Low-Carbon Control of Integrated Energy by Combining Cuckoo Search Algorithm and Particle Swarm Optimization Algorithm

Dandan Wang, Jian Guan, Hongyan Liu, Hanwen Zhang, Qi Wang, Lijian Zhang and Jingzheng Dong ()
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Dandan Wang: Department of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Jian Guan: Department of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Hongyan Liu: Department of Marine Convergence Design Engineering, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Hanwen Zhang: Department of Marine Convergence Design Engineering, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Qi Wang: Department of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Lijian Zhang: Department of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea
Jingzheng Dong: Department of Global and Area Studies, Pukyong National University, 45, Yongso-ro, Nam-gu, Busan 48513, Republic of Korea

Sustainability, 2025, vol. 17, issue 7, 1-21

Abstract: With the increasing severity of global climate change, low-carbon development has become a key issue in the energy industry. As an effective way to optimize energy utilization and reduce carbon emissions, integrated energy system is receiving increasing attention. However, existing low-carbon control methods still face many challenges in improving system efficiency and reducing carbon emissions, and the ability of multi-energy cooperative scheduling and optimal control is insufficient. Therefore, a hybrid algorithm combining the particle swarm optimization and cuckoo search algorithms is designed to adjust the integrated energy low-carbon control capability. The proposed algorithm required fewer iterations than the genetic cuckoo algorithm, which only went through 43 iterations. The convergence speed was improved by 34.8% compared with a single cuckoo algorithm. Among the four scenarios, scenario 4 and scenario 3 had the highest utilization rates of 99.75%, while scenario 1 had the lowest utilization rate of 61.96%. This indicates that the integrated energy system controlled by the particle swarm optimization cuckoo algorithm, while considering carbon capture and storage as well as power-to-gas conversion, can effectively utilize solar energy resources for power generation and achieve energy-saving and emission reduction effects. In summary, this method can help the integrated energy system adapt to various optimization strategies, which promotes the development of low-carbon control technologies in the energy industry.

Keywords: cuckoo algorithm; PSO algorithm; low-carbon control; integrated energy; CCS; P2G (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2025
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