In order to address user-side load shifting difficulties and poor response effects under time-of-use electricity prices
a flexible load optimization method considering time-of-use electricity price is proposed
which firstly adopts convolutional neural network to expand user data
and then establishes an optimization model by formulating the constraints of economic cost of load transfer out
interruption
and transfer in to optimize the compensation of demand-side loads. Finally
the seasonal time-of-use electricity price scenarios and different contract capacity compensation price scenarios are analyzed by combining the actual data. The experimental results show that the model can effectively reduce the cost of electricity purchase for load aggregators
and adjust the optimization strategy for different scenarios with obvious effects.