Energy Systems
This PhD project aims to design heat integration strategies within multi-vector energy systems to enhance overall system flexibility and efficiency.
The route to net zero faces two main challenges: first, the increasing integration of non-dispatchable and variable renewable energy resources, such as wind and solar power, creates significant challenges for energy systems, notably in terms of maintaining reliability and balancing supply with demand; and, second, there is almost no progress and not even a credible roadmap for heat decarbonisation (low temperature space heating as well as high temperature industrial heat). By focusing on the thermal aspects of energy systems, and particularly on strategies for efficient heat integration, this research aims to provide novel solutions that enhance system stability and provide affordable and sustainable heat.
The project will investigate heat integration techniques across various levels of the energy system, including industrial processes, district heating networks, and residential heating solutions. Key areas of focus will include the integration of advanced thermal storage technologies, the utilisation of waste heat recovery, and the implementation of innovative heat pump technologies. This multi-scale approach ensures that the project addresses both high-grade industrial heat and low-grade residential heat requirements.
A significant component of the research will involve the development of mathematical models and simulation tools to evaluate potential heat integration scenarios. The models and tools will be built on existing open-source tools in the Institute for Energy Systems, commercials tools such as TRNSYS and open-source tools such as PyPSA. These tools will help in identifying optimal ways to deploy thermal energy storage and recovery, thus enabling better management of renewable generation variability. The methodologies developed will consider not only energy efficiency but also economic and environmental impacts, ensuring that the solutions are sustainable both technically and financially.
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.
Overall, this PhD project offers a comprehensive approach to enhancing system flexibility through heat integration, addressing critical challenges in the transition to a more sustainable and reliable energy future.
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
- Data analysis, optimisation and/or machine learning
- Experience in thermal energy system modelling
Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere
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
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.