Impact Analysis and Optimization of EV Charging Loads on the LV Grid: A Case Study of Workplace Parking in Tunisia

Lazher Mejdi (), Faten Kardous and Khaled Grayaa
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Lazher Mejdi: National School of Advanced Sciences and Technologies of Borj Cedria (ENSTAB), University of Carthage, LR16ES08 Research Laboratory of Smart Grids and Nanotechnology (LaRINa), Hammam-Chott 1164, Tunisia
Faten Kardous: National School of Advanced Sciences and Technologies of Borj Cedria (ENSTAB), University of Carthage, LR16ES08 Research Laboratory of Smart Grids and Nanotechnology (LaRINa), Hammam-Chott 1164, Tunisia
Khaled Grayaa: National School of Advanced Sciences and Technologies of Borj Cedria (ENSTAB), University of Carthage, LR16ES08 Research Laboratory of Smart Grids and Nanotechnology (LaRINa), Hammam-Chott 1164, Tunisia

Energies, 2022, vol. 15, issue 19, 1-18

Abstract: With the growth of electric vehicles’ (EVs) deployment as a substitute for internal combustion engine vehicles, the impact of this kind of load on the distribution grid cannot be neglected. An in-depth study needs to be performed on a regional basis to investigate the impacts of electric vehicle (EV) charging on the grid for each country’s grid configuration and specifications, in order to be able to reduce them. In this work, we built a case study of a charging infrastructure of a Tunisian workplace parking lot, by combining different measured data and simulations using OpenDSS and Matlab. The first objective was to analyze the integration impacts on the Tunisian low-voltage (LV) grid including phase unbalance, voltage drop, harmonics, and power losses. We found that 10 metric tons of carbon dioxide ( MtCO 2 ) in yearly emissions were caused by power losses, and 50% of these emissions came from harmonic losses, which can be avoided by active and passive filtering. The second objective was to decrease phase unbalance by formulating an optimization problem and solving it by combining a genetic algorithm (GA) and a pattern search (PS) in the Matlab environment. The GA returned interesting results by balancing the phases, and the addition of PS as a hybrid function reduced the convergence speed by 38%. Moreover, the optimization led to a reduction of 83% in the neutral current maximum value, a reduction of 67% in the violation period of the voltage drop, a minimum voltage drop of 0.94 pu. and kept the total current consumption within a fixed limit. The developed model can be adapted to any similar workplace parking facility in Tunisia that is equipped with an EV charging infrastructure.

Keywords: e-mobility; LV grid; harmonic analysis; phase balancing; power quality; genetic algorithm; pattern search (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: 2022
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