Optimizing Electric Vehicle Charging Networks: Adaptive Placement and Energy Management in Uncertain Environments

Abstract

With the emerging proliferation of electric vehicles (EVs) in traffic, the optimal deployment of EV charging stations has become a critical issue due to the foreseeable significant impact on conventional power distribution systems and traffic networks. With the complex coupling between time-varying traffic flow demand and power demand during a day, it is challenging to intelligently compromise the infrastructure cost and service quality to ensure cost-effective investment as well as customers’ comfort. To deal with this particular challenge, in this study, an iterative algorithm comprising three stages with comprehensive formulations is presented to optimize the locations and sizing of charging stations, considering the EVs' behavior and customers' perspective in the composite transportation and power network. To verify the proposed algorithm, a case study based on a 25-node transportation network integrated with IEEE 33-bus system is done. Numerical results show that our algorithm can efficiently solve the problem in power-traffic coupled networks while accounting for time-varying flow demand and power demand. Additionally, we addressed the high power needs of charging stations by treating them as microgrids capable of generating their own power from renewable sources. This new approach aims to make the system more robust and dependable. To do this, we introduced a sophisticated online algorithm based on thresholds. This algorithm is crucial in managing the energy storage in each microgrid, finding the best balance between charging, discharging, and interactions with the main grid. We conducted a thorough mathematical analysis to understand how well this algorithm performs in worst-case scenarios. Then, we tested it with two detailed case studies: one using generated data and the other using real-world information. In both cases, our model consistently showed strong performance, often reaching nearly optimal results.