Advanced Stochastic Control for Renewable Energy Integration in Power Grids Edinburgh Napier University in United Kingdom

This PhD project at Edinburgh Napier University addresses the growing challenges faced by national and regional power grids as renewable energy penetration increases. Traditional deterministic control and optimisation methods are often inadequate for managing the variability and uncertainty inherent in modern energy systems. The research will focus on developing advanced stochastic and learning-based control frameworks to facilitate the integration of renewable energy sources into power grids.

Using Fully Probabilistic Design (FPD), the project aims to explicitly manage uncertainty in both generation and demand. Additionally, hypergraph-based models will be explored to capture complex multi-way interactions among distributed energy resources, transmission constraints, and flexible demand units, surpassing the limitations of conventional pairwise graph representations. The expected outcomes include the creation of scalable, intelligent controllers that enhance grid stability and resilience, supporting the global transition to Net Zero emissions.

The successful candidate will develop probabilistic control and optimisation strategies, apply learning methods to improve adaptability under uncertain conditions, investigate hypergraph models for resource coordination, and validate these approaches through simulation studies relevant to wind, solar, and hybrid energy integration. Applicants should have a strong academic background (minimum 2:1 degree) in relevant fields such as Electrical/Electronic Engineering, Control Engineering, Energy Systems, Mechanical Engineering (with a focus on control or energy), Computer Science (with interest in optimisation or modelling), or Applied Mathematics (with interest in control and energy systems).

Essential skills include motivation for clean energy research, critical thinking, independent research ability, strong communication skills, and a willingness to learn new methods. Desirable skills include prior research experience, programming proficiency (MATLAB, Python), and interdisciplinary collaboration. The studentship covers full UK or international tuition fees and provides a standard living allowance at the RCUK rate (£21,383 per annum, subject to annual increases). International applicants should note that visa application costs and the NHS health surcharge are not included.

The application deadline is January 9, 2026, with the studentship starting in October 2026. Applicants must submit a completed application form, CV, two academic references, a two-page research project outline, a one-page motivation statement, and evidence of English proficiency if required. For informal enquiries, contact Dr YZ Zhou at [email protected]. Further application guidance and the online application link are provided in the position details.