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Renewable energy targets and unintended storage cycling: Implications for energy modeling

To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artif...

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Detalles Bibliográficos
Autores principales: Kittel, Martin, Schill, Wolf-Peter
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Elsevier 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8991206/
https://www.ncbi.nlm.nih.gov/pubmed/35402861
http://dx.doi.org/10.1016/j.isci.2022.104002
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author Kittel, Martin
Schill, Wolf-Peter
author_facet Kittel, Martin
Schill, Wolf-Peter
author_sort Kittel, Martin
collection PubMed
description To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as “unintended storage cycling”, which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in energy models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling.
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spelling pubmed-89912062022-04-09 Renewable energy targets and unintended storage cycling: Implications for energy modeling Kittel, Martin Schill, Wolf-Peter iScience Article To decarbonize the economy, many governments have set targets for the use of renewable energy sources. These are often formulated as relative shares of electricity demand or supply. Implementing respective constraints in energy models is a surprisingly delicate issue. They may cause a modeling artifact of excessive electricity storage use. We introduce this phenomenon as “unintended storage cycling”, which can be detected in case of simultaneous storage charging and discharging. In this paper, we provide an analytical representation of different approaches for implementing minimum renewable share constraints in energy models, and show how these may lead to unintended storage cycling. Using a parsimonious optimization model, we quantify related distortions of optimal dispatch and investment decisions as well as market prices, and identify important drivers of the phenomenon. Finally, we provide recommendations on how to avoid the distorting effects of unintended storage cycling in energy modeling. Elsevier 2022-03-04 /pmc/articles/PMC8991206/ /pubmed/35402861 http://dx.doi.org/10.1016/j.isci.2022.104002 Text en © 2022 The Author(s) https://creativecommons.org/licenses/by/4.0/This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Kittel, Martin
Schill, Wolf-Peter
Renewable energy targets and unintended storage cycling: Implications for energy modeling
title Renewable energy targets and unintended storage cycling: Implications for energy modeling
title_full Renewable energy targets and unintended storage cycling: Implications for energy modeling
title_fullStr Renewable energy targets and unintended storage cycling: Implications for energy modeling
title_full_unstemmed Renewable energy targets and unintended storage cycling: Implications for energy modeling
title_short Renewable energy targets and unintended storage cycling: Implications for energy modeling
title_sort renewable energy targets and unintended storage cycling: implications for energy modeling
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8991206/
https://www.ncbi.nlm.nih.gov/pubmed/35402861
http://dx.doi.org/10.1016/j.isci.2022.104002
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