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

Abdellah Benallal (), Nawal Cheggaga, Adrian Ilinca (), Selma Tchoketch-Kebir, Camelia Ait Hammouda and Noureddine Barka
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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
Noureddine Barka: Department of Mathematic, Engineering and Informatic, University of Quebec At Rimouski, 300 Allée des Ursulines, Rimouski, QC G5L 3A1, Canada

Energies, 2023, vol. 17, issue 1, 1-16

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.

Keywords: energy management; techno-economic optimization; hybrid systems; PV–wind; demand management; microgrid; Bayesian inference (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
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