Low-Carbon Economic Dispatch of Integrated Energy Systems in Coal Mines Based on Multi-Strategy Multi-Objective Particle Swarm Optimization

Introduction: In the context of dual carbon goals, coal mining areas face challenges, including low energy utilization efficiency, high carbon emissions, and limited optimization in scheduling solutions. There is an urgent need to establish integrated energy systems for coal mines and develop suitable solution algorithms. Methods: First, a Coal Mine Integrated Energy Systems (CMIES) model was developed based on traditional Integrated Energy Systems (IES), incorporating high proportions of Coal Mine Associated Energy (CMAE) and Renewable Energy (RE) utilization. Second, a Multi-Strategy Multi-Objective Particle Swarm Optimization (MSMOPSO) algorithm based on dynamic population partitioning was proposed, building upon the traditional Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Employing dynamic population partitioning, a triple-competition mechanism, and a differentiated particle-update strategy effectively balances global exploration and local exploitation. Finally, the improved algorithm was applied to solve the constructed system model, yielding an optimal scheduling scheme. Result: Compared to IES, the system models incorporating CMAE and RE utilization reduced total costs by 14.3% and 10.4%, while achieving 100% energy utilization. Furthermore, compared to traditional NSGA-II, MOPSO, and MODE algorithms, the ratio of non-dominant particles improved by 111.3%, 80.7%, and 44.2%, respectively. Discussion: Integrating CMAE and RE into CMIES not only effectively advances the low-carbon transformation of the coal industry but also reinforces its sustainable development. Concurrently, the enhanced MSMOPSO algorithm provides an effective solution for addressing the challenges posed by multi-objective CMIES scheduling problems. Conclusion: The study offers innovative solutions for modeling and simulating CMIES. The solutions expand the research approaches and application prospects in other fields.

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Journal
Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering)
Published
2026-04-06
DOI
https://doi.org/10.2174/0123520965450907260130080125
Primary Topic
Integrated Energy Systems Optimization
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article
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article

Low-Carbon Economic Dispatch of Integrated Energy Systems in Coal Mines Based on Multi-Strategy Multi-Objective Particle Swarm Optimization

Yingbo Xie, Chaohua Yang, Fei Ge, Xuhui Bu
Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering)
Integrated Energy Systems Optimization
article

Low-Carbon Economic Dispatch of Integrated Energy Systems in Coal Mines Based on Multi-Strategy Multi-Objective Particle Swarm Optimization

Yingbo Xie, Chaohua Yang, Fei Ge, Xuhui Bu
article en

Abstract

Introduction: In the context of dual carbon goals, coal mining areas face challenges, including low energy utilization efficiency, high carbon emissions, and limited optimization in scheduling solutions. There is an urgent need to establish integrated energy systems for coal mines and develop suitable solution algorithms. Methods: First, a Coal Mine Integrated Energy Systems (CMIES) model was developed based on traditional Integrated Energy Systems (IES), incorporating high proportions of Coal Mine Associated Energy (CMAE) and Renewable Energy (RE) utilization. Second, a Multi-Strategy Multi-Objective Particle Swarm Optimization (MSMOPSO) algorithm based on dynamic population partitioning was proposed, building upon the traditional Multi-Objective Particle Swarm Optimization (MOPSO) algorithm. Employing dynamic population partitioning, a triple-competition mechanism, and a differentiated particle-update strategy effectively balances global exploration and local exploitation. Finally, the improved algorithm was applied to solve the constructed system model, yielding an optimal scheduling scheme. Result: Compared to IES, the system models incorporating CMAE and RE utilization reduced total costs by 14.3% and 10.4%, while achieving 100% energy utilization. Furthermore, compared to traditional NSGA-II, MOPSO, and MODE algorithms, the ratio of non-dominant particles improved by 111.3%, 80.7%, and 44.2%, respectively. Discussion: Integrating CMAE and RE into CMIES not only effectively advances the low-carbon transformation of the coal industry but also reinforces its sustainable development. Concurrently, the enhanced MSMOPSO algorithm provides an effective solution for addressing the challenges posed by multi-objective CMIES scheduling problems. Conclusion: The study offers innovative solutions for modeling and simulating CMIES. The solutions expand the research approaches and application prospects in other fields.

Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering)Vol. 19
Henan Polytechnic University (CN)
Industry, innovation and infrastructure
Openalex Percentile: Top 8%
Integrated Energy Systems Optimization
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