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Title: Improved multi-objective quantum genetic algorithm for low-carbon economic dispatch in power grids

Authors: Jinhui Ma; Xincun Shen; Li Zhang; Lu Chen

Addresses: Power Dispatching Control Center, State Grid Anhui Electric Power Co., Ltd, Hefei, Anhui, 230022, China ' Power Dispatching Control Center, State Grid Anhui Electric Power Co., Ltd, Hefei, Anhui, 230022, China ' Power Dispatching Control Center, State Grid Anhui Electric Power Co., Ltd, Hefei, Anhui, 230022, China ' Power Dispatching Control Center, State Grid Hefei Power Supply Company, Hefei Anhui, 230022, China

Abstract: To improve the low-carbon economic regulation effect of the power grid, reduce carbon emissions and operating costs, a low-carbon economic regulation method for the power grid based on improved multi-objective quantum genetic algorithm is proposed. Firstly, integrate photovoltaic, wind power, energy storage, and electricity market data, use local outlier factor algorithm to clean the data and fill in missing values. Secondly, establish a multi-objective optimisation model that includes power generation costs, carbon emissions, and demand response costs. Finally, an improved quantum genetic algorithm is proposed to enhance solution efficiency through quantum gate updates and intelligent population management, achieving low-carbon economic dispatch of the power grid. The results showed that under the control of the proposed method, the highest average carbon emissions were 0.39 tons of CO₂/MWh, the highest cost was only 135,000 yuan/MWh, and the average new energy consumption rate reached 91.87%.

Keywords: low carbon economic regulation; multi-objective quantum genetic algorithm; power generation cost; clean data.

DOI: 10.1504/IJBIDM.2026.154226

International Journal of Business Intelligence and Data Mining, 2026 Vol.28 No.9, pp.1 - 25

Received: 23 Oct 2025
Accepted: 02 Feb 2026
Published online: 17 Jun 2026 *