Data-driven mining method for adjustable capacity of electric boiler with heat reservoir

In order to solve the problems of insufficient new energy consumption and serious abandonment of wind and light in the three north areas of China, the electric boiler is installed in the power grid area where the wind power consumption is insufficient, and the electric boiler is used to absorb wind power on the spot. In this paper, the heating demand and the maximum heat storage capacity of the electric boiler with heat reservoir are reversely deduced based on the user's electricity consumption data for the first time. According to the time of use electricity price in Jilin Province, the bubble sorting principle is used to tap the adjustable potential of the electric boiler with heat reservoir, and the peak power consumption is shifted to the low, so as to reduce the user's electricity charge and absorb the abandoned wind during the low period of the power grid, it plays the role of cutting peak and filling valley and stabilizing power grid. Compared with the brute force exhaustive method, this method greatly reduces the computational complexity. Compared with the method relying on the parameters of electric boiler and environmental parameters, this method greatly reduces the data which is needed to be collected, and reduces the evaluation errors caused by the performance degradation of electric boiler.