A Dynamic Stochastic Optimization for Recharging Plug-In Electric Vehicles

Abstract

This paper presents a recharging scheme for plug-in (hybrid) electric vehicles. Despite their many advantages such as reducing carbon footprint, lower fuel cots, and high performance, uncoordinated recharging of electric vehicles in a high-penetration system can increase system peak load and create new peaks in the demand profile, hence, reducing system reliability and operational integrity. To optimize electric vehicle recharging costs and prevent such reliability problems, a dynamic stochastic optimization method is proposed that formulates a stochastic linear programming approach taking into account load, electricity pricing, and renewable energy generation uncertainties, and solves the day-ahead problem in an offline fashion. A second online stage is also proposed that uses offline solutions, collects real-time system data, and adjusts recharging schedules to obtain a better recharging scheme once system uncertainties are revealed. The proposed method is robust to variations in different stochastic parameters, has a low communication requirement, and benefits both users and the power utility. Recharging system structure, data models, and mathematical formulation of the proposed method are presented. Results demonstrate that unlike other recharging schemes, the proposed method does not increase system peak, does not create new peaks, and fills the valleys of demand profile to optimize power system operations.