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Offer: PhD Studentship - Holistic Analysis of Flexibility in Integrated Low Carbon Energy Systems:Job description:Qualification Type: PhD The Institute for Energy Systems at the University of Edinburgh invites applications for are invited for a PhD studentship in ?Holistic Analysis of Flexibility in Integrated Low Carbon Energy Systems?. With this studentship, you will get the opportunity to advance your study in the field of renewable-base future energy system and contribute to the next major leap towards net zero. The student will be supervised by Dr Wei Sun and in collaboration with Prof Gareth Harrison. The studentship will start as soon as possible from October 2022. As the pace of decarbonization accelerates towards net zero, the electricity, gas, road transport and heating sectors are becoming more closely integrated. While significant progress has been made on the electrification of the heat and transportation, the non-dispatchable nature of most renewable electricity generation poses a significant challenge for further decarbonization. For example, a further increase in non-dispatchable generation will lead to increasing times with surplus renewable electricity so that the assets aren't optimally utilized. This issue can be addressed by employing flexibility technologies to support supply and demand balancing. There are already significant amount of energy storage and demand response potentials in electricity, heat and road transport systems, which can be obtained through the optimal operation of ?Power-to-X' systems (for example, using surplus renewable to hydrogen generation as Power-to-Hydrogen, or similarly Power-to-Mobility, Power-to-Heat, etc.). Therefore, it is critical to consider these sectors holistically as an integrated system in order to exploit flexibility and synergies for rapid, cost-effective decarbonization. However, the coupling of electricity with other sectors presents many complex interconnected challenges. Therefore, there is an urgent need for new developments in the research area. This cross-disciplinary PhD program will use a holistic approach to develop novel co-simulation methods and optimisation tools for modelling the multi-vector energy systems integration at multiple scales (local, distribution and transmission), and under multiple uncertainties, in order to maximize the system flexibility in achieving net zero. Advanced optimization techniques and data analytics will be explored. The developed models will be applied to evaluate the impact of Power-To-X, in increasing the use of renewables at different time scales, and help realize the full potential of broadly coupling electricity with other energy sectors. Please note that this position may be closed early if a appropriate candidate is identified; Early application 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:www.ed.ac.uk/equality-diversity Interested candidates are welcome to contact Dr. Wei SUN by email for pre-application enquiries (w.sun@ed.ac.uk,www.eng.ed.ac.uk/about/people/dr-wei-sun). Applications should be submitted via the University's online system. Principal manager:Dr. Wei Sun adjunct manager:Prof. Gareth Harrison Eligibility: To apply for this position, candidates should have (or expect) a Distinction/First class Master degree (or international equivalent) in Electrical Engineering, Computing, Applied Mathematics, or related areas. Strong expertise in one or more of the following areas is highly desirable: » Power network modelling and control » Optimization, control theory and/or data science. » Strong programming expertise using languages such as Python, MATLAB or Julia » significant industry or research practice Funding: Applications are welcomed from self-funded students, or students who are applying for scholarships from the University of Edinburgh or elsewhere. Tuition fees + stipend are available for Home/EU and International students more information and other funding options. Skills:
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