PhD Scholarship: Smart Grids: This PhD opportunity focuses on developing a multi-agent reinforcement learning framework to secure and optimize renewable-energy-driven smart grids under cyber-attack scenarios. The project blends control engineering, data science, machine learning, cyber-security, and energy systems. Work will be carried out at the University of Exeter’s Penryn Campus under the guidance of an interdisciplinary supervisory team.
PhD Scholarship: Multi-Agent Reinforcement Learning for Cyber-Physical Smart Grids
Engineering and Physical Sciences Research Council (EPSRC) Doctoral Landscape Award
Quick Overview
| Item | Details |
|---|---|
| Designation | PhD Candidate |
| Funding | EPSRC Doctoral Landscape Award |
| Project Title | Multi-agent Reinforcement Learning-based Cyber-Physical Networked Control for Renewable Energy Fed Smart Grids under Cyber-Attacks |
| Supervisors | Dr Saptarshi Das (Lead), Dr Shuyue Lin |
| Department | Earth and Environmental Sciences |
| Programme | PhD in Earth and Environmental Science |
| Location | DDM Building, Penryn Campus, University of Exeter, Cornwall |
| Application Mode | Online Application |
| Contact | s.das3@exeter.ac.uk |
Designation
PhD Candidate – EPSRC-funded doctoral researcher.
Research Area
The work sits at the intersection of:
- Multi-agent reinforcement learning
- Cyber-physical systems
- Smart grid optimization
- Renewable energy integration
- Cyber-attack detection and resilience
- Federated learning
- Game-theoretic energy market modelling
Location
DDM Building
Penryn Campus
University of Exeter, Cornwall, UK
Eligibility / Qualification
Applicants should hold:
- A strong Master’s degree in Mathematics, Statistics, Computer Science, Engineering, or Physics
- Solid programming ability
- Strong analytical and problem-solving skills
- Good writing and presentation skills
Job Description (PhD Project Work)
Here’s what the role involves:
- Designing a multi-agent reinforcement learning architecture for real-time control of renewable-energy-fed smart grids.
- Modelling and managing DERs such as solar, wind, marine, bioenergy, and storage technologies including batteries, fuel cells, hydrogen storage, and PHEVs.
- Addressing voltage and frequency regulation, economic scheduling, and fault-tolerant control.
- Incorporating cyber-attack scenarios such as false-data injection, DoS attacks, and network reconfiguration disruptions.
- Using deep neural networks to scale decision-making in large, complex state-action spaces.
- Implementing federated learning for agent-level coordination while protecting data privacy.
- Embedding game-theoretic concepts for energy trading and negotiation among grid actors.
- Validating algorithms through simulation testbeds and hardware-in-the-loop microgrid setups.
- Advancing secure, resilient, and efficient next-generation smart grid operations.
How to Apply
Submit your application through the official University of Exeter portal.
Application Link: APPLY HERE
For project-related queries, contact: s.das3@exeter.ac.uk
Make sure you meet the entry requirements for the PhD in Earth and Environmental Science before applying.
Last Date to Apply
Until position filled
