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Two-Tier Configuration Model for the Optimization of Enterprise Costs and User Satisfaction for Rural Microgrids

Author

Listed:
  • Yong Fang

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Minghao Li

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

  • Yunli Yue

    (Economic and Technical Research Institute, State Grid Jibei Electric Power Company Limited, Beijing 100038, China)

  • Zhonghua Liu

    (School of Economics and Management, Beijing University of Chemical Technology, Beijing 100029, China)

Abstract

The construction costs and operational challenges of rural microgrids have garnered widespread attention. This study focuses on grid-connected rural microgrids incorporating wind, solar, hydro, and storage systems, and proposes a two-tier optimization configuration model that considers both enterprise costs and user satisfaction. The upper-tier model aims to minimize enterprise costs, covering construction, operation and maintenance, as well as penalties for a curtailment of wind, solar, and hydro power. The lower-tier model evaluates power reliability and cost-effectiveness to maximize user satisfaction. Using the particle swarm optimization algorithm, this study analyzes a case in Yudaokou, Hebei Province, and proposes three optimization schemes: minimizing enterprise costs, maximizing user satisfaction, and a compromise between the two. The optimal scheme, which employs 17 photovoltaic panels, 12 wind turbines, and 15 energy storage units, achieved a user satisfaction score of 0.90. This two-tier planning model provides practical insights for the rational configuration of rural microgrids and reveals the nonlinear relationship between costs and user experience.

Suggested Citation

  • Yong Fang & Minghao Li & Yunli Yue & Zhonghua Liu, 2024. "Two-Tier Configuration Model for the Optimization of Enterprise Costs and User Satisfaction for Rural Microgrids," Mathematics, MDPI, vol. 12(20), pages 1-19, October.
    • Handle: RePEc:gam:jmathe:v:12:y:2024:i:20:p:3256-:d:1500867
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    References listed on IDEAS

    as
    1. Wang, Tonghe & Hua, Haochen & Shi, Tianying & Wang, Rui & Sun, Yizhong & Naidoo, Pathmanathan, 2024. "A bi-level dispatch optimization of multi-microgrid considering green electricity consumption willingness under renewable portfolio standard policy," Applied Energy, Elsevier, vol. 356(C).
    2. Abid, Md. Shadman & Apon, Hasan Jamil & Hossain, Salman & Ahmed, Ashik & Ahshan, Razzaqul & Lipu, M.S. Hossain, 2024. "A novel multi-objective optimization based multi-agent deep reinforcement learning approach for microgrid resources planning," Applied Energy, Elsevier, vol. 353(PA).
    3. Jiménez-Vargas, Iván & Rey, Juan M. & Osma-Pinto, German, 2023. "Sizing of hybrid microgrids considering life cycle assessment," Renewable Energy, Elsevier, vol. 202(C), pages 554-565.
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