Energy Systems

This PhD project delves into the dynamics of residential energy consumption, system flexibility, and employs the systems transition engineering processes (STEPs) to tackle energy poverty with novel utility network-to-end-use flexibility opportunities. The research is framed around the critical need to create resilient urban energy systems that not only adapt to fast-paced technological and environmental changes but also promote energy equity and efficiency.

In urban environments, residential areas are key consumers of energy and greatly influence the overall dynamics of urban energy flow. The primary aim of this research is to innovate, model and optimise the intake and distribution of energy in residential sectors and examine how these modifications can alleviate energy poverty, characterised by lack of access to reliable and affordable energy services. This involves understanding the specific energy needs of underserved populations and integrating solutions that ensure equitable energy distribution.

Transition engineering principles guide the project's approach, integrating systems thinking, predictive modelling, and simulation techniques to explore novel and practical engineering adjustments for improving system flexibility and reliability amid increasing green energy integration and fluctuating demand. Expertise will be gained in grid and network technology and commercial operations, and energy end uses—from heating and lighting to appliances and electronic devices. The project will assess initiatives like participatory demand-response technologies, energy-efficient retrofitting, integrated storage, and community energy systems.

Moving beyond technical analysis, the study will incorporate socioeconomic data to paint a more accurate picture of energy consumption patterns and barriers to energy access in various residential demographics. Simulation tools will evaluate how different interventions might impact energy affordability and reliability at the household level and their wider effects on the energy system's flexibility and sustainability.

Policy implications will also be a significant focus of this research. By identifying regulatory and institutional barriers to equitable energy distribution and system flexibility, the project aims to suggest robust policy measures that can support broad adoption of efficient and equitable energy solutions.

The expected contribution of this PhD project is pioneering energy transition shifts for adaptable, forward-thinking strategies that enhance energy system infrastructure in urban areas, ensuring that they are not only sustainable and flexible but also fair and responsive to the needs of all community members. The PhD candidate will have a Mechanical or Electric Power Engineering qualification, utility industry or energy systems engineering experience, aptitude for modelling, and passion for energy systems transition engineering. Candidates who are systems thinkers are preferred.

This PhD project is advertised as a part of the Edinburgh Research Partnership in Engineering, a joint partnership between the University of Edinburgh and Heriot-Watt University. The successful candidate will be supervised by a team consisting of academics from the University of Edinburgh and Heriot-Watt University.

Minimum entry qualification - an Honours degree at 2:1 or above (or International equivalent) in a relevant science or engineering discipline, possibly supported by an MSc Degree. Further information on English language requirements for EU/Overseas applicants.

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:

• Energy system modelling and optimisation
• Experience in energy systems transition engineering
• Data analysis, optimisation and/or machine learning
• Experience in energy system modelling

Applications are welcomed from self-funded students, or students who are applying for  scholarships from the University of Edinburgh or elsewhere

Further information and other funding options.

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.

Further information and other funding options.

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.

Further information and other funding options.

PhD in Electrical Engineering, Pontificia Universidad Catolica de Chile and University of Edinburgh, 2023.

Título de Ingeniero Civil Electricista (Electrical Engineer), Pontificia Universidad Catolica de Chile, 2016.

• Member of the Institute of Electrical and Electronic Engineers (IEEE)
• Member of the Institution of Engineering and Technology (IET)
• Next Generation Network (NGN) Member of CIGRE 

PhD in Electrical Engineering, Pontificia Universidad Catolica de Chile and University of Edinburgh, 2023.

Título de Ingeniero Civil Electricista (Electrical Engineer), Pontificia Universidad Catolica de Chile, 2016.

• Member of the Institute of Electrical and Electronic Engineers (IEEE)
• Member of the Institution of Engineering and Technology (IET)
• Next Generation Network (NGN) Member of CIGRE