Adaptive Mixed-Integer Linear Programming-Based Energy Management System of Fast Charging Station with Nuclear–Renewable Hybrid Energy System

Abu Bakar Siddique and Hossam A. Gabbar ()
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Abu Bakar Siddique: Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1H 7K4, Canada
Hossam A. Gabbar: Faculty of Energy Systems and Nuclear Science, Ontario Tech University (UOIT), Oshawa, ON L1H 7K4, Canada

Energies, 2023, vol. 16, issue 2, 1-22

Abstract: The concept of transportation electrification is proliferating due to its high impact on emission reduction. However, the increased usage of electric vehicles strains the power grid’s charging infrastructure. As a result, to reduce demand on the power grid, lower the emissions, and solve the intermittency problem of Renewable Energy Sources (RESs), a Nuclear–renewable Hybrid Energy System (N-R HES) is proposed in this research to support the load demand of a Fast Charging Station (FCS). Fulfilling the power demand of the FCS while reducing the generation cost and waste of energy is a vital issue, and hence, energy management with optimization is a must for the hybrid energy system. To address this issue, a model reference adaptive control with a mixed-integer linear programming-based energy management method was modelled to accomplish the charging station’s extensive performance. MATLAB/Simulink software has been used to model and simulate the proposed system, and the results are analyzed. The assessment shows that the proposed energy management system offers an optimized performance of the fast charging station integrating with nuclear and renewable energy.

Keywords: nuclear–renewable hybrid energy system; fast charging station; energy management system; optimization; adaptive control; mixed-integer linear programming (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
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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