Transfer learning-based cost function approximation for look-ahead dispatch of power systems

Abstract

In this paper, a transfer learning-based cost function approximation (TL-CFA) algorithm is proposed for the look-ahead dispatch of power systems. Cost function approximation (CFA) provides an effective sequential decision framework in the stochastic environment, which is suitable for the look-ahead dispatch of power systems with uncertainties. However, the parameters of CFA need to be iteratively updated, which may lead to additional computational burden, and thus hinder its real-time decision-making. Different from the traditional CFA algorithm, a transfer learning (TL) technique is used to describe the relationship between the parameters and stochastic information in the proposed TL-CFA algorithm. Thus, in the look-ahead dispatch process, the parameters of CFA can be directly obtained through the transfer function, which saves the computational time, while almost not losing the accuracy. Case studies are carried out on a modified IEEE 30-bus system and a real 2778-bus system, and the results demonstrate the effectiveness of the proposed approach.