Multi-Objective Distributionally Robust Optimization of Power-Gas Energy Integrated Systems Considering Environmental-Economic Dispatch

Since gas turbines have fast start-stop and load regulation characteristics, they can effectively suppress the instability caused by wind power and solar power units, so the collaborative operation of integrated power-gas systems(IPGS) is increasingly important. In this paper, a multi-objective envi...

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Vydané v:Journal of electrical engineering & technology Ročník 20; číslo 5; s. 3009 - 3027
Hlavní autori: Liu, Yubing, Gao, Guangkuo, Zhao, Wenhui
Médium: Journal Article
Jazyk:English
Vydavateľské údaje: Singapore Springer Nature Singapore 01.07.2025
Springer Nature B.V
대한전기학회
Predmet:
Electrical Engineering
Electrical Machines and Networks
Electronics and Microelectronics
Engineering
Instrumentation
Original Article
Power Electronics
전기공학
Solar power
Carbon emission
Multi-objective grey wolf optimization algorithm
Distribution robust optimization
Wind power
Integrated power-gas systems
ISSN:1975-0102, 2093-7423
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Shrnutí:Since gas turbines have fast start-stop and load regulation characteristics, they can effectively suppress the instability caused by wind power and solar power units, so the collaborative operation of integrated power-gas systems(IPGS) is increasingly important. In this paper, a multi-objective environmental-economic dispatch optimization model for the IPGS with wind power and solar power is considered in a random environment, and the coupling constraint of the IPGS is established. Aiming at the random problem in the model, considering the statistical significance, the random variables are limited to the Wasserstein distribution set, and the distribution robust optimization is introduced. Since multi-objective optimization problems are difficult to solve exactly, this paper improves the multi-objective gray wolf optimization algorithm(IMOGWO) and verifies the effectiveness of the improved algorithm by standard functions. Finally, through numerical experiments for comparison, the results show that: (1) The IMOGWO algorithm is more suitable for this type of problem as it can solve the multi-objective distribution robust model more quickly than the existing algorithms; (2) Distributed robust multi-objective modeling effectively reduces the environmental-economic dispatch cost of the IPGS, improves the dispatch economy, reduces CO 2 emissions, and provides a powerful decision-making tool for policy-makers.
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ISSN:1975-0102
2093-7423
DOI:10.1007/s42835-025-02215-4