Research On Load Forecasting Considering Generalized Demand Response Resources

With the booming social economy,more and more factors affect power load.The open and inclusive market environment also provides a channel for users with generalized demand response resources to interact with the power grid.Under this background,the adaptability of traditional forecasting ideas and methods is limited.In order to improve the accuracy of forecasting effectively,load forecasting considering generalized demand response resources is studied in this paper.First,the load classification and characteristics under demand response are studied,and the load characteristics of response resources are modeled.According to the categories of resource,the performance characteristics of response resources are described,and the technology of demand response is introduced.Taking electric vehicles as an example,the charging and discharging behavior models of the electric vehicle as the response resource are constructed.Through the analysis of the traditional load resource,and load characteristics of the load resource and the source-load resource under the demand response,the influence degree on the traditional load curve is illustrated.Secondly,the optimization of prediction model and data processing method are studied.In order to improve the stability of the model,the bat algorithm is used to optimize the relevant parameters,and the reverse learning strategy optimization initialization method is introduced to increase the population diversity,and the mutation strategy improved iteration mechanism is also introduced to improve the search ability.In order to extract the intrinsic characteristics of the data,the variational mode technique is used to decompose the load,and the selection of decomposing scale is analyzed to reduce the task of modeling work.Finally,according to the different characteristics of response load and traditional load,a combined prediction model considering generalized demand response resources is built.According to the mechanism of the response load,the linear time-varying model is used to fit the response load,and the prediction is based on the linear model in the support vector machine model.For the traditional load,the variational modal decomposition algorithm is used to establish a combined model,and according to the characteristics of each mode,the predictions are based on the time series method and the optimized extreme learning machine model.The validity of the proposed load forecasting method in practical application is verified by the analysis of a numerical example.