The invention provides a large-
scale unit commitment solving method based on first-order optimization and successive fixation, and relates to the field of
data processing. In the method, through instance
perception preprocessing scaling, normalization is carried out on related parameters of a power
system generator set, a linear relaxation model is generated and input into a first-order
linear programming solver, and a continuous domain solution vector is obtained through iterative updating. And calculating confidence and logic consistency of binary start-stop, start-stop and shutdown variables based on the solution vector, and outputting a weighted confidence rule set for successive fixation. And if the conditions are met, solidifying the variables, updating the linear relaxation model, and inputting into a
solver to obtain a new solution. After multiple rounds of successive fixation are executed, if the number of the fixed binary variables exceeds a threshold value, a small-scale mixed integer
linear model is constructed, accurate solving is conducted through a
branch shear method, and finally an
executable generator set combination plan is output. By implementing the technical scheme provided by the invention, the solving efficiency of the
server in
processing the SCUC problem can be improved.