Milky Ali Gelchu , Jimmy Ehnberg , Dereje Shiferaw , Erik O. Ahlgren
{"title":"Exploring the advantages of a multi-year-adaptive approach on cost-optimal long-term mini-grid design under different demand evolution scenarios","authors":"Milky Ali Gelchu , Jimmy Ehnberg , Dereje Shiferaw , Erik O. Ahlgren","doi":"10.1016/j.segy.2025.100178","DOIUrl":null,"url":null,"abstract":"<div><div>Mini-grids are essential for rural electrification in sub-Saharan Africa, but due to uncertainty about future demand evolution in non-electrified communities, cost-optimal long-term sizing and design is particularly difficult. Standard, non-adaptive design approaches single-year and multi-year, are highly susceptible to demand evolution uncertainties. Despite potentially great advantages there is a lack of studies investigating adaptive design approaches. Thus, this study, using particle swarm optimization, explores the advantages of a multi-year-adaptive approach on cost-optimal long-term solar PV mini-grid component sizing under three demand evolution scenarios, considering the impacts of load flexibility, varying discount rates, and potential future mini-grid component cost reductions. The results show that the multi-year-adaptive approach helps to manage demand evolution challenges. It leads to significant cost-savings, up to three-quarters, in higher demand evolution scenarios, compared to multi-year and single-year approaches. These cost-savings increase with load flexibility (up to 4 % with 10 % flexibility), higher discount rates (up to 9.4 % with rates from 7 % to 20 %), and component cost reductions (up to 3.6 % per 1 % reduction). The study demonstrates how an adaptive approach can be utilized to optimize mini-grid component sizing and enhance cost efficiency.</div></div>","PeriodicalId":34738,"journal":{"name":"Smart Energy","volume":"18 ","pages":"Article 100178"},"PeriodicalIF":5.4000,"publicationDate":"2025-02-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Smart Energy","FirstCategoryId":"1085","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666955225000061","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENERGY & FUELS","Score":null,"Total":0}
引用次数: 0
Abstract
Mini-grids are essential for rural electrification in sub-Saharan Africa, but due to uncertainty about future demand evolution in non-electrified communities, cost-optimal long-term sizing and design is particularly difficult. Standard, non-adaptive design approaches single-year and multi-year, are highly susceptible to demand evolution uncertainties. Despite potentially great advantages there is a lack of studies investigating adaptive design approaches. Thus, this study, using particle swarm optimization, explores the advantages of a multi-year-adaptive approach on cost-optimal long-term solar PV mini-grid component sizing under three demand evolution scenarios, considering the impacts of load flexibility, varying discount rates, and potential future mini-grid component cost reductions. The results show that the multi-year-adaptive approach helps to manage demand evolution challenges. It leads to significant cost-savings, up to three-quarters, in higher demand evolution scenarios, compared to multi-year and single-year approaches. These cost-savings increase with load flexibility (up to 4 % with 10 % flexibility), higher discount rates (up to 9.4 % with rates from 7 % to 20 %), and component cost reductions (up to 3.6 % per 1 % reduction). The study demonstrates how an adaptive approach can be utilized to optimize mini-grid component sizing and enhance cost efficiency.