Multi Objective Optimization of Electric Vehicle Charging Strategy Considering User Selectivity - Sheng Li; Xiangyu Yan; Guanhua Wang (World Electric Vehicle Journal)
Electric vehicles (EVs) are increasing in number every year, and large-scale uncontrolled EV charging can impose significant load pressure on the power grid (PG), affecting its stability and economy. This paper proposes an EV charging strategy that considers user selectivity. The user’s selection strategy includes options for fast and slow charging types, as well as the choice of whether to comply with grid-controlled charging. Charging types are selected based on the ability to reach the desired state of charge (SOC), while compliance with grid-controlled charging is determined by comparing the unit charging cost (CC). An objective function is established to minimize the peak valley load difference (PVLD) rate of PGs and users’ CC. To achieve this, an improved non-dominated sorting whale optimization algorithm (INSWOA) is proposed which initializes the population through logistic mapping, introduces nonlinear convergence factors for position updates, and uses adaptive inertia weights to improve population diversity, enhance global optimization ability, reduce premature convergence, and improve solution accuracy. Finally, simulating distribution networks in a certain region, the results obtained from the INSWOA were compared with those from the non-dominated sorting whale optimization algorithm (NSWOA) and other algorithms. The comparisons demonstrated that the INSWOA significantly reduced the PVLD rate of the PG load and users’ CCs, highlighting its high practical value.