PhD in Statistics

About the Project The future power grid will be a highly complex cyber-physical system, integrating multiple distributed energy resources (DERs) such as solar, wind, marine, and bioenergy alongside conventional synchronous generators. Efficient management of energy storage devices, including batteries, fuel cells, supercapacitors, hydrogen storage, and plug-in hybrid electric vehicles (PHEVs), will be crucial for ensuring grid stability and economic operation in future high, medium transmission and low voltage power distribution networks. This integration introduces significant challenges in voltage and frequency regulation, energy scheduling, and overall smart grid system optimization. Moreover, such complex interconnections between power system dynamics, communication networks, and information technology increases the grid’s exposure to cyber-attacks, which can compromise measurement signals, disrupt control commands, or induce model or data-driven instability. This project aims to develop a robust multi-agent reinforcement learning (MARL) framework for cyber-physical networked fault-tolerant control of renewable energy-fed smart grids under adversarial conditions. Multiple autonomous agents will represent distributed generators, storage units, and controllable loads, each learning optimal control policies through independent interaction with the environment. The MARL formulation will address coordination problems, conflict resolution, and cooperative/competitive behaviour among cyber-physical actors/agents while optimizing for their local and global objectives such as minimizing generation cost, reducing emissions, maintaining smart grid voltage stability indicators and designing fault tolerant control strategies.