Article: Optimisation of operation algorithms based on artificial intelligence in power system control Journal: International Journal of Reasoning-based Intelligent Systems (IJRIS) 2025 Vol.17 No.9 pp.34 - 43 Abstract: The implementation of the dual carbon policy has introduced increased complexity in power system control and operation, owing to the interdependent nature of diverse generation units. To resolve the limitation in current research where power generation costs and environmental benefits cannot be optimised concurrently, this article first offers a multi-strategy adaptive particle swarm optimisation (PSO) approach (MAPSO) in light of a reward mechanism. Then, a dual-objective optimisation framework was established, simultaneously addressing power generation costs and gaseous pollutant emissions, while satisfying all necessary system constraints. Finally, by integrating external archiving technology, a multi-objective MAPSO (MOMAPSO) was proposed to compute dominated solutions for multiple objectives, thereby achieving overall operational optimisation. Simulation outcome indicate that the offered algorithm reduces fuel costs for power generation by at least $294.6863/h and reduces pollutant emissions by at least 0.0101 t/h, achieving both economic and environmental benefits. Inderscience Publishers - linking academia, business and industry through research

Title: Optimisation of operation algorithms based on artificial intelligence in power system control

Authors: Lei Yao

Addresses: School of Information Engineering, Nanning College of Technology, Guilin 541006, China

Abstract: The implementation of the dual carbon policy has introduced increased complexity in power system control and operation, owing to the interdependent nature of diverse generation units. To resolve the limitation in current research where power generation costs and environmental benefits cannot be optimised concurrently, this article first offers a multi-strategy adaptive particle swarm optimisation (PSO) approach (MAPSO) in light of a reward mechanism. Then, a dual-objective optimisation framework was established, simultaneously addressing power generation costs and gaseous pollutant emissions, while satisfying all necessary system constraints. Finally, by integrating external archiving technology, a multi-objective MAPSO (MOMAPSO) was proposed to compute dominated solutions for multiple objectives, thereby achieving overall operational optimisation. Simulation outcome indicate that the offered algorithm reduces fuel costs for power generation by at least $294.6863/h and reduces pollutant emissions by at least 0.0101 t/h, achieving both economic and environmental benefits.

Keywords: power system control; operation algorithm optimisation; particle swarm optimisation algorithm; reward mechanism; external archiving technology.

DOI: 10.1504/IJRIS.2025.147656

International Journal of Reasoning-based Intelligent Systems, 2025 Vol.17 No.9, pp.34 - 43

Received: 06 May 2025
Accepted: 29 May 2025
Published online: 24 Jul 2025 *

Title: Optimisation of operation algorithms based on artificial intelligence in power system control

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