Degree Name

Doctor of Philosophy


School of Electrical, Computer and Telecommunications Engineering


In recent decades, the integration of intermittent and un-schedulable renewable energy brings more uncertainties into the real-time operation of the power systems that can adversely impact the power grid resilience.

In many reported research works, advanced predictive analytics and computational intelligence optimization for the real-time operations and controls of the power system, based on online monitoring, parameter optimization, and real-time decision making, have been proposed to improve power system resilience.

In this thesis, novel predictive and large-scale optimization methodologies are proposed to respond quickly and proactively to uncertain changes in the power systems to be able to make intelligent long- and short-term planning and decision-making strategies to improve the power system resilience. The proposed optimization methodologies involve multi-period, multi-objective, large-scale optimizations that can utilize parallel programming taking into consideration the increasing penetration levels of distributed energy resources (DERs), such as the small-scale renewable energy resources (RES), the energy storage system in the form of a battery energy storage system (BESS), and the increasing demands for a fast response to fluctuations on the real-time price and load demand.

This thesis presents a set of predictive analytics and real-time computational intelligence algorithms and strategies to solve the large-scale optimization problems in modem power systems to improve the power system resilience considering the uncertainties of the sources, loads, and electricity prices.

FoR codes (2008)




Unless otherwise indicated, the views expressed in this thesis are those of the author and do not necessarily represent the views of the University of Wollongong.