Energy Trading Strategies for Integrated Energy Systems Considering Uncertainty

Jin Gao, Zhenguo Shao, Feixiong Chen () and Mohammadreza Lak
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Jin Gao: Key Laboratory of Energy Digitalization, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Zhenguo Shao: Key Laboratory of Energy Digitalization, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Feixiong Chen: Key Laboratory of Energy Digitalization, College of Electrical Engineering and Automation, Fuzhou University, Fuzhou 350108, China
Mohammadreza Lak: Department of Electrical Engineering, Yuan Ze University, Taoyuan 32003, Taiwan

Energies, 2025, vol. 18, issue 4, 1-21

Abstract: To improve the stable operation and promote the energy sharing of the integrated energy system (IES), a comprehensive energy trading strategy considering uncertainty is proposed. Firstly, an IES model incorporating power-to-gas (P2G) and a carbon capture system (CCS) is established to reduce carbon emissions. Secondly, this model is integrated into a four-level robust optimization to address the fluctuation of renewable energy sources in IES operations. This not only considers probability distribution scenarios of renewable energy and the uncertainty of its output, but also effectively reduces the model’s conservatism by constructing a multi-interval uncertainty set. On this basis, a Nash–Harsanyi bargaining method is used to solve the issue of benefit allocation among multiple IESs. Finally, the energy trading model is solved using a distributed algorithm that ensures an equitable distribution of benefits while protecting the privacy of each IES. The simulation results validate the effectiveness of the proposed strategy.

Keywords: integrated energy system (IES); four-level robust optimization; Nash–Harsanyi bargaining; distributed algorithm (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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