Research on time-of-use price considering wind power uncertainty

As the large-scale new energy grid-on, it brings challenges to the power system's security and stability. In order to optimize the user's electricity consumption behavior and ease the pressure, which is caused by the new energy, on the grid, this paper proposed a time-of-use price model that takes the wind power uncertainty into account. Firstly, the interval prediction method is used to predict wind power. Then typical wind power scenes are selected by random sampling and bisecting K-means algorithm. On this basis, integer programming is used to divide the peak-valley period of multi-scenes load. Finally, under the condition of many factors such as user response based on consumer psychology, user electricity charge and power consumption, this paper takes the peak-valley difference of equivalent net load and the user dissatisfaction degree as the goal, and using NSGA-II multi-objective optimization algorithm and evaluates the Pareto solution set to get the optimal solution. In order to test the validity of the model proposed in this paper, we apply it into industrial user and wind farms in Yan'an city, China. The results show that the model can effectively ensure the user's electrical comfort while achieving the role of peak shaving and valley filling.