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A Day-Ahead Optimal Battery Scheduling Considering the Grid Stability of Distribution Feeders

Author

Listed:
  • Umme Mumtahina

    (Central Queensland University, Rockhampton, QLD 4701, Australia)

  • Sanath Alahakoon

    (Central Queensland University, Gladstone, QLD 4680, Australia)

  • Peter Wolfs

    (Central Queensland University, Rockhampton, QLD 4701, Australia)

Abstract

This study presents a comprehensive framework for optimizing energy management systems by integrating advanced methodologies for weather forecasting, energy cost analysis, and grid stability using a mixed-integer linear programming (MILP) algorithm. A novel approach is proposed for day-ahead weather forecasting, leveraging real-time data extraction from reliable weather websites and applying clear sky modeling to estimate photovoltaic (PV) generation with high accuracy. By automating weather data acquisition, the methodology bridges the gap between weather predictions and practical energy management, providing utilities with a reliable tool for operating and integrating renewable energy. The optimization framework focuses on minimizing the utility bill by analyzing a distribution feeder representative of Australia’s energy infrastructure, incorporating time-of-use (TOU) and flat tariff systems across eight Australian states to simulate realistic energy costs. Furthermore, voltage constraints are applied within the optimization framework to maintain system stability and improve voltage profiles, ensuring both technical reliability and economic efficiency. The proposed framework delivers actionable insights for utility industries, enhancing the scheduling of battery energy storage systems (BESS) and facilitating the integration of renewable energy into the grid.

Suggested Citation

  • Umme Mumtahina & Sanath Alahakoon & Peter Wolfs, 2025. "A Day-Ahead Optimal Battery Scheduling Considering the Grid Stability of Distribution Feeders," Energies, MDPI, vol. 18(5), pages 1-20, February.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1067-:d:1597262
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    References listed on IDEAS

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    1. Ros, Agustin J. & Sai, Sai Shetty, 2023. "Residential rooftop solar demand in the U.S. and the impact of net energy metering and electricity prices," Energy Economics, Elsevier, vol. 118(C).
    2. Zhang, Hao & Cai, Jie & Fang, Kan & Zhao, Fu & Sutherland, John W., 2017. "Operational optimization of a grid-connected factory with onsite photovoltaic and battery storage systems," Applied Energy, Elsevier, vol. 205(C), pages 1538-1547.
    3. Vanderlei Aparecido Silva & Alexandre Rasi Aoki & Germano Lambert-Torres, 2020. "Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System," Energies, MDPI, vol. 13(19), pages 1-28, October.
    4. Ovidiu Ivanov & Bogdan-Constantin Neagu & Gheorghe Grigoras & Florina Scarlatache & Mihai Gavrilas, 2021. "A Metaheuristic Algorithm for Flexible Energy Storage Management in Residential Electricity Distribution Grids," Mathematics, MDPI, vol. 9(19), pages 1-17, September.
    5. Kang, Dongju & Kang, Doeun & Hwangbo, Sumin & Niaz, Haider & Lee, Won Bo & Liu, J. Jay & Na, Jonggeol, 2023. "Optimal planning of hybrid energy storage systems using curtailed renewable energy through deep reinforcement learning," Energy, Elsevier, vol. 284(C).
    6. Nottrott, A. & Kleissl, J. & Washom, B., 2013. "Energy dispatch schedule optimization and cost benefit analysis for grid-connected, photovoltaic-battery storage systems," Renewable Energy, Elsevier, vol. 55(C), pages 230-240.
    7. Sun, Wenjing & Zou, Yuan & Zhang, Xudong & Guo, Ningyuan & Zhang, Bin & Du, Guodong, 2022. "High robustness energy management strategy of hybrid electric vehicle based on improved soft actor-critic deep reinforcement learning," Energy, Elsevier, vol. 258(C).
    Full references (including those not matched with items on IDEAS)

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