期刊
SUSTAINABLE ENERGY GRIDS & NETWORKS
卷 32, 期 -, 页码 -出版社
ELSEVIER
DOI: 10.1016/j.segan.2022.100913
关键词
Hybrid energy networks; Multi -energy systems; Smart energy systems; Sector coupling; Modeling; Simulation
资金
- German Federal Ministry for Economic Affairs and Climate Action (BMWK) [020E100331618]
- Danish Energy Technology Development and Demonstration Programme (EUDP) [64019-0123]
- French Agency on Ecological Transition (ADEME) [876727]
- Austrian Research Funding Association (FFG) [1805C0001]
This article introduces innovative tools for modeling and simulating hybrid energy networks (HENs) and categorizes them to provide a guideline for early adopters in selecting the most suitable tools for their specific application needs.
Sector coupling is expected to play a key role in the decarbonization of the energy system by enabling the integration of decentralized renewable energy sources and unlocking hitherto unused synergies between generation, storage and consumption. Within this context, a transition towards hybrid energy networks (HENs), which couple power, heating/cooling and gas grids, is a necessary requirement to implement sector coupling on a large scale. However, this transition poses practical challenges, because the traditional domain-specific approaches struggle to cover all aspects of HENs. Methods and tools for conceptualization, system planning and design as well as system operation support exist for all involved domains, but their adaption or extension beyond the domain they were originally intended for is still a matter of research and development. Therefore, this work presents innovative tools for modeling and simulating HENs. A categorization of these tools is performed based on a clustering of their most relevant features. It is shown that this categorization has a strong correlation with the results of an independently carried out expert review of potential application areas. This good agreement is a strong indicator that the proposed classification categories can successfully capture and characterize the most important features of tools for HENs. Furthermore, it allows to provide a guideline for early adopters to understand which tools and methods best fit the requirements of their specific applications. (C) 2022 The Authors. Published by Elsevier Ltd.
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