Wind Power Fluctuation Absorption through EV Charging Power Control Based on Markov Chain

It is a general trend for new energy vehicles to replace traditional fuel vehicles, while the number of electric vehicles has increased dramatically, the pressure of charging on the power grid has also increased. Therefore, from the angle of wind power absorption of electric vehicles, this paper mainly studies the time series of wind speed power. The time series of wind speed is decomposed into the sum of 15-minute average wind speed signal and turbulent wind speed signal. The relationship between the magnitude of turbulent signal and average wind speed is studied. Based on the discrete Markov chain, the battery state transition probability of electric vehicle is established according to the results of Monte Carlo simulation. The state transition matrix varies with time, and the stationary distribution is obtained to obtain the charging potential of the electric vehicle in a certain area, that is, the charging demand of the electric vehicle with time. The main factors affecting the state transition matrix are analyzed, and the results of each factor affecting the state transition matrix are given. Thus, a more accurate charging potential model can be obtained. By predicting the average wind speed, the number of electric vehicles participating in the regulation can be obtained. The results of simulation indicate that wind fluctuation can be effectively reduced by controlling charging power of electric vehicle.