Energy Management System for Smart Electric Vehicle Charging Stations under Multiple Uncertainties : A Wasserstein Metric-Based Distributionally Robust Optimization Approach

This paper proposes an optimal power management system for electric vehicle charging station aim at maximizing profit with the consideration of uncertainties of electricity price, electric vehicle (EV) load, and photovoltaic (PV) generation. To address the limitations of conventional optimization methods such as stochastic or robust optimization, we adopt a data-driven distributionally robust optimization (DRO) framework. In addition, we propose a distributionally robust chance-constrained programming (DRCCP) approach to address the uncertainty-related constraints of EV loads and PV generation. To render the proposed DRO-based model as a finite-dimensional convex problem, we present a tractable reformulation for proposed problem. Simulation investigations show that proposed DRO-based power management system achieves the profit increase compared to conventional approaches, while effectively managing multiple uncertainties.