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Optimal Day-Ahead Scheduling of Microgrids with Battery Energy Storage System

Author

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  • Vanderlei Aparecido Silva

    (Department of Electrical Engineering, Federal University of Parana, Curitiba 82590-300, Brazil)

  • Alexandre Rasi Aoki

    (Department of Electrical Engineering, Federal University of Parana, Curitiba 82590-300, Brazil)

  • Germano Lambert-Torres

    (R&D Department, Gnarus Institute, Itajuba 37500-052, Brazil)

Abstract

Optimal scheduling is a requirement for microgrids to participate in current and future energy markets. Although the number of research articles on this subject is on the rise, there is a shortage of papers containing detailed mathematical modeling of the distributed energy resources available in a microgrid. To address this gap, this paper presents in detail how to mathematically model resources such as battery energy storage systems, solar generation systems, directly controllable loads, load shedding, scheduled intentional islanding, and generation curtailment in the microgrid optimal scheduling problem. The proposed modeling also includes a methodology to determine the availability cost of battery and solar systems assets. Simulations were carried out considering energy prices from an actual time-of-use tariff, costs based on real market data, and scenarios with scheduled islanding. Simulation results provide support to validate the proposed model. Data illustrate how energy arbitrage can reduce microgrid costs in a time-of-use tariff. Results also show how the microgrid’s self-sufficiency and the storage system’s capacity can impact the microgrid’s energy bill. The findings also bring out the need to consider the scheduled islanding event in the day-ahead optimization for microgrids.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:13:y:2020:i:19:p:5188-:d:424013
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    Cited by:

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    3. Paolo Tenti & Tommaso Caldognetto, 2022. "Generalized Control of the Power Flow in Local Area Energy Networks," Energies, MDPI, vol. 15(4), pages 1-21, February.
    4. Xuehan Zhang & Yongju Son & Sungyun Choi, 2022. "Optimal Scheduling of Battery Energy Storage Systems and Demand Response for Distribution Systems with High Penetration of Renewable Energy Sources," Energies, MDPI, vol. 15(6), pages 1-18, March.
    5. Hossein Abedini & Tommaso Caldognetto & Paolo Mattavelli & Paolo Tenti, 2020. "Real-Time Validation of Power Flow Control Method for Enhanced Operation of Microgrids," Energies, MDPI, vol. 13(22), pages 1-19, November.
    6. Dominika Kaczorowska & Jacek Rezmer & Michal Jasinski & Tomasz Sikorski & Vishnu Suresh & Zbigniew Leonowicz & Pawel Kostyla & Jaroslaw Szymanda & Przemyslaw Janik, 2020. "A Case Study on Battery Energy Storage System in a Virtual Power Plant: Defining Charging and Discharging Characteristics," Energies, MDPI, vol. 13(24), pages 1-22, December.
    7. Eugenio Borghini & Cinzia Giannetti & James Flynn & Grazia Todeschini, 2021. "Data-Driven Energy Storage Scheduling to Minimise Peak Demand on Distribution Systems with PV Generation," Energies, MDPI, vol. 14(12), pages 1-22, June.
    8. Maria Carmela Di Piazza, 2022. "Recent Developments and Trends in Energy Management Systems for Microgrids," Energies, MDPI, vol. 15(21), pages 1-6, November.
    9. Hafiz Abdul Muqeet & Hafiz Mudassir Munir & Haseeb Javed & Muhammad Shahzad & Mohsin Jamil & Josep M. Guerrero, 2021. "An Energy Management System of Campus Microgrids: State-of-the-Art and Future Challenges," Energies, MDPI, vol. 14(20), pages 1-34, October.

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