Predictive Control and Generative AI for Integration of EVs, ESS, and Renewables in Safe and Autonomous Smart Grids

Abstract:

This research aims to develop a framework for integrating Electric Vehicles (EVs), Energy Storage Systems (ESS), and Renewable Energy Sources (RES) into smart grids using predictive control and generative AI. The focus is on enhancing grid safety, optimizing energy management, and improving resilience and autonomy.

Research Objectives:

The first objective is to develop predictive control algorithms tailored for real-time demand response and energy management, implementing fault detection and mitigation techniques to ensure grid stability and safety. The second objective involves utilizing generative AI to model complex grid scenarios, creating models for predictive maintenance and anomaly detection, and optimizing energy distribution and storage management. The third objective is to design a unified framework for the seamless integration of EVs, ESS, and RES, ensuring interoperability and standardization of communication protocols and data formats, while developing a robust safety and cybersecurity strategy for smart grids. The fourth objective is to conduct extensive case studies and simulations to validate the proposed framework, assess the impact of different levels of EV, ESS, and RES penetration on grid performance, and evaluate the economic and environmental benefits. Finally, the fifth objective is to develop and test a prototype of the proposed framework in a real-world environment, collaborating with industry partners and stakeholders to refine and scale the solution, and performing a comprehensive analysis of the implementation results to provide insights for future research and development.

Expected Contributions:

This research is expected to contribute novel predictive control and generative AI approaches for managing the integration of EVs, ESS, and RES in smart grids. It aims to provide a comprehensive framework that can be adopted by utility companies and grid operators worldwide, offering enhanced understanding of the challenges and solutions associated with high penetration of EVs, ESS, and RES. Additionally, it will offer recommendations for policy, standardization and regulatory frameworks to support the adoption of safe and autonomous smart grids. This research will advance smart grids. This research will advance smart grid technologies, promoting a sustainable and resilient energy future.

How to Apply:

Applicants must 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. To apply, please select the PhD in Engineering for September 2024 entry. Please specify in your PhD application that you would like to be considered for this studentship.

Applications for this studentship will be considered on a first-come, first-served basis and the position will be filled as soon as a suitable applicant is identified.

We are seeking a highly motivated doctoral candidate with a strong background in digital technologies, engineering, computer science, or a related field (ideally with Master’s degree or equivalent). The ideal candidate will possess a keen interest in power systems, control systems, artificial intelligence, and cybersecurity, along with strong analytical and problem-solving skills. Proficiency in programming and data analysis is essential, as are excellent communication and teamwork abilities. In addition, a passion for interdisciplinary research is crucial.