Low Carbon Economic Dispatch of Power System Based on Multi-Region Distributed Multi-Gradient Whale Optimization Algorithm

Linfei Yin (), Yongzi Ye, Xiaoping Xiong, Jiajia Chai, Hanzhong Cui and Haoyuan Li
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Linfei Yin: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Yongzi Ye: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Xiaoping Xiong: School of Electrical Engineering, Guangxi University, Nanning 530004, China
Jiajia Chai: New Energy Power Generation Department, China Resources New Energy Investment Co., Ltd., Guangxi Branch, Nanning 530022, China
Hanzhong Cui: New Energy Equipment Management Department, China Resources New Energy Investment Co., Ltd., Guangxi Branch, Nanning 530022, China
Haoyuan Li: New Energy Equipment Management Department, China Resources New Energy Investment Co., Ltd., Guangxi Branch, Nanning 530022, China

Energies, 2025, vol. 18, issue 15, 1-29

Abstract: The rapid development of the modern power system puts forward high requirements for economic dispatch, and the defects of the traditional centralized economic dispatch method with low security and poor optimization effect have been difficult to adapt to the development of power system. Therefore, finding an economic dispatch method that reduces electricity generation costs and CO 2 emissions is important. This study establishes a multi-region distributed optimization model and combines the multi-region distributed optimization model with a multi-gradient optimization algorithm to propose a multi-region distributed multi-gradient whale optimization algorithm (MRDMGWOA). In this study, MRDMGWOA is simulated on the IEEE 39 system and 118 system, and its performance is compared with other heuristic algorithms. The results show that: (1) in the IEEE 39 system, MRDMGWOA reduces the power generation cost and CO 2 emission by 17% and 22%, respectively, and reduces the computation time by 16.14 s compared with the centralized optimization; (2) in the IEEE 118 system, the two metrics are further optimized, with a 20% and 17% reduction in the cost and emission, respectively, and an improvement in the computational efficiency by 45.46 s; (3) in the spacing, hypervolume, and Euclidian metrics evaluation, MRDMGWOA outperforms other algorithms; (4) compared with the existing DMOGWO and DMOMFO, the computation time of MRDMGWOA is reduced by 177.49 s and 124.15 s, respectively, and the scheduling scheme obtained by MRDMGWOA is more optimal than DMOGWO and DMOMFO.

Keywords: economic dispatch; multi-region distributed optimization; multi-gradient optimization algorithms; cost and CO 2 emissions (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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