Abstract
In order to better manage demand response resources of user side and reduce short-term load forecasting error, a short-term load forecasting method considering demand response in virtual power plant mode is proposed. Firstly, the demand response mechanism of the virtual power plant is analyzed. Taking the maximum profit of the virtual power plant as the goal, considering the user’s energy consumption habits, self built photovoltaic, energy storage behavior and thermal electric coupling, the optimization model is established for each type of demand response resources. The CPLEX solver is called to solve the mixed integer linear programming problem after the model transformation, and the sub signals of each resource participating in the demand response are obtained. Then, based on this model, a long-term and short-term memory network model considering demand response signals is established to predict load power iteratively. At the same time, the long-term and short-term memory network model considering the demand response signals effectively makes up for the shortcomings of the traditional forecasting model without considering the demand response, and is more accurate in predicting the future trend of load change.
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