncertainty was reduced, and the uncertainty and fluctuations of the electric system were captured using stochastic optimization. The optimisation shows that higher renewable penetration improved system cost, reliability, and sustainability. For 20% penetration, the cost is high (450 bn), and LOLP is also high (8%). With 30% renewable penetration, costs have been reduced to 420 bn, and LOLP is reduced to 4%. Additionally, emission is decreased by 15%, and it meets the Nigerian 2030 renewable policy target. Also, for 40%, the system cost is further decreased to 400 bn, and LOLP is further reduced to 2%. However, emissions decreased by 30%, and Nigeria overachieved its 2030 renewable policy target. Sensitivity analysis indicates that demand growth is the most significant parameter in influencing system cost and reliability compared to solar and wind variability. In conclusion, there appears to be a favorable tradeoff between feasibility and cost and reliability at 30% renewable penetration; however, maximizing system sustainability at 40% renewable penetration required substantial investment in energy storage and flexible generation capacity. Energy Engineering Bayesian Inference Stochastic Optimization Renewable Energy Integration Electricity Reliability Nigeria Power Sector Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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-9254501","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":613836706,"identity":"3be90809-941f-4b4b-b565-42df570f764e","order_by":0,"name":"Tayo P. 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