Energy Systems
The Institute for Energy Systems at the University of Edinburgh invites applications for a PhD studentship in "Next-Generation Electricity Market Modelling for Sustainable Energy Systems." This project offers a unique opportunity to contribute to transformative research in the transition of electricity markets in the UK.
The UK electricity market requires significant reform to meet the demands of a net-zero energy future. This project will look to help provide quantitative evidence to support the design of future electricity markets by developing the next-generation of open-source electricity market models. Model development will ideally build on state-of-the-art tools such as PyPSA for energy system optimisation and AMIRIS for agent-based modelling of market behaviour.
The research will focus on rethinking the UK’s electricity market design, addressing key REMA challenges, including locational signals, promoting investment in renewables, and enhancing system flexibility. By integrating renewable energy, energy storage (e.g., hydrogen production), and demand-side participation into these models, the project aims to optimise market efficiency while supporting the UK’s broader decarbonisation goals.
The candidate will develop a wide range of skills in simulation, optimisation, and data analysis which are widely applicable to future career development. Additionally, there are opportunities for engaging with an open and inclusive community of open-source energy system developers both within IES and globally.
*NOTE: Competitive funding may be applied for if applications are received before the 31st January, 2025 via EPSRC DLA scheme - https://www.ukri.org/news/major-investment-to-support-the-next-generation-of-researchers/
**NOTE: This position will remain open until filled. Early contact is highly recommended.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Essential background:
2.1 or above (or equivalent) in Engineering, Mathematics, Physics, Energy Engineering/Economics, Informatics, or similar Programming in Python, Julia or other high-level language
Desirable background:
Knowledge of energy economics Experience of energy system modelling and optimisation Data analysis, optimisation and/or machine learning
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.
As AI and data centers expand, their energy demands are becoming a significant sustainability issue, with AI projected to consume 85-134 TWh of electricity by 2027—comparable to a small country's annual usage. The growing adoption of AI models like OpenAI’s ChatGPT and Google’s Bard underscores the need to reduce data centers' carbon footprints. Future AI data centers will focus on low-carbon strategies to mitigate their environmental impact.
This cross-disciplinary PhD program in electrical engineering and computer science aims to develop real-time strategies for minimizing the carbon footprint of data centers. The research will optimize the jobs scheduling at large scale data center, addressing the challenge of managing high volumes of computational tasks required by large AI models. Advanced machine learning, optimization, and data analytics techniques will be explored to enable a more proactive response to the electricity supply profile, ensuring that data center operations are effectively integrated with the broader energy network.
A key component of the program is exploring how AI data centers can support energy networks, providing grid services like frequency and demand response during periods of low-carbon electricity or when the grid requires ancillary services. This research seeks to enhance the interaction between data centers and the electricity grid, promoting better coordination and contributing to grid stability and efficiency.
The solution will focus on developing practical, low-carbon data center operations for real-world deployment. By improving demand response services, data centers can operate more sustainably while helping stabilize the grid. This research is vital for addressing the energy demands of AI systems and ensuring that future AI developments align with global sustainability goals.
To apply for this position, It will be an advantage if applicants have relevant industry or research experience, or good programming skills using Python, Julia or matlab. Strong knowledge in one or more of the following areas is highly desirable:
• Power network modelling and control
• Optimization, data science, operational research and mathematical programming.
• Machine Learning and Reinforcement Learning
• Computer science with understanding of data center structure and operation
**NOTE: There is no closing date for this position, which will remain open until filled. Early contact is highly recommended.
Reference:
‘Generative AI’s Energy Problem Today Is Foundational’ https://spectrum.ieee.org/ai-energy-consumption Misaghian, M. Saeed, et al. "Assessment of carbon-aware flexibility measures from data centres using machine learning." IEEE Transactions on Industry Applications 59.1 (2022): 70-80. Sarkar, Soumyendu, et al. "Real-time Carbon Footprint Minimization in Sustainable Data Centers with Reinforcement Learning." NeurIPS 2023 Workshop on Tackling Climate Change with Machine Learning. 2023.
The University of Edinburgh is committed to equality of opportunity for all its staff and students, and promotes a culture of inclusivity. Please see details here: https://www.ed.ac.uk/equality-diversity
Interested applicants are welcome to contact Dr Wei Sun by email for pre-application enquiries (W.Sun@ed.ac.uk).
Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in Electrical Engineering, Computer Science, Mathematics, or related areas. Possibly supported by an MSc Degree.
Further information on English language requirements for EU/Overseas applicants.
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere.