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Energy hub gas: a multi-domain system modelling and co-simulation approach

Published: 06 October 2021 Publication History

Abstract

Coping with the complexity of future energy grids and the rising challenges of the energy transition to more renewable energy sources (RES), an Energy Hub Gas (EHG) concept appears to be a promising approach. This concept combines various technical components to a sector-coupling system network to support the electricity grid with ancillary and balancing services to cope with the fluctuating generation by RES and to provide (renewable) energy carriers. Additionally, the EHG serves as regional gateway and as a converter for large, centralized RES-feed-in and aggregation/distribution hub of local RES-feed-in. For combining several separate models from different domains to an EHG system model, a co-simulation approach is used with high regard on flexibility concerning the modelling aspects as well as high modularity to easily adapt the concept to further use cases. As main results presented in the paper, the coherence of the extended EHG system model and its usability for implementation in co-simulation can be shown in first simulations.

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Cited By

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  • (2024)Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub SchedulingEnergy Informatics10.1007/978-3-031-74741-0_14(205-223)Online publication date: 23-Oct-2024
  • (2023)Energy Hub Gas: A Modular Setup for the Evaluation of Local Flexibility and Renewable Energy Carriers ProvisionEnergies10.3390/en1606272016:6(2720)Online publication date: 14-Mar-2023
  • (2023)Dynamic Mapping for Evolutionary Algorithm Based Optimization of Energy Hub Gas Scheduling2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)10.1109/SEGE59172.2023.10274571(206-211)Online publication date: 13-Aug-2023
  • Show More Cited By

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        cover image ACM Conferences
        MSCPES '21: Proceedings of the 9th Workshop on Modeling and Simulation of Cyber-Physical Energy Systems
        May 2021
        83 pages
        ISBN:9781450386081
        DOI:10.1145/3470481
        This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives International 4.0 License.

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        • IEEE Signal Processing Society
        • IEEE CS

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        Association for Computing Machinery

        New York, NY, United States

        Publication History

        Published: 06 October 2021

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        Author Tags

        1. co-simulation
        2. cyber-physical energy systems
        3. hybrid modeling and simulation
        4. multi-domain systems

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        • Research-article

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        • German Federal Ministry of Education and Research (BMBF)

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        Cited By

        View all
        • (2024)Dynamic Phenotype Mapping in Evolutionary Algorithms for Energy Hub SchedulingEnergy Informatics10.1007/978-3-031-74741-0_14(205-223)Online publication date: 23-Oct-2024
        • (2023)Energy Hub Gas: A Modular Setup for the Evaluation of Local Flexibility and Renewable Energy Carriers ProvisionEnergies10.3390/en1606272016:6(2720)Online publication date: 14-Mar-2023
        • (2023)Dynamic Mapping for Evolutionary Algorithm Based Optimization of Energy Hub Gas Scheduling2023 IEEE 11th International Conference on Smart Energy Grid Engineering (SEGE)10.1109/SEGE59172.2023.10274571(206-211)Online publication date: 13-Aug-2023
        • (2023)The Kopernikus ENSURE Co-Demonstration PlatformIEEE Open Journal of Power Electronics10.1109/OJPEL.2023.33325154(987-1002)Online publication date: 2023
        • (2022)An adapter-based architecture for evaluating candidate solutions in energy system schedulingEnergy Informatics10.1186/s42162-022-00246-z5:S4Online publication date: 21-Dec-2022
        • (2022)Energy Hub Gas: A Modular Setup for the Evaluation of Local Flexibility and Renewable Energy Carriers Provision2022 IEEE 10th International Conference on Smart Energy Grid Engineering (SEGE)10.1109/SEGE55279.2022.9889751(33-41)Online publication date: 10-Aug-2022
        • (2022)Dynamic Optimization of Energy Hubs with Evolutionary Algorithms Using Adaptive Time Segments and Varying ResolutionIntelligent Data Engineering and Automated Learning – IDEAL 202210.1007/978-3-031-21753-1_50(513-524)Online publication date: 24-Nov-2022

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