| With the rapid development of China's new energy,the new energy technologies occupy an important position in the power systems.The collaborative optimization dispatch of wind power and EV(Electric Vehicle)has received widespread attention.Because that wind power and EV have the randomness on time and space,which will bring a threat on the power system safe and stable operation.Therefore,it has great theoretical and application value to study the optimal dispatch of wind power and EV.This paper studies a new method of optimal dispatch considering the randomness of wind power output and EV charging and discharging power.Firstly,the randomness of wind speed is described by considering the normal distribution of turbulence,and then,according to the relationship between the wind power output and the wind speed,analyze the randomness of wind power output.Secondly,set up a probabilistic model of EV charging and discharging,considering the initial charge/discharge time,the daily travel distance and the state of charge to analyze the randomness of the charge and discharge of EV.Finally,establish an optimal dispatch model considering the fuel cost,randomness cost and V2 G cost.The Monte Carlo method and the relaxation method are used to deal with the opportunity constraints and complementary constraints,and the objective function is smoothed by the cohesive function to simplify the model.Combining the cultural based PSO(Particle Swarm Optimization)to solve the optimization model,the algorithm not only can avoid the premature convergence,but also maintain a faster search speed and higher search accuracy.The results in IEEE10 system with one wind farm and one V2 G facility indicate the efficiency and practicality of the model and method.It provides a theoretical basis and practical significance for the rational planning of win d power and EV by comparing the optimization results of EV free charging,controlled charging and V2 G. |
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