A Preliminary Study of Temporal Variables Reduction to Reduce Computational Time of V2G Scheduling in Stochastic Programming Approach

EV (electric vehicle) aggregators can get more profit by buying and selling electricity from/to the market through their V2G (Vehicle to Grid) scheduling with increase of the number of EV. As EV usage for them includes uncertainty, V2G scheduling problem is formulated by stochastic programming with scenarios and their probabilities. However, the increase of number of scenarios makes a burden of computational time for EV aggregators. Then the scenario reduction techniques have been developed to shorten the computational time. This paper proposes a new method of temporal variables reduction to reduce the computational time. The proposed method is to reduce the variables related to the time slots in the problem. This paper also shows the simulation results of the new method applied to Japanese electric market and its comparison to the traditional scenario reduction method.