An Efficient Implementation of Automatic Differentiation in Interior Point Optimal Power Flow

摘要

This paper presents an improved implementation of automatic differentiation (AD) technique in rectangular interior point optimal power flow (OPF). Distinguished from the existing implementation of AD, the proposed implementation adds a subroutine to identify all constant first-order and second-order derivates by AD and form a list of constant derivates before the processing of iterations. At every iteration of interior point OPF algorithm, only the changing derivates are updated by AD tool. An excellent AD software—ADC—is used as a basic AD tool to finish the proposed implementation. A user-defined model interface is provided with AD technique to enhance performance and flexibility. Numerical studies on several large-scale power systems indicate that the proposed implementation of AD can compete with hand code in execution speed without loss of maintainability and flexibility of AD codes. This paper demonstrates that AD technique has an application potential in online operating environments of power systems instead of hand-coded derivates, and greatly relieves the burdens of software developers.