Dynamic electricity price response method for clean heating of heat accumulating type electric boiler

A dynamic electricity price response method for clean heating of a heat storage type electric boiler is characterized by comprising the following steps: S1, acquiring load data of local residents, and dividing resident peak and valley time periods by using a memory neural network and an improved K-means clustering algorithm; s2, establishing a mathematical model of the heat storage electric boiler, and proposing an operation strategy that the heat storage electric boiler participates in photovoltaic consumption under the time-of-use electricity price; and S3, taking the minimum daily heating cost of the resident as an objective function, establishing corresponding constraint conditions including heating constraint of an electric boiler, operation constraint of a heat storage device, energy balance constraint and related constraint of time-of-use electricity price, and calling a CPLEX solver by adopting MATLAB software to find out the optimal peak-valley time-of-use electricity price. According to the dynamic