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- Publisher Website: 10.1109/TSG.2023.3310947
- Scopus: eid_2-s2.0-85169664223
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Article: Distributed Energy Management of Multi-Entity Integrated Electricity and Heat Systems: A Review of Architectures, Optimization Algorithms, and Prospects
Title | Distributed Energy Management of Multi-Entity Integrated Electricity and Heat Systems: A Review of Architectures, Optimization Algorithms, and Prospects |
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Authors | |
Keywords | Cogeneration Computer architecture distributed optimization Energy management Heat engines Heating systems Integrated electricity and heat system multi-entity coordination Optimization Resistance heating |
Issue Date | 1-Jan-2023 |
Publisher | Institute of Electrical and Electronics Engineers |
Citation | IEEE Transactions on Smart Grid, 2023 How to Cite? |
Abstract | Synergies among multiple energy sectors in an integrated electricity and heat system (IEHS) improve energy efficiency, economic operation and renewable energy utilization, thus contributing to a sustainable society. In reality, however, energy sectors are owned and operated by different entities, constituting a multi-entity integrated electricity and heat system (ME-IEHS). This calls for distributed energy management due to the curse of dimensionality and privacy concerns. Specifically, it is challenging to achieve socially optimal coordination using local information and limited information exchange, while the key to distributed energy management is to decompose the integrated energy management problem into smaller subproblems for interactive entities, respectively. This paper first explains the fundamentals of IEHSs including the definition of entities. According to the interactions among entities, this paper then establishes and compares the current energy management architectures. Distributed optimization algorithms supporting distributed energy management of the IEHS are systematically reviewed, while the extension to deal with uncertainties in a distributed fashion is also summarized. Finally, future research directions regarding the strategic behaviors yielded from multi-stakeholders and the distributed learning solution are envisioned. |
Persistent Identifier | http://hdl.handle.net/10722/338403 |
ISSN | 2021 Impact Factor: 10.275 2020 SCImago Journal Rankings: 3.571 |
DC Field | Value | Language |
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dc.contributor.author | Zheng, W | - |
dc.contributor.author | Lu, H | - |
dc.contributor.author | Zhang, M | - |
dc.contributor.author | Wu, Q | - |
dc.contributor.author | Hou, Y | - |
dc.contributor.author | Zhu, J | - |
dc.date.accessioned | 2024-03-11T10:28:35Z | - |
dc.date.available | 2024-03-11T10:28:35Z | - |
dc.date.issued | 2023-01-01 | - |
dc.identifier.citation | IEEE Transactions on Smart Grid, 2023 | - |
dc.identifier.issn | 1949-3053 | - |
dc.identifier.uri | http://hdl.handle.net/10722/338403 | - |
dc.description.abstract | <p>Synergies among multiple energy sectors in an integrated electricity and heat system (IEHS) improve energy efficiency, economic operation and renewable energy utilization, thus contributing to a sustainable society. In reality, however, energy sectors are owned and operated by different entities, constituting a multi-entity integrated electricity and heat system (ME-IEHS). This calls for distributed energy management due to the curse of dimensionality and privacy concerns. Specifically, it is challenging to achieve socially optimal coordination using local information and limited information exchange, while the key to distributed energy management is to decompose the integrated energy management problem into smaller subproblems for interactive entities, respectively. This paper first explains the fundamentals of IEHSs including the definition of entities. According to the interactions among entities, this paper then establishes and compares the current energy management architectures. Distributed optimization algorithms supporting distributed energy management of the IEHS are systematically reviewed, while the extension to deal with uncertainties in a distributed fashion is also summarized. Finally, future research directions regarding the strategic behaviors yielded from multi-stakeholders and the distributed learning solution are envisioned.</p> | - |
dc.language | eng | - |
dc.publisher | Institute of Electrical and Electronics Engineers | - |
dc.relation.ispartof | IEEE Transactions on Smart Grid | - |
dc.rights | This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License. | - |
dc.subject | Cogeneration | - |
dc.subject | Computer architecture | - |
dc.subject | distributed optimization | - |
dc.subject | Energy management | - |
dc.subject | Heat engines | - |
dc.subject | Heating systems | - |
dc.subject | Integrated electricity and heat system | - |
dc.subject | multi-entity coordination | - |
dc.subject | Optimization | - |
dc.subject | Resistance heating | - |
dc.title | Distributed Energy Management of Multi-Entity Integrated Electricity and Heat Systems: A Review of Architectures, Optimization Algorithms, and Prospects | - |
dc.type | Article | - |
dc.identifier.doi | 10.1109/TSG.2023.3310947 | - |
dc.identifier.scopus | eid_2-s2.0-85169664223 | - |
dc.identifier.eissn | 1949-3061 | - |
dc.identifier.issnl | 1949-3053 | - |