RA and PhD in multi-vector energy sytem integration

We are currently looking for two highly motivated researchers (one RA and one PhD) for positions in the INTEGRATE project (Integrating seasonal Thermal storage with multiple energy sources to decarbonise Thermal Energy). The RA position closes on the 07.08.2020 and the PhD will remain open until filled.

1. Research Associate in Whole Energy System Modelling
The researcher for the RA position will develop and implement a multi-vector energy system model for the design of Smart Thermal Grids (STG) with Seasonal Thermal Energy Storage (STES) as part of the wider energy system. Building on available electricity dispatch and network models, and thermal demand and distribution models you will develop a multi-vector, multiple stakeholder simulation and optimisation model for the integrated design of STG with STES that considers the interplay and coordination between energy supply and demand, seasonal thermal storage characteristics, and regulation and market frameworks. You will collaborate with engineering and social science colleagues on the EPSRC funded INTEGRATE project to combine the results from the individual work packages to develop representative case studies and guidelines for urban, suburban and campus thermal energy systems based around the smart integration of STES systems. This position is in the School of Engineering and is supervised by Daniel Friedrich and Gareth Harrison.

2. PhD in Machine learning methods to manage the integration of heating systems into the wider energy system
The researcher for the PhD position will develop, implement and apply machine learning methods for the design and optimisation of district heating system with STES as part of the wider energy system. While the main focus is on using machine learning based surrogate models to link detailed CFD simulations with whole system models, there is scope to investigate other areas such as system control and demand/supply predictions. The candidate will develop a wide range of skills in heating systems with STES design and machine learning methods which will be widely applicable to the candidate’s future career. The project is linked to the EPSRC funded INTEGRATE project and the PhD student will be jointly supervised by Dr Daniel Friedrich at the School of Engineering at the University of Edinburgh and Prof Ben Hughes at the University of Hull.

About the INTEGRATE project
The INTEGRATE project considers Seasonal Thermal Energy Storage (STES) systems as a vital part of a future zero carbon energy system and will evaluate the interplay between regulation and market frameworks, heating/cooling demands, energy storage systems and different energy sources. We will design integrated STES systems that provide affordable, flexible and reliable thermal energy for the customers while also providing flexibility services for the wider energy system. The project is a collaboration between the Universities of Edinburgh, Glasgow and Hull. The successful candidates will work alongside a multi-disciplinary team to combine efforts and provide solutions towards the decarbonisation of heating and cooling. The project is supported by nine industrial partners, which cover every aspect of the proposed work, and two leading, international research institutions which are at the forefront of renewable heating research.

Links:
https://blogs.ed.ac.uk/integrate/
https://www.vacancies.ed.ac.uk/pls/corehrrecruit/erq_jobspec_version_4.jobspec?p_id=052516
https://www.eng.ed.ac.uk/studying/postgraduate/research/phd/machine-learning-methods-manage-integration-heating-systems-wider