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Assisting Residential Distribution Grids in Overcoming Large-Scale EV Preconditioning Load.

Authors :
Antoun, Joseph
Kabir, Mohammad Ekramul
Atallah, Ribal
Moussa, Bassam
Ghafouri, Mohsen
Assi, Chadi
Source :
IEEE Systems Journal; Sep2022, Vol. 16 Issue 3, p4345-4355, 11p
Publication Year :
2022

Abstract

The repercussion of increased electric vehicle (EV) charging demand is notable at the distribution grid especially during the cold morning, while users tend to precondition their vehicles before leaving their premises. Moreover, due to the price declination, a tendency of installing level 2 chargers in residential premises is anticipated, which should stimulate the appearance of a new peak to the residential load profile. Hence, multiple scenarios of preconditioning are simulated, and the corresponding network’s quality metrics (e.g., voltage level and power losses) are assessed to analyze the impact. And a remarkable consequence is observed. As a consequence, to mitigate the consequences and manage the new peak load, the optimal reconfiguration of network is implemented, and unfortunately, with a larger number of EVs, this technique fails to attain the minimum voltage level. Therefore, leveraging this high number of EVs, instead of relying on the network reconfiguration, power is assumed to be injected from idle EVs through vehicle-to-grid (V2G) energy transmission. An integer linear program is formed to schedule a set of EVs in participating in V2G, and the outcome indicates that V2G alone could not compensate for the disturbance in the network. Accordingly, a hybrid method of V2G and reconfiguration is proposed and evaluated to assist the network in handling the new peak load, and this hybrid solution reduces power losses in the network by 50% on average and maintains the voltage level above the operational threshold of 0.95 p.u. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
19328184
Volume :
16
Issue :
3
Database :
Complementary Index
Journal :
IEEE Systems Journal
Publication Type :
Academic Journal
Accession number :
158869500
Full Text :
https://doi.org/10.1109/JSYST.2021.3104185