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Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification

Author

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  • Mahmoud Elkazaz

    (Department of Electrical & Electronic Engineering, The University of Nottingham, Nottingham NG7 2RD, UK
    Department of Electrical Power & Machines Engineering, Tanta University, Tanta 31527, Egypt)

  • Mark Sumner

    (Department of Electrical & Electronic Engineering, The University of Nottingham, Nottingham NG7 2RD, UK)

  • David Thomas

    (Department of Electrical & Electronic Engineering, The University of Nottingham, Nottingham NG7 2RD, UK)

Abstract

A new energy management system (EMS) is presented for small scale microgrids (MGs). The proposed EMS focuses on minimizing the daily cost of the energy drawn by the MG from the main electrical grid and increasing the self-consumption of local renewable energy resources (RES). This is achieved by determining the appropriate reference value for the power drawn from the main grid and forcing the MG to accurately follow this value by controlling a battery energy storage system. A mixed integer linear programming algorithm determines this reference value considering a time-of-use tariff and short-term forecasting of generation and consumption. A real-time predictive controller is used to control the battery energy storage system to follow this reference value. The results obtained show the capability of the proposed EMS to lower the daily operating costs for the MG customers. Experimental studies on a laboratory-based MG have been implemented to demonstrate that the proposed EMS can be implemented in a realistic environment.

Suggested Citation

  • Mahmoud Elkazaz & Mark Sumner & David Thomas, 2019. "Real-Time Energy Management for a Small Scale PV-Battery Microgrid: Modeling, Design, and Experimental Verification," Energies, MDPI, vol. 12(14), pages 1-26, July.
    • Handle: RePEc:gam:jeners:v:12:y:2019:i:14:p:2712-:d:248714
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    Cited by:

    1. George Kandilogiannakis & Paris Mastorocostas & Athanasios Voulodimos, 2022. "ReNFuzz-LF: A Recurrent Neurofuzzy System for Short-Term Load Forecasting," Energies, MDPI, vol. 15(10), pages 1-18, May.
    2. Mahmoud Elkazaz & Mark Sumner & Seksak Pholboon & Richard Davies & David Thomas, 2020. "Performance Assessment of an Energy Management System for a Home Microgrid with PV Generation," Energies, MDPI, vol. 13(13), pages 1-23, July.
    3. Luis Gabriel Marín & Mark Sumner & Diego Muñoz-Carpintero & Daniel Köbrich & Seksak Pholboon & Doris Sáez & Alfredo Núñez, 2019. "Hierarchical Energy Management System for Microgrid Operation Based on Robust Model Predictive Control," Energies, MDPI, vol. 12(23), pages 1-19, November.
    4. Nemanja Mišljenović & Matej Žnidarec & Goran Knežević & Damir Šljivac & Andreas Sumper, 2023. "A Review of Energy Management Systems and Organizational Structures of Prosumers," Energies, MDPI, vol. 16(7), pages 1-32, March.

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