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Bayesian Inference-Based Energy Management Strategy for Techno-Economic Optimization of a Hybrid Microgrid

Author

Listed:
  • Abdellah Benallal

    (Department of Mathematic, Engineering and Informatic, University of Quebec At Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada)

  • Nawal Cheggaga

    (Faculty of Technology, University of Blida 1, 270 Route de Soumaa, Ouled Yaich, Blida 09000, Algeria)

  • Adrian Ilinca

    (Mechanical Engineering Department, École de Technologie Supérieure, 1100 Rue Notre-Dame Ouest, Montréal, QC H3C 1K3, Canada)

  • Selma Tchoketch-Kebir

    (École Nationale Polytechnique, Algiers 16000, Algeria)

  • Camelia Ait Hammouda

    (Department of Mathematic, Engineering and Informatic, University of Quebec At Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada
    Faculty of Technology, University of Blida 1, 270 Route de Soumaa, Ouled Yaich, Blida 09000, Algeria)

  • Noureddine Barka

    (Department of Mathematic, Engineering and Informatic, University of Quebec At Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada)

Abstract

This paper introduces a novel techno-economic feasibility analysis of energy management utilizing the Homer software v3.14.5 environment for an independent hybrid microgrid. This study focuses on a school with twelve classes, classifying the electrical components of the total load into three priority profiles: green, orange, and red. The developed approach involves implementing demand management for the hybrid microgrid through Bayesian inference, emphasizing goal-directed decision making within embodied or active inference. The Bayesian inference employs three parameters as inputs: the total production of the hybrid system, the load demand, and the state of charge of batteries to determine the supply for charge consumption. By framing decision making and action selection as variational Bayesian inference, the approach transforms the problem from selecting an optimal action to making optimal inferences about control. The results have led to the creation of a Bayesian inference approach for the new demand management strategy, applicable to load profiles resembling those of commercial and service institutions. Furthermore, Bayesian inference management has successfully reduced the total unmet load on secondary and tertiary priority charges to 1.9%, thereby decreasing the net present cost, initial cost, and energy cost by 37.93%, 41.43%, and 36.71%, respectively. This significant cost reduction has enabled a substantial decrease in investments for the same total energy consumption.

Suggested Citation

  • Abdellah Benallal & Nawal Cheggaga & Adrian Ilinca & Selma Tchoketch-Kebir & Camelia Ait Hammouda & Noureddine Barka, 2023. "Bayesian Inference-Based Energy Management Strategy for Techno-Economic Optimization of a Hybrid Microgrid," Energies, MDPI, vol. 17(1), pages 1-16, December.
  • Handle: RePEc:gam:jeners:v:17:y:2023:i:1:p:114-:d:1306889
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    References listed on IDEAS

    as
    1. Pascual, Julio & Arcos-Aviles, Diego & Ursúa, Alfredo & Sanchis, Pablo & Marroyo, Luis, 2021. "Energy management for an electro-thermal renewable–based residential microgrid with energy balance forecasting and demand side management," Applied Energy, Elsevier, vol. 295(C).
    2. McKelvey Richard D. & Palfrey Thomas R., 1995. "Quantal Response Equilibria for Normal Form Games," Games and Economic Behavior, Elsevier, vol. 10(1), pages 6-38, July.
    3. Sarker, Eity & Seyedmahmoudian, Mehdi & Jamei, Elmira & Horan, Ben & Stojcevski, Alex, 2020. "Optimal management of home loads with renewable energy integration and demand response strategy," Energy, Elsevier, vol. 210(C).
    4. Thiaux, Yaël & Dang, Thu Thuy & Schmerber, Louis & Multon, Bernard & Ben Ahmed, Hamid & Bacha, Seddik & Tran, Quoc Tuan, 2019. "Demand-side management strategy in stand-alone hybrid photovoltaic systems with real-time simulation of stochastic electricity consumption behavior," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
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