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

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