Advanced Stochastic Control for Renewable Energy Integration in Power Grids

About the Project

As renewable penetration grows, national and regional power grids face increasing challenges in balancing variable supply with demand, maintaining stability, and ensuring resilience against uncertainty. Traditional deterministic control and optimisation approaches often fail to cope with the scale and variability of modern energy systems.

This PhD will explore probabilistic and learning-based control frameworks for renewable integration in power grids. Using Fully Probabilistic Design (FPD), the project will investigate how to explicitly manage uncertainty in generation and demand. In parallel, hypergraph-based models will be developed to capture multi-way couplings among distributed energy resources, transmission constraints, and flexible demand units—moving beyond the limits of pairwise graph representations.

The outcomes will contribute to a new generation of stochastic, scalable, and intelligent controllers for renewable-rich grids, supporting the global Net Zero transition.

What you will do

• Develop probabilistic control and optimisation strategies for grid-level renewable integration.
• Apply learning methods to improve adaptability under variable and uncertain operating conditions.
• Investigate hypergraph models for multi-resource coordination in smart grids.
• Validate approaches in simulation studies of large-scale power networks, with relevance to wind, solar, and hybrid energy integration.

Academic qualifications

A first degree (minimum 2:1 classification) in one of the following areas:

• Electrical and Electronic Engineering
• Control Engineering / Automation
• Energy Systems / Renewable Energy Engineering
• Mechanical Engineering (with strong focus on control or energy)
• Computer Science (with interest in optimisation or modelling)
• Applied Mathematics (with interest in control and energy systems)

English language requirement

IELTS score must be at least 6.5 (with not less than 6.0 in each of the four components). Other, equivalent qualifications will be accepted. Full details of the University’s policy are available online.

Subject knowledge

• Control theory
• Energy/power systems and renewable integration
• Optimisation methods
• Probability and stochastic theory
• Mathematics for control and systems analysis
• Programming and simulation tools

Essential attributes

• Strong motivation to contribute to clean energy and advanced control research
• Ability to think critically and solve complex problems
• Capacity for independent research and self-management
• Good written and verbal communication skills
• Willingness to learn new methods and develop interdisciplinary knowledge
• Commitment to completing a challenging long-term project

Desirable attributes

• Prior research experience in control, optimisation, or renewable energy
• Familiarity with programming for simulation and data analysis (e.g. MATLAB, Python)
• Ability to connect theoretical methods with practical applications
• Experience in presenting research or writing technical reports
• Interest in interdisciplinary collaboration across engineering, mathematics, and energy domains
• Enthusiasm for developing transferable skills and contributing to the research community

APPLICATION CHECKLIST

• Completed application form
• CV
• 2 academic references, using the Postgraduate Educational Reference Form (download)
• Research project outline of 2 pages (list of references excluded). The outline may provide details about
  1. Background and motivation of the project. The motivation, explaining the importance of the project, should be supported also by relevant literature. You can also discuss the applications you expect for the project results.
  2. Research questions or objectives.
  3. Methodology: types of data to be used, approach to data collection, and data analysis methods.
  4. List of references.

The outline must be created solely by the applicant. Supervisors can only offer general discussions about the project idea without providing any additional support.

• Statement no longer than 1 page describing your motivations and fit with the project.
• Evidence of proficiency in English (if appropriate)

To be considered, the application must use

• the advertised title as project title

For informal enquiries about this PhD project, please contact y.zhou@napier.ac.uk

Application Enquiries: https://www.napier.ac.uk/research-and-innovation/doctoral-college/application-guidance