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A Two-Layer Cooperative Optimization Approach for Coordinated Photovoltaic-Energy Storage System Sizing and Factory Energy Dispatch Under Industrial Load Profiles

Author

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  • Xiaohui Wang

    (State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

  • Shijie Cui

    (State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China)

  • Qingwei Dong

    (State Key Laboratory of Robotics, Shenyang Institute of Automation, Chinese Academy of Sciences, Shenyang 110016, China
    Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China
    University of Chinese Academy of Sciences, Beijing 100049, China)

Abstract

Driven by policy incentives and economic pressures, energy-intensive industries are increasingly focusing on energy cost reductions amid the rapid adoption of renewable energy. However, the existing studies often isolate photovoltaic-energy storage system (PV-ESS) configurations from detailed load scheduling, limiting industrial park energy management. To address this, we propose a two-layer cooperative optimization approach (TLCOA). The upper layer employs a genetic algorithm (GA) to optimize the PV capacity and energy storage sizing through natural selection and crossover operations, while the lower layer utilizes mixed integer linear programming (MILP) to derive cost-minimized scheduling strategies under time-of-use tariffs. Multi-process parallel computing accelerates the fitness evaluations, resolving high-dimensional industrial data challenges. Multi-process parallel computing is introduced to accelerate fitness evaluations, effectively addressing the challenges posed by high-dimensional industrial data. Validated with real power market data, the TLCOA demonstrated rapid adaptation to load fluctuations while achieving a 23.68% improvement in computational efficiency, 1.73% reduction in investment costs, 7.55% decrease in power purchase costs, and 8.79% enhancement in renewable energy utilization compared to traditional methods. This integrated framework enables cost-effective PV-ESS deployment and adaptive energy management in industrial facilities, offering actionable insights for renewable integration and scalable energy optimization.

Suggested Citation

  • Xiaohui Wang & Shijie Cui & Qingwei Dong, 2025. "A Two-Layer Cooperative Optimization Approach for Coordinated Photovoltaic-Energy Storage System Sizing and Factory Energy Dispatch Under Industrial Load Profiles," Sustainability, MDPI, vol. 17(6), pages 1-24, March.
    • Handle: RePEc:gam:jsusta:v:17:y:2025:i:6:p:2713-:d:1615380
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    References listed on IDEAS

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    1. Turdybek, Balgynbek & Tostado-Véliz, Marcos & Mansouri, Seyed Amir & Rezaee Jordehi, Ahmad & Jurado, Francisco, 2024. "A local electricity market mechanism for flexibility provision in industrial parks involving Heterogenous flexible loads," Applied Energy, Elsevier, vol. 359(C).
    2. Babu, C.A. & Ashok, S., 2009. "Optimal utilization of renewable energy-based IPPs for industrial load management," Renewable Energy, Elsevier, vol. 34(11), pages 2455-2460.
    3. Min Xu & Wanwei Li & Zhihui Feng & Wangwang Bai & Lingling Jia & Zhanhong Wei, 2023. "Economic Dispatch Model of High Proportional New Energy Grid-Connected Consumption Considering Source Load Uncertainty," Energies, MDPI, vol. 16(4), pages 1-20, February.
    4. Guo, Jiacheng & Wu, Di & Wang, Yuanyuan & Wang, Liming & Guo, Hanyuan, 2023. "Co-optimization method research and comprehensive benefits analysis of regional integrated energy system," Applied Energy, Elsevier, vol. 340(C).
    5. Li, Yutong & Hou, Jian & Yan, Gangfeng, 2024. "Exploration-enhanced multi-agent reinforcement learning for distributed PV-ESS scheduling with incomplete data," Applied Energy, Elsevier, vol. 359(C).
    6. Li, Longxi & Cao, Xilin, 2022. "Comprehensive effectiveness assessment of energy storage incentive mechanisms for PV-ESS projects based on compound real options," Energy, Elsevier, vol. 239(PA).
    7. Bai, Zhang & Gu, Yucheng & Wang, Shuoshuo & Jiang, Tieliu & Kong, Debin & Li, Qi, 2023. "Applying the solar solid particles as heat carrier to enhance the solar-driven biomass gasification with dynamic operation power generation performance analysis," Applied Energy, Elsevier, vol. 351(C).
    8. Tao Wang & Cunhao Lin & Kuo Zheng & Wei Zhao & Xinglu Wang, 2023. "Research on Grid-Connected Control Strategy of Photovoltaic (PV) Energy Storage Based on Constant Power Operation," Energies, MDPI, vol. 16(24), pages 1-21, December.
    9. Silva, Jéssica Alice A. & López, Juan Camilo & Arias, Nataly Bañol & Rider, Marcos J. & da Silva, Luiz C.P., 2021. "An optimal stochastic energy management system for resilient microgrids," Applied Energy, Elsevier, vol. 300(C).
    10. Fan, Guo-Feng & Feng, Yi-Wen & Peng, Li-Ling & Huang, Hsin-Pou & Hong, Wei-Chiang, 2024. "Uncertainty analysis of photovoltaic power generation system and intelligent coupling prediction," Renewable Energy, Elsevier, vol. 234(C).
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