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Regular Paper | Open Access

Carbon Reduction Oriented Regional Integrated Energy System Optimization via Cloud-edge Cooperative Framework

Haochen Hua1Xingchen Wu1Xingying Chen1Hui Kong2( )Yizhong Sun3Qiaoyin Yang4Maria Cristina Tavares5Pathmanathan Naidoo6
College of Energy and Electrical Engineering, Hohai University, Nanjing 211100, China
School of Mechanical Engineering, Beijing Institute of Technology, Beijing 100081, China
School of Economics and Management, Beihang University, Beijing 100083, China
Department of Electrical Engineering, Tsinghua University, Beijing 100084, China
School of Electrical and Computer Engineering, University of Campinas, Campinas, São Paulo, Brazil
Department of Mechanical Engineering Science, University of Johannesburg, Johannesburg, ZA 2094, South Africa
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Abstract

In the regional integrated energy system (RIES) with multi-participation of user side, supply side, and integrated energy retailer (IER), the interaction and collaboration among various participants in a multi-energy trading mode are difficult to reveal under the conventional vertical integrated structure, which poses challenges to achieve an economical and low-carbon operation of RIES. This paper proposes a cloud-edge cooperative framework-based electricity-heat collaborative optimization strategy to address this issue. The multi-energy trading considering carbon trading process is embedded within the cloud-edge coordination framework, where IER is considered as the cloud side. In contrast, the user and supply sides are considered the edge sides, and the success-history-based adaptive differential evolution (SHADE) algorithm is adopted to obtain the interaction strategy. The simulation demonstrates that compared to the conventional vertical integrated structure, the RIES’s carbon emissions have decreased by 2.4% and transaction costs have decreased by 3.4%.

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CSEE Journal of Power and Energy Systems
Pages 969-981

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Cite this article:
Hua H, Wu X, Chen X, et al. Carbon Reduction Oriented Regional Integrated Energy System Optimization via Cloud-edge Cooperative Framework. CSEE Journal of Power and Energy Systems, 2026, 12(2): 969-981. https://doi.org/10.17775/CSEEJPES.2023.07270

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Received: 05 September 2023
Revised: 29 October 2023
Accepted: 08 November 2023
Published: 10 January 2025
© 2023 CSEE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).