Steffensen's Acceleration Method for ADMM for Solving EV Charging Optimization Problems

Charging control strategies determine the charging schedules of electric vehicles (EVs) to meet the travel demands of residents. Due to the bandwidth of the communication system, the scalability, and the robustness of the controller, distributed model predictive control (DMPC) approaches are widely adopted for EV charging control. This paper proposes a novel Steffensen Alternating Direction Method of Multipliers (S-ADMM) algorithm to accelerate the solving process of DMPC charging control planning problems. Via a case study, the performance of the proposed S-ADMM algorithm and the traditional ADMM algorithm are compared. The S-ADMM algorithm shows better performance in terms of convergence speed.