Multi-Objective Optimization for the Low-Carbon Operation of Integrated Energy Systems Based on an Improved Genetic Algorithm

Yao Duan, Chong Gao, Zhiheng Xu, Songyan Ren () and Donghong Wu
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Yao Duan: Grid Planning & Research Center, Guangdong Power Grid Co., Ltd., CSG, Guangzhou 510000, China
Chong Gao: Grid Planning & Research Center, Guangdong Power Grid Co., Ltd., CSG, Guangzhou 510000, China
Zhiheng Xu: Grid Planning & Research Center, Guangdong Power Grid Co., Ltd., CSG, Guangzhou 510000, China
Songyan Ren: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 100045, China
Donghong Wu: Guangzhou Institute of Energy Conversion, Chinese Academy of Sciences, Guangzhou 100045, China

Energies, 2025, vol. 18, issue 9, 1-20

Abstract: As global climate change and energy crises intensify, the pursuit of low-carbon integrated energy systems (IESs) has become increasingly important. This paper proposes an improved genetic algorithm (IGA) designed to optimize the multi-objective low-carbon operations of IESs, aiming to minimize both operating costs and carbon emissions. The IGA incorporates circular crossover and polynomial mutation techniques, which not only preserve advantageous traits from the parent population but also enhance genetic diversity, enabling comprehensive exploration of potential solutions. Additionally, the algorithm selects parent populations based on individual fitness and dominance, retaining successful chromosomes and eliminating those that violate constraints. This process ensures that subsequent generations inherit superior genetic traits while minimizing constraint violations, thereby enhancing the feasibility of the solutions. To evaluate the effectiveness of the proposed algorithm, we tested it on three different IES scenarios. The results demonstrate that the IGA successfully reduces equality constraint violations to below 0.3 kW, representing less than 0.2% deviation from the IES’s power demand in each time slot. We compared its performance against a multi-objective genetic algorithm, a multi-objective particle swarm algorithm, and a single-objective genetic algorithm. Compared to conventional genetic algorithms, the IGA achieved maximum 5% improvement in both operational cost reduction and carbon emission minimization objectives compared to the unimproved single-objective genetic algorithm, demonstrating its superior performance in multi-objective optimization for low-carbon IESs. These outcomes underscore the algorithm’s reliability and practical applicability.

Keywords: improved genetic algorithm; integrated energy system; low-carbon optimization; multi-objective optimization (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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