Quadratic Taylor Expansion-Based Approximate Dynamic Programming for Fully Decentralized AC-OPF of Multi-Area Power Systems

Abstract

In this paper, a quadratic Taylor expansion-based approximate dynamic programming (QTE-ADP) algorithm is proposed for the decentralized solution of multi-area AC-optimal power flow (MA-ACOPF). Different from traditional ADP algorithms, the proposed algorithm does not need to approximate the value function via a given function structure, but directly obtains the quadratic Taylor expansion (QTE) of value function based on KKT conditions. Moreover, compared with the commonly used linearized value function approximation techniques, the information used to approximate the value function is also extended from first order to second order in the proposed algorithm, which helps to improve the accuracy and efficiency of ADP. When using QTE-ADP to solve the MA-ACOPF problem, only the boundary voltage information needs to be interchanged between adjacent areas, which facilitates preserving the information privacy and decision independence of each area. Numerical simulations on several test systems demonstrate that the proposed algorithm has good performance in terms of accuracy, efficiency, and adaptability.