Quantifying and enabling the resiliency of a microgrid considering electric vehicles using a Bayesian network risk assessment

Bahador Ahmadisourenabadi, Mousa Marzband, Saman Hosseini-Hemati, S. Muhammad Bagher Sadati and Abdollah Rastgou

Energy, 2024, vol. 308, issue C

Abstract: With the increase in the number of natural disasters, caused by climate change, the role of emission and resilience has become more outstanding. In this paper, a novel model for quantifying and enabling the resiliency of a microgrid, using a Bayesian network risk assessment approach is presented. The three objective functions of cost, pollution, and resilience are optimized simultaneously using an improved lexicographic augmented ɛ-constraint method. It should be mentioned that these items are included in the proposed model due to the irreplaceable role of electric vehicles (EVs) and renewable energy sources (RESs) in distribution systems. Moreover, with the Bayesian network, the interaction between random variables is considered and a graphic view of casual relationships between stochastic and probabilistic variables is investigated in this study. The Monte Carlo scenario-based technique has handled the uncertainties of EVs and RESs. The proposed mixed integer linear programming (MILP) is implemented in the GAMS software environment under the CPLEX solver and finally, the results are discussed.

Keywords: Bayesian network; Optimization in power system; Resiliency; Risk assessment (search for similar items in EconPapers)
Date: 2024
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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:308:y:2024:i:c:s036054422402810x

DOI: 10.1016/j.energy.2024.133036

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