Reliability Optimization Method for Gas–Electric Integrated Energy Systems Considering Cyber–Physical Interactions

Buxiang Zhou, Yating Cai, Tianlei Zang (), Jiale Wu, Xuan Li and Shen Dong
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Buxiang Zhou: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Yating Cai: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Tianlei Zang: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Jiale Wu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xuan Li: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Shen Dong: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2023, vol. 16, issue 13, 1-19

Abstract: With the development in the field of energy and the growing demand for sustainable energy, gas–electric integrated energy systems are attracting attention as an emerging energy supply method. At the same time, with the deep application of information technology, the cyber–physical interactions of gas–electric integrated energy systems are increasingly enhanced. To this end, first, the reliability assessment indices of a gas–electric integrated energy system, which comprehensively considers the interactions between cyber–physical and different energy sources, are established in this paper to quantitatively assess the reliability level of the system under different fault and failure conditions. Second, to solve the reliability optimization problem, a comprehensive reliability enhancement optimization model is constructed in this paper, which targets the sum of the total penalties of the failure rate and average repair time modification. The impact of the cyber systems on the gas–electric integrated energy systems is transformed into a modification of the failure rate and the average repair time, and the model is solved by an adaptive Gaussian particle swarm optimization algorithm. Finally, the applicability and superiority of the adaptive Gaussian particle swarm optimization algorithm to the reliability optimization of the gas–electricity integrated energy system are verified by conducting simulation tests on the gas–electricity integrated energy system coupled with an 8-node distribution system and the 11-node natural gas system in Belgium. Furthermore, the effects of cyber systems and cyber-attacks on system reliability optimization are also analyzed to verify the effectiveness of the proposed method and the rationality of the newly defined reliability indices.

Keywords: gas–electric integrated energy system; cyber–physical interaction; reliability indices; reliability optimization; adaptive Gaussian particle swarm optimization 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: 2023
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