Work

Research on multi-objective scheduling optimization of power system based on hybrid model of genetic algorithm and particle swarm optimization

Year: 2026

Type: article

Abstract: This paper presents a hybrid model combining Genetic Algorithm and Particle Swarm Optimization for multi-objective scheduling optimization in power systems. The model aims to minimize power generation costs, reduce emissions, and enhance system stability. A real-time scheduling approach is incorporated, enabling dynamic adjustments to fluctuating loads and optimizing the power system through rolling-horizon rescheduling. Experimental results based on actual data from a regional power system show that the hybrid GA-PSO model reduces generation costs by 12.5%, emissions by 9.8%, and improves system stability by 15.3%. These results demonstrate the model’s effectiveness in solving multi-objective scheduling problems and its potential application in sustainable power system management.

Source: Discover Computing

DOI: 10.1007/s10791-026-09929-7

Author: Xia Zhao

Institution: Zhengzhou Technical College

Language: English

Topic: Electric Power System Optimization

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Affordable and clean energy, Industry, innovation and infrastructure

Open Access status: gold

Estimated APC paid: $1,520