Optimal Charging Of Electric Vehicles For Load Shaping: A Dual-Splitting Framework With Explicit Convergence Bounds

This paper proposes a tailored distributed optimal charging algorithm for plug-in electric vehicles (PEVs). If controlled properly, large PEV populations can enable high penetration of renewables by balancing loads with intermittent generation. The algorithmic challenges include scalability, computation, uncertainty, and constraints on driver mobility and power-system congestion. This paper addresses computation and communication challenges via a scalable distributed optimal charging algorithm. Specifically, we exploit the mathematical structure of the aggregated charging problem to distribute the optimization program, using duality theory. Explicit bounds of convergence are derived to guide computational requirements. Two variations in the dual-splitting algorithm are also presented, which enable privacy-preserving properties. Constraints on both individual mobility requirements and power-system capacity are also incorporated. We demonstrate the proposed dual-splitting framework on a load-shaping case study for the so-called California "Duck Curve" with mobility data generated from the vehicle-to-grid simulator.