Minimization of net-load variance using smart EV charging algorithm

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University of New Brunswick


Electric vehicles (EVs) are developing faster than ever. With the increasing number of EVs and their uncoordinated charging, the additional electric load significantly impacts the distribution grid for low penetration levels. If the EV penetration level reaches a high degree for a specific regional grid, the EV load will cause more significant risks to the grid. In this research, using localized statistical information and the Dichotomous Search Method, a charging algorithm considering the charging priority is proposed to minimize the net-load variance and the negative impacts of EV load on a medium voltage distribution. The charging priority of EVs is defined according to the State of Charge (SoC), the charging time required of individual EVs, and the power generated by the local grid. Each EV is assigned a specific period to charge. This motivates minimizing the demand peak and the valley filling by shifting the EV load.