Optimal scheduling of battery energy storage train and renewable power generation

With the increasing penetration of renewable energy sources (RES), a battery energy storage (BES) Train supply system with flexibility and high cost-effectiveness is urgently needed. In this context, the mobile battery energy storage (BES) Train, as an efficient media of wind energy transfer to the load center with a time-space network (TSN), is proposed to assist the system operations. A real-time operation strategy for this TSN is proposed considering the vehicle routing problem (VRP) of BES Train. To achieve this goal, stochastic scheduling of BES Trains integrated with network constraints along wind power uncertainty is addressed in this work. Autoregressive integrated moving average (ARIMA) models generate power scenarios. Also, consider uncertainties related to wind power and standard deviation to optimize the placement of charging/discharging BES Train station. The uncertain parameter connected to wind power for scenario generations is observed. The proposed mixed-integer linear programming (MILP) to solve the model efficiently is based on tackling the strong coupling between integer and continuous variables. Using GAMS software, the proposed model is simulated on the IEEE six-bus systems, and case studies examine the capability of the suggested approach based on operational, flexibility, and reliability criteria.