Methodologies for Selection of Optimal Sites for Renewable Energy Under a Diverse Set of Constraints and Objectives

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dc.contributor.authorSen, Arunabha
dc.contributor.authorSumnicht, Christopher
dc.contributor.authorChoudhuri, Sandipan
dc.contributor.authorAdeniye, Suli
dc.contributor.authorSen, Amit B.
dc.date.accessioned2023-12-26T18:39:27Z
dc.date.available2023-12-26T18:39:27Z
dc.date.issued2024-01-03
dc.identifier.doi10.24251/HICSS.2023.357
dc.identifier.isbn978-0-9981331-7-1
dc.identifier.other67ff72f4-0c4b-4bd7-a7e2-c0ec213d7325
dc.identifier.urihttps://hdl.handle.net/10125/106740
dc.language.isoeng
dc.relation.ispartofProceedings of the 57th Hawaii International Conference on System Sciences
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.subjectDistributed, Renewable, and Mobile Resources
dc.subjectmathematical programming
dc.subjectnetwork flow
dc.subjectoptimal site selection
dc.subjectrenewable energy
dc.subjectseasonal variation in supply and demand
dc.titleMethodologies for Selection of Optimal Sites for Renewable Energy Under a Diverse Set of Constraints and Objectives
dc.typeConference Paper
dc.type.dcmiText
dcterms.abstractIn this paper, we present methodologies for optimal site selection for renewable energy sites under a different set of constraints and objectives. We consider two different models for the site-selection problem - coarse-grained and fine-grained, and analyze them to find solutions. We consider multiple different ways to measure the benefits of setting up a site. We provide approximation algorithms with a guaranteed performance bound for two different benefit metrics with the coarse-grained model. For the fine-grained model, we provide a technique utilizing Integer Linear Program to find the optimal solution. We present the results of our extensive experimentation with synthetic data generated from sparsely available real data from solar farms in Arizona.
dcterms.extent10 pages
prism.startingpage2956

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