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Quantifying the carbon footprint of energy storage applications with an energy system simulation framework — Energy System Network

  • Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation,Energy storage is a crucial flexibility measure to temporally decouple power generation from power demand and is touted as the missing link in realizing a decarbonized energy system based on renewable energy. Energy storage capacity buildup at all levels of the global energy system is expected to accelerate the decarbonization process. To this end, a coherent mathematical framework to ascertain the carbon footprint of localized energy systems with energy storage is indispensable. This article presents an open-source energy system simulation program — Energy System Network (ESN). A variety of energy system configurations can be simulated with the Python program, which incorporates key energy system components such as generation, grid, storage, and loads. ESN features an integrated bottom-up approach that combines energy system modeling with streamlined life cycle assessment techniques to quantify the carbon footprint of all components in a localized energy system. The lifecycle phases of each component, including production, operation, and end-of-life treatment, can be considered. Carbon footprint values are obtained for two demonstrative case studies with lithium-ion battery applications: energy arbitrage and home energy systems. The metric Levelized Emissions of Energy Supply (LEES) has been used to evaluate the carbon footprint of each application. An unconventional energy arbitrage strategy designed to exploit the grid carbon intensity spreads instead of the energy price spreads manages to achieve a LEES value about 17% lower than the conventional variant. The influence of rooftop solar generation, battery energy storage system, and the energy management strategy on the LEES values for a home energy system is explored. A maximum LEES reduction of over 37% vis-á-vis the base scenario was observed with optimal energy management for the solar generation and the battery system. The open-source availability of ESN can contribute to transparency, comparability, and reproducibility in carbon footprint assessments of localized energy systems with energy storage.show moreshow less

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Metadaten
Author:Anupam Parlikar, Benedikt Tepe, Marc Möller, Holger HesseORCiDGND, Andreas Jossen
DOI:https://doi.org/10.1016/j.enconman.2024.118208
Identifier:0196-8904 OPAC HS OPAC extern
Parent Title (English):Energy Conversion and Management
Publisher:Elsevier
Place of publication:Amsterdam
Document Type:Article
Language:English
Date of Publication (online):2024/02/20
Year of first Publication:2024
Volume:304
Article Number:118208
Number of pages:19 Seiten
Institutes:Fakultät Maschinenbau
IEES - Institut für Elektrische Energiesysteme
Dewey Decimal Classification:6 Technik, Medizin, angewandte Wissenschaften
Open Access:open_access
Research focus:FSP1: Energie
Publication Lists:Hesse, Holger
Publication reviewed:begutachtet
Release Date:2024/02/26
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