Research On Collaborative Scheduling Algorithm Of Electric Vehicles And Wind Power In Distribution System

Wind power as a renewable energy force, has become a hot research at home and abroad. Wind power output has volatility and randomness, large scale wind power access will bring difficulty to grid scheduling and control, sometimes in order to ensure the safe operation of the power grid can not help but be "abandon wind" behavior. The development of electric vehicles is highly valued by many countries in recent years, large-scale development will have an impact on power grid load characteristic. Electric vehicles as a kind of decentralized large capacity storage devices, if it is able to guide the behavior of charge and discharge, through the coordination with wind power and conventional power grid scheduling, not only can play the role of increasing wind power assumption, but also can reduce the peak valley difference of equivalent load of power grid and improve the security and economy of power system.Predictably, the number of electric vehicles in power system will be very large in the future. In this case, the traditional direct scheduling method may exist the problem of "dimension disaster". Another possible way, scheduling strategy through electricity price to reflect, the electric vehicle users automatic response scheduling based on electricity price incentives. Based on the above background, this paper presents a two real-time electricity price scheduling strategy considering wind power access and the user’s interactive willingness, two real-time electricity price specifically include:day-ahead price and real-time price. This paper mainly studies the calculation method of electric vehicles day-ahead price and real-time price, the concrete research content includes:(1) Load modeling for electric vehicles charging station. Electric vehicles charging station load modeling is the important link to realize the whole scheduling strategy, this paper describes parameters of electric vehicle charging stations load by probability distribution, and taking the residential charging stations as an example, simulating the charging load based on Monte Carlo simulation.(2) Day-ahead electricity price calculation method for electric vehicles. Establishing the electric vehicle day-ahead electricity price calculation model considering the unit commitment and electric vehicle V2G direct cost; Then, the optimization model is equivalent to the unit model, and the adaptive genetic algorithm is used to solve the problem.(3) Real-time electricity price calculation method for electric vehicle. The charging power of the electric vehicle is controlled by the charging device, and the device is different from the generator(no rotor inertia effect), so it has a fast speed of power adjustment. Therefore, in this paper, the wind power forecasting error is considered to compensate by electric vehicles and generators. Firstly, the mathematical model of the optimal operation of electric power company is established, and take electric vehicle users’ interactive willingness into account.Then, adopting optimization algorithm to correct each generators’ output and electric vehicles charging power time period by time period. Finally, through modifying day-ahead price to form real-time price, indirectly to pay fees to the electric vehicle users.(4) Simulation. This paper set up the IEEE-30 nodes regional power network simulation model with the wind power installed capacity of 7.69% of total installed capacity, through simulation to verify the proposed cooperative strategy of electric vehicles and wind power can reduce the effects of wind power fluctuation to power system, and improve the economic benefit of power grid operation.