Adaptive and AI-based Control Strategies for Grid and Market Integration of Renewable Energy Resources
Not stated
- Funding
- Self-Funded PhD Students Only
- Application deadline
- Year-round applications
About the project
About the Project The integration of renewable energy sources, such as wind, solar, and hydropower, poses challenges for grid stability and energy market operations due to their variability and intermittency. Traditional grid control mechanisms often lack the adaptability needed to fully leverage modern technologies like energy storage and distributed energy resources (DERs). This research focuses on developing AI-driven adaptive control strategies to enhance grid stability, flexibility, and market integration of renewables. Key objectives include designing AI-powered algorithms for voltage stability, frequency regulation, and renewable energy forecasting, as well as optimizing energy dispatch, storage scheduling, and participation in ancillary service markets. Advanced techniques such as machine learning, reinforcement learning, and optimization will be used to determine optimal energy storage placement and operation. The research will also employ digital twin simulations to validate the proposed models under real-world grid and market conditions. Ultimately, this work aims to enable more reliable, flexible, and economically efficient power systems, supporting the seamless integration of renewable energy and storage. Entry Requirements: Candidates should have (or expect to obtain) a minimum of a UK upper second class honours degree (2.1) or equivalent in electrical, control, or energy systems engineering, How to Apply: Applicants should apply via the University’s online application system at https://www.york.ac.uk/study/postgraduate-research/apply/ . Please read the application guidance first so that you understand the various steps in the application process.