Real time controlling algorithm for vehicle to grid system under price uncertainties

Multi-directional flow of electricity can be possible by the implementation of vehicle-to-grid system (V2G), this system allows the two side flow of electricity. The stored energy in EV batteries can be sold back to the electric grid in peak hours for load and frequency management. Therefore, a control algorithm is required to enable frequency regulation services and control the EVs charging and discharging according to grid situation. Due to price uncertainties V2G control has become more complicated, because the electricity price changes for every hour. This paper consists of a V2G controlling algorithm, which helps to counter the price uncertainties in real time. By using the Markov chain technique a controlling algorithm has been formulated, with the unidentified switching probabilities, entitled as a Morkov decision process (MDP). Inherent assessment is featured in this model to find out the long term profits of current controlling operation with dynamic electricity price. To enhance the profit margin of EV owners a Q-learning algorithm is adopted for control purposes. The proposed algorithm is then evaluated under different pricing scenarios. The results proved that, the proposed algorithm can provide better frequency regulation services, as well as increase the profits for EV owners, as compared to conventional EV charging schemes.