IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i5p1067-d1597262.html
   My bibliography  Save this article

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
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/5/1067/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/5/1067/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    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)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Cai, Jie & Zhang, Hao & Jin, Xing, 2019. "Aging-aware predictive control of PV-battery assets in buildings," Applied Energy, Elsevier, vol. 236(C), pages 478-488.
    2. Pham, An & Jin, Tongdan & Novoa, Clara & Qin, Jin, 2019. "A multi-site production and microgrid planning model for net-zero energy operations," International Journal of Production Economics, Elsevier, vol. 218(C), pages 260-274.
    3. Hajo Terbrack & Thorsten Claus & Frank Herrmann, 2021. "Energy-Oriented Production Planning in Industry: A Systematic Literature Review and Classification Scheme," Sustainability, MDPI, vol. 13(23), pages 1-32, December.
    4. Hartmann, Bálint & Divényi, Dániel & Vokony, István, 2018. "Evaluation of business possibilities of energy storage at commercial and industrial consumers – A case study," Applied Energy, Elsevier, vol. 222(C), pages 59-66.
    5. Ghorbanzadeh, Masoumeh & Ranjbar, Mohammad, 2023. "Energy-aware production scheduling in the flow shop environment under sequence-dependent setup times, group scheduling and renewable energy constraints," European Journal of Operational Research, Elsevier, vol. 307(2), pages 519-537.
    6. Golpîra, Hêriş, 2020. "Smart Energy-Aware Manufacturing Plant Scheduling under Uncertainty: A Risk-Based Multi-Objective Robust Optimization Approach," Energy, Elsevier, vol. 209(C).
    7. Ding, Yihong & Tan, Qinliang & Shan, Zijing & Han, Jian & Zhang, Yimei, 2023. "A two-stage dispatching optimization strategy for hybrid renewable energy system with low-carbon and sustainability in ancillary service market," Renewable Energy, Elsevier, vol. 207(C), pages 647-659.
    8. Zixiao Ban & Fei Teng & Huifeng Zhang & Shuo Li & Geyang Xiao & Yajuan Guan, 2023. "Distributed Fixed-Time Energy Management for Port Microgrid Considering Transmissive Efficiency," Mathematics, MDPI, vol. 11(17), pages 1-13, August.
    9. DiOrio, Nicholas & Denholm, Paul & Hobbs, William B., 2020. "A model for evaluating the configuration and dispatch of PV plus battery power plants," Applied Energy, Elsevier, vol. 262(C).
    10. Talent, Orlando & Du, Haiping, 2018. "Optimal sizing and energy scheduling of photovoltaic-battery systems under different tariff structures," Renewable Energy, Elsevier, vol. 129(PA), pages 513-526.
    11. Kang, Hyuna & Jung, Seunghoon & Kim, Hakpyeong & Jeoung, Jaewon & Hong, Taehoon, 2024. "Reinforcement learning-based optimal scheduling model of battery energy storage system at the building level," Renewable and Sustainable Energy Reviews, Elsevier, vol. 190(PA).
    12. Zhang, Hao & Lei, Nuo & Chen, Boli & Li, Bingbing & Li, Rulong & Wang, Zhi, 2024. "Modeling and control system optimization for electrified vehicles: A data-driven approach," Energy, Elsevier, vol. 310(C).
    13. Nge, Chee Lim & Ranaweera, Iromi U. & Midtgård, Ole-Morten & Norum, Lars, 2019. "A real-time energy management system for smart grid integrated photovoltaic generation with battery storage," Renewable Energy, Elsevier, vol. 130(C), pages 774-785.
    14. Darghouth, Naïm R. & Wiser, Ryan H. & Barbose, Galen & Mills, Andrew D., 2016. "Net metering and market feedback loops: Exploring the impact of retail rate design on distributed PV deployment," Applied Energy, Elsevier, vol. 162(C), pages 713-722.
    15. Heine, Karl & Thatte, Amogh & Tabares-Velasco, Paulo Cesar, 2019. "A simulation approach to sizing batteries for integration with net-zero energy residential buildings," Renewable Energy, Elsevier, vol. 139(C), pages 176-185.
    16. Isa, Normazlina Mat & Das, Himadry Shekhar & Tan, Chee Wei & Yatim, A.H.M. & Lau, Kwan Yiew, 2016. "A techno-economic assessment of a combined heat and power photovoltaic/fuel cell/battery energy system in Malaysia hospital," Energy, Elsevier, vol. 112(C), pages 75-90.
    17. Fahad Alismail & Mohamed A. Abdulgalil & Muhammad Khalid, 2021. "Optimal Coordinated Planning of Energy Storage and Tie-Lines to Boost Flexibility with High Wind Power Integration," Sustainability, MDPI, vol. 13(5), pages 1-17, February.
    18. Cui, Can & Xue, Jing, 2024. "Energy and comfort aware operation of multi-zone HVAC system through preference-inspired deep reinforcement learning," Energy, Elsevier, vol. 292(C).
    19. Bandyopadhyay, Arkasama & Leibowicz, Benjamin D. & Webber, Michael E., 2021. "Solar panels and smart thermostats: The power duo of the residential sector?," Applied Energy, Elsevier, vol. 290(C).
    20. Dougier, Nathanael & Garambois, Pierre & Gomand, Julien & Roucoules, Lionel, 2021. "Multi-objective non-weighted optimization to explore new efficient design of electrical microgrids," Applied Energy, Elsevier, vol. 304(C).

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:gam:jeners:v:18:y:2025:i:5:p:1067-:d:1597262. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.