Smart Grid Efficiency: Integrating AI and Automation

Smart Grid is the emerging next-generation power grid concept that uses two-way flows of electricity and information to create a widely distributed automated energy delivery network. The key goal of SG is to transform the grid to greener and improve the delivery of power by shifting to sustainable technologies such as distributed generation and microgrids. More recently, SGs have rapidly evolved to incorporate sophisticated sensors, domestic power micro-generators, and IT infrastructures. However, traditional algorithms e.g., power flow algorithms, demand forecasting algorithms etc. used in SGs often struggle with the complexity and dynamism of modern energy networks. To address these challenges, DevOps practices can play a significant role in streamlining and automating the development and deployment processes, particularly in real-time data analysis, predictive maintenance, and adaptive energy management. In this context, this Ph.D. project will explore the integration of advanced automation techniques and machine learning (ML) algorithms into Smart Grid systems. The focus will be on developing ML-based algorithms to optimise energy storage, improve micro-generator efficiency, and enhance communication between grid components. Additionally, DevOps practices to automate source-code management, software development, and IoT orchestration within SGs can be explored as well. By leveraging DevOps principles, the aim is to create a robust framework for continuous integration and deployment, ensuring that updates are seamlessly incorporated into the SG infrastructure, thus improving overall system efficiency and reliability. 

The effectiveness of the proposed framework will be tested using simulation environments and real-world SG data. Experiments will be conducted to compare the performance of the new automation techniques against existing state-of-the-art methods, focusing on key metrics such as energy efficiency, fault tolerance, and system responsiveness. Prospective applicants are encouraged to contact the Supervisor to discuss their interest in this cutting-edge research at the intersection of Smart Grids, automation, and machine learning. 

Academic qualifications

A first-class honours degree, or a distinction at master level, or equivalent achievements ideally in Computer Science, Cyber Security or Artificial Intelligence.

English language requirement

If your first language is not English, comply with the University requirements for research degree programmes in terms of English language.

Application process

Prospective applicants are encouraged to contact the supervisor, Dr Saima Rafi (Email: s.rafi@napier.ac.uk) to discuss the content of the project and the fit with their qualifications and skills before preparing an application. 

Contact details

Should you need more information, please email SCEBERDL@napier.ac.uk.

The application must include: 

Research project outline of 2 pages (list of references excluded). The outline may provide details about

• Background and 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.
• Research questions or
• Methodology: types of data to be used, approach to data collection, and data analysis methods.
• 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.
• Recent and complete curriculum vitae. The curriculum must include a declaration regarding the English language qualifications of the candidate.
• Supporting documents will have to be submitted by successful candidates.
• Two academic references (but if you have been out of education for more than three years, you may submit one academic and one professional reference), on the form can be downloaded here.

Applications can be submitted here. To be considered, the application must use:

• the advertised title as project title

Download a copy of the project details here