The Impact of Considering State-of-Charge-Dependent Maximum Charging Powers on the Optimal Electric Vehicle Charging Scheduling

Abstract

Intelligent charging solutions facilitate mobility electrification. Mathematically, electric vehicle (EV) charging scheduling formulations are constrained optimization problems.

Large-Scale Ev Charging Scheduling Considering On-Site Pv Generation by Combining an Aggregated Model and Sorting-Based Methods

“…For example, the optimal individual model is applied in to optimally distribute the EV charging load over the scheduling horizon to improve PV-EV synergy, and the applied QP will result in a polynomial increased complexity when increasing the number of variables (Goldfarb & Liu, 1990), making it difficult to apply to large-scale EV charging scheduling. Moreover, considering other practical constraints, such as the minimum charging power (considered in Qian, Fachrizal, Munkhammar, Ebel, and Adam (2023a)) or distinguishing charging and discharging (considered in Sabillón Antúnez, Franco, Rider, and Romero (2016)) will result in integer or binary decision variables, which leads to an exponential increase in computational complexity (Basu, Conforti, Di Summa, & Jiang, 2023;Morrison, Jacobson, Sauppe, & Sewell, 2016).…”

“…However, sortingbased methods usually lead to near-optimal performance. The optimal individual model can still be applied in a constrained infrastructure problem to find the optimal scheduling, such as in Qian et al (2023a). Apart from prioritizing the charging with a fixed infrastructure bottleneck, the maximum aggregated charging power can be a varying curve, such as following the PV generation in Kádár and Varga (2013), where the charging priority follows a first-come-first-server strategy.…”

“…In addition to the constraints considered in this paper for EV charging modeling, it is recommended to explore the inclusion of more practical scheduling constraints, such as minimum charging and SoCdependent maximum charging, as in Qian et al (2023a). However, incorporating additional constraints will inevitably increase the complexity of the individual scheduling models, especially if the constraint formulations require binary or integer decision variables.…”