Joint EV Eco-Charging Strategy Considering Spatiotemporal Flexibility in Transportation– Distribution Networks

To evaluate the energy savings of electric vehicles (EVs) in transportation-distribution networks (T-DNs), this paper proposes a tri-level collaborative charging scheduling framework (Tri-CCSF) based on EV’s spatio-temporal flexibility, integrating their spatial flexibility before charging requests and charging flexibility at charging stations (CSs). Tri-CCSF is designed to deliver an integrated optimization of economic charging navigation and scheduling, considering the implications of charging load on the main power grid and the inherent uncertainties associated with renewable energy (RE). The first level utilizes aggregated EV charging flexibility to minimize power exchange between CS and main power grid. The second level develops low-cost charging plans for each EV user based on energy attributes, which helps avoid the “curse of dimensionality” caused by large-scale EVs. The third level proposes an EV spatio-temporal dynamic energy consumption estimation model based on macroscopic traffic flow speed prediction, which informs economical charging navigation. Furthermore, the proximal policy optimization-alternating direction method of multipliers (PPO-ADMM) algorithm is proposed to enhance the solution efficiency of the quadratic programming problem. Based on real-world map experiments, Tri-CCSF benefits T-DNs and increases RE utilization by 4.5%-36.2% and EV energy consumption estimation accuracy by 5%-7.5%.