On–Off Scheduling for Electric Vehicle Charging in Two-Links Charging Stations Using Binary Optimization Approaches

Abstract

In this study, we deal with the problem of scheduling charging periods of electrical vehicles (EVs) to satisfy the users’ demands for energy consumption as well as to optimally utilize the available power. We assume three-phase EV charging stations, each equipped with two charging ports (links) that can serve up to two EVs in the scheduling period but not simultaneously. Considering such a specification, we propose an on–off scheduling scheme wherein control over an energy flow is achieved by flexibly switching the ports in each station on and off in a manner such as to satisfy the energy demand of each EV, flatten the high energy-consuming load on the whole farm, and to minimize the number of switching operations. To satisfy these needs, the on–off scheduling scheme is formulated in terms of a binary linear programming problem, which is then extended to a quadratic version to incorporate the smoothness constraints. Various algorithmic approaches are used for solving a binary quadratic programming problem, including the Frank–Wolfe algorithm and successive linear approximations. The numerical simulations demonstrate that the latter is scalable, efficient, and flexible in a charging procedure, and it shaves the load peak while maintaining smooth charging profiles.

Analysis of Energy Flow in Hybrid and Electric-Drive Vehicles

“…The electric machine allows the vehicle's kinetic energy to be recovered during braking. A vehicle with such a system is equipped with a battery with a larger capacity [13][14][15].…”

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

“…To tackle the impracticality, Schoot Uiterkamp et al [23] presented an efficient scheduling algorithm that allows only charging above a given minimum threshold. Zdunek et al [37], in their modeling, included binary variables to indicate whether the EV is charging or not, which can potentially include the minimum charging power in the modeling. Besides traditional modeling, recent literature has successfully applied model-free reinforcement learning (RL) to EV smart charging, where the action space of the RL agent often avoids charging power allocation below the minimum charging power.…”

“…either 0 or in the range [P min , P max ]. Similar to [37], this paper utilizes extra binary decision variable s ∈ B N K×1 to constrain charging power variables by:…”

“…This work numerically compares the final mean SoCs for scheduling via simplified modeling ("ideal") and for scheduling based on accurate problem formulation including all constraints ("SoC-dependent"). Note that, the "ideal" scheduling formulation calculating p ideal covers simplified modeling as formulated in [23,37]. Fig.…”

Heuristic Greedy Scheduling of Electric Vehicles in Vehicle-to-Grid Microgrid Owned Aggregators

“…Zdunek et al. [ 26 ] developed a scheduling scheme via the binary linear programming. They concluded that the binary linear programming does not enforce the smoothness of the considered scenarios and the scheduling issues of EVs are still open for further future studies.…”