Date of Award


Document Type


Degree Name

Doctor of Philosophy (PhD)


Electrical and Computer Engineering

Committee Chair/Advisor

Chris S. Edrington

Committee Member

Johan H. R. Enslin

Committee Member

Zheyu Zhang

Committee Member

Benjamin J. Lawler


Future distribution grids are expected to face an increasing penetration of heterogeneous distributed energy resources (DERs) and electric vehicles (EVs). This landscape change will pose challenges to the control and management of distribution grids because of the variability of renewable energy resources and EV charging. In addition, multiple DERs dispersed over networks can also challenge the grid operation and maintenance as various DERs at various locations are needed to be monitored and managed. However, customers will not be content with reductions in power quality, reliability, economy, safety, or security. To enhance the effectiveness of grid control and management, future grids will be given more autonomy in the form of advanced distribution management systems (ADMS). Energy management (EM) is one of the main constituents of ADMS to enhance system efficiency. EM typically considers only saving fuel consumption costs. However, grids’ components degrade over time, and it adds up to the systems’ operation cost. Knowing the degradation behaviors of grids’ components to control them properly can reduce their degradation, and consequentially it can reduce the total operation cost. In addition, in order to maintain the highest reliability of the system, degradation models should also be developed along with appropriate decision-making strategies that allow information regarding components’ status to be integrated with ADMS. This dissertation proposes a framework to integrate a degradation forecasting (DF) layer into ADMS to abate components’ degradation processes, reduce the total operation cost, and enhance system reliability. The DF layer will collaborate with EM to find a solution that compromises fuel consumption costs and degradation costs.



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