Integrating degradation forecasting into distribution grids’ advanced distribution management systems

Abstract

Advanced distribution management systems (ADMSs) are considered tools for deploying control and management strategies to address challenges in future distribution grids populated with distributed energy resources (DERs). This work presents a framework to integrate a degradation forecasting (DF) layer into ADMSs. Based on observations of considered DERs’ degradation behaviors, a load-dependent degradation model is constructed and integrated into a dynamical Markov chain-based degradation prediction model. Multiple sources of data can be used to train the Markov prediction model, and then Evidence Theory (ET) is used to fuse predicted results from the prediction model to enhance the reliability of the prediction model. Then, the predicted results are used to adjust energy management (EM), which is a component of the ADMS concept, to abate the degradation of components with higher degradation costs. The proposed strategy is verified on the IEEE 33 bus system by numerical simulations. In addition, controller-hardware-in-the-loop (CHIL) experimentation for the proposed scheme is implemented. Both the numerical simulations and CHIL experimentation show operation cost savings for the studied system.