A MINLP Framework for Day-Ahead Scheduling of Fast Electric Vehicle Charging Stations Integrated Wind and Solar Energy Considering Load Balancing and Energy Trading

Increasing the number of fast charging stations (FCSs) is a must for the development of the electric vehicle industry. However, the simultaneous charging operation of multiple FCSs may threaten the grid stability of the power grid. One contributing factor is the highly imbalanced load profile of FCSs. Previous studies have primarily focused on maximizing profit for station owners without considering load balancing of FCSs. Therefore, in this study, we propose a bi-objective mixed-integer nonlinear programming (MINLP) framework for a comprehensive FCS model which utilizes renewable energy (including wind and solar energy) and a battery energy storage system. The proposed FCS framework is validated through simulations under three cases: maximizing profit only, minimizing load imbalance only, and jointly optimizing both objectives. The simulation results reveal that only maximizing profit leads to a high load imbalance which poses risks to grid stability. Conversely, focusing only on minimizing load imbalance may significantly reduce profits, potentially limiting the expansion of FCSs. By applying the proposed bi-objective MINLP framework, a balance between profitability and load profile smoothing is effectively achieved. This approach supports both the sustainable growth of FCSs and the protection of grid stability.