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Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application

Author

Listed:
  • Félix Dubuisson

    (Department of Electrical Engineering, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada)

  • Miloud Rezkallah

    (Department of Electrical Engineering, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada
    CR2Ie, Sept-Îles, QC G4R 5B7, Canada)

  • Hussein Ibrahim

    (CR2Ie, Sept-Îles, QC G4R 5B7, Canada)

  • Ambrish Chandra

    (Department of Electrical Engineering, École de Technologie Supérieure, Montréal, QC H3C 1K3, Canada)

Abstract

In this paper, the predictive-based control with bacterial foraging optimization technique for power management in a standalone microgrid is studied and implemented. The heuristic optimization method based on the social foraging behavior of Escherichia coli bacteria is employed to determine the power references from the non-renewable energy sources and loads of the proposed configuration, which consists of a fixed speed diesel generator and battery storage system (BES). The two-stage configuration is controlled to maintain the DC-link voltage constant, regulate the AC voltage and frequency, and improve the power quality, simultaneously. For these tasks, on the AC side, the obtained power references are used as input signals to the predictive-based control. With the help of the system parameters, the predictive-based control computes all possible states of the system on the next sampling time and compares them with the estimated power references obtained using the bacterial foraging optimization (BFO) technique to get the inverter current reference. For the DC side, the same concept based on the predictive approach is employed to control the DC-DC buck-boost converter by regulating the DC-link voltage using the forward Euler method to generate the discrete-time model to predict in real-time the BES current. The proposed control strategies are evaluated using simulation results obtained with Matlab/Simulink in presence of different types of loads, as well as experimental results obtained with a small-scale microgrid.

Suggested Citation

  • Félix Dubuisson & Miloud Rezkallah & Hussein Ibrahim & Ambrish Chandra, 2021. "Real-Time Implementation of the Predictive-Based Control with Bacterial Foraging Optimization Technique for Power Management in Standalone Microgrid Application," Energies, MDPI, vol. 14(6), pages 1-15, March.
    • Handle: RePEc:gam:jeners:v:14:y:2021:i:6:p:1723-:d:520811
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    References listed on IDEAS

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    1. Denis Sidorov & Daniil Panasetsky & Nikita Tomin & Dmitriy Karamov & Aleksei Zhukov & Ildar Muftahov & Aliona Dreglea & Fang Liu & Yong Li, 2020. "Toward Zero-Emission Hybrid AC/DC Power Systems with Renewable Energy Sources and Storages: A Case Study from Lake Baikal Region," Energies, MDPI, vol. 13(5), pages 1-18, March.
    2. Athira M. Mohan & Nader Meskin & Hasan Mehrjerdi, 2020. "A Comprehensive Review of the Cyber-Attacks and Cyber-Security on Load Frequency Control of Power Systems," Energies, MDPI, vol. 13(15), pages 1-33, July.
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