To improve the absorption ability of renewable energy in the power system

we put forward a combination（SCUC） model of chance-constrained day-ahead security constraint unit after considering the uncertainties of dynamic line rating（DLR） and wind power. Firstly

the Elman neural network method and multivariate adaptive regression splines（MARS） method are used to construct the DLR combination forecasting method based on the entropy evaluation method. Secondly

the uncertainty of DLR is depicted as the chance-constrained of DLR

and corresponding constraints of DLR usages are used to formulate a chance-constrained day-ahead SCUC model with considering the uncertainties of DLR and wind power. Finally

the proposed model is simulated on the IEEE 118-bus system. The simulation results verify the correctness and effectiveness of the proposed model

and reveal that DLR technology can significantly reduce the total operation costs of the power system and greatly improve the absorption ability of renewable energy in the power system.