Emission-Constrained Dispatch Optimization Using Adaptive Grouped Fish Migration Algorithm in Carbon-Taxed Power Systems

Kai-Hung Lu (), Xinyi Jiang and Sang-Jyh Lin
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Kai-Hung Lu: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Xinyi Jiang: School of Electronic and Electrical Engineering, Minnan University of Science and Technology, Quanzhou 362700, China
Sang-Jyh Lin: Department of Electronic Communication Engineering, National Kaohsiung University of Science and Technology, Kaohsiung 811213, Taiwan

Mathematics, 2025, vol. 13, issue 17, 1-24

Abstract: With increasing global pressure to decarbonize electricity systems, particularly in regions outside international carbon trading frameworks, it is essential to develop adaptive optimization tools that account for regulatory policies and system-level uncertainty. An emission-constrained power dispatch strategy based on an Adaptive Grouped Fish Migration Optimization (AGFMO) algorithm is proposed. The algorithm incorporates dynamic population grouping, a perturbation-assisted escape strategy from local optima, and a performance-feedback-driven position update rule. These enhancements improve the algorithm’s convergence reliability and global search capacity in complex constrained environments. The proposed method is implemented in Taiwan’s 345 kV transmission system, covering a decadal planning horizon (2023–2033) with scenarios involving varying load demands, wind power integration levels, and carbon tax schemes. Simulation results show that the AGFMO approach achieves greater reductions in total dispatch cost and CO 2 emissions compared with conventional swarm-based techniques, including PSO, GACO, and FMO. Embedding policy parameters directly into the optimization framework enables robustness in real-world grid settings and flexibility for future carbon taxation regimes. The model serves as decision-support tool for emission-sensitive operational planning in power markets with limited access to global carbon trading, contributing to the advanced modeling of control and optimization processes in low-carbon energy systems.

Keywords: carbon emission constraints; adaptive grouped fish migration optimization (AGFMO); renewable energy integration; power dispatch optimization; low-carbon energy transition (search for similar items in EconPapers)
JEL-codes: C (search for similar items in EconPapers)
Date: 2025
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