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Mechanism that determines the economics of 100% renewable power systems

Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and...

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Detalles Bibliográficos
Autor principal: Hara, Takuya
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518472/
https://www.ncbi.nlm.nih.gov/pubmed/37752944
http://dx.doi.org/10.1016/j.isci.2023.107872
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author Hara, Takuya
author_facet Hara, Takuya
author_sort Hara, Takuya
collection PubMed
description Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems.
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spelling pubmed-105184722023-09-26 Mechanism that determines the economics of 100% renewable power systems Hara, Takuya iScience Article Many studies have evaluated the economic feasibility of 100% renewable power systems using the optimization approach, but the mechanisms determining the results remain unclear, making this issue still debatable. This study presents a mathematical formulation of the mechanism that only the demand and power generation profiles determine the optimal capacities of generation and storage and their trade-off relationship. Furthermore, this study demonstrates the comprehensive quantification of the corresponding relationships among the factor cost of technologies, their optimal capacities, and total system cost. Based on these findings, the study also shows that hybrid systems comprising multiple renewable energy sources and different types of storage, including long-duration energy storage, are critical to reducing the total system cost by using actual profile data for multiple years and regions in Japan. This suggests that large-scale deployment of current-level power-to-gas technologies, such as water electrolysis, can contribute to the economics of 100% renewable power systems. Elsevier 2023-09-09 /pmc/articles/PMC10518472/ /pubmed/37752944 http://dx.doi.org/10.1016/j.isci.2023.107872 Text en © 2023 The Author https://creativecommons.org/licenses/by-nc-nd/4.0/This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
spellingShingle Article
Hara, Takuya
Mechanism that determines the economics of 100% renewable power systems
title Mechanism that determines the economics of 100% renewable power systems
title_full Mechanism that determines the economics of 100% renewable power systems
title_fullStr Mechanism that determines the economics of 100% renewable power systems
title_full_unstemmed Mechanism that determines the economics of 100% renewable power systems
title_short Mechanism that determines the economics of 100% renewable power systems
title_sort mechanism that determines the economics of 100% renewable power systems
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10518472/
https://www.ncbi.nlm.nih.gov/pubmed/37752944
http://dx.doi.org/10.1016/j.isci.2023.107872
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