Multi-Objective Optimization of Industrial Productivity and Renewable Energy Allocation Based on NSGA-II for Carbon Reduction and Cost Efficiency: Case Study of China

Lei Liu (), Hui Luo, Li Tian, Shuo Wang, Lishan Ma, Xin Gao, Chen Fang, Hao Sun, Xincheng Jin, Shan Jiang and Ying Zhang
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Lei Liu: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Hui Luo: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Li Tian: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Shuo Wang: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Lishan Ma: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Xin Gao: State Grid Beijing Electric Power Company, Beijing 100032, China
Chen Fang: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Hao Sun: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Xincheng Jin: State Grid Beijing Yizhuang Electric Power Supply Company, Beijing 100176, China
Shan Jiang: State Grid Beijing Yanqing Electric Power Supply Company, Beijing 102100, China
Ying Zhang: School of Control and Computer Engineering, North China Electric Power University, Beijing 102206, China

Energies, 2025, vol. 18, issue 20, 1-29

Abstract: This study develops a novel framework for optimizing regional power generation structures in support of China’s “dual carbon” goals. The framework introduces three main innovations. First, it formulates a comprehensive optimization paradigm that simultaneously balances industrial output, carbon emissions, and electricity costs, thereby directly addressing the trade-offs in regional energy planning. Second, it enhances data reliability and representativeness through systematic augmentation strategies, improving the robustness of optimization under data scarcity. Third, it incorporates an intelligent multi-objective search mechanism that yields a practical, three-dimensional decision space for policymakers. Beyond its methodological contributions, this research provides significant scientific value by offering a replicable framework for accelerating low-carbon energy transitions in rapidly industrializing economies. The proposed approach directly supports global sustainability targets and aligns with the United Nations Sustainable Development Goals (SDG 7, SDG 9, and SDG 13) by promoting clean energy adoption, fostering industrial innovation, and contributing to climate action. Together, these contributions provide a scalable and actionable pathway for adjusting regional power structures, aligning with national policies and accelerating the achievement of the carbon peak target by 2030.

Keywords: multi-objective optimization; carbon emissions; non-dominated sorting genetic algorithm II (NSGA-II); conditional generative adversarial network (CGAN); convolutional neural network (CNN) (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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