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Towards Optimal Digitalisation in Smart Local Energy Systems (RDF23/MPEE/JIANG)


   Faculty of Engineering and Environment

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  Dr Jing Jiang, Dr Zhiwei Gao  No more applications being accepted  Competition Funded PhD Project (Students Worldwide)

About the Project

Due to global environmental concerns, the UK has set an ambitious target of net-zero carbon emissions by 2050. To achieve this target, future power systems will be rapidly transformed in the next couple of decades to: i) integrate more renewable energy sources and connect more distributed energy resources in smart local energy systems to meet local energy demand and deliver flexibility, and ii) deploy more digital technologies for monitoring system status, coordinating operations and avoiding catastrophic failures. However, such a transition faces significant challenges, particularly how to effectively manage large amounts of renewable and distributed energy resources given bidirectional power flows, complicated digitalisation infrastructure and the ever-increasing dependence on cyber systems for collecting data and coordinating operations. Efficient operations will require data intensive techniques and optimisation methods to enable the dynamic balance of power supply and demand.

This project aims at addressing challenges for future smart local energy systems and will develop practical planning and optimization methods considering realistic cyber network limitations. Data collected from measurement/metering devices will be used to develop digital twins, building on which optimization methods will be developed to minimize the operational cost or carbon emissions. Data-driven digital twins can be integrated with artificial intelligence (AI), where data-intensive AI tools will be developed to enable more efficient decision-making and planning. This project will also exploit the interrelation of physical energy systems and cyber systems, and address the challenges caused by the high dependence on cyber systems. 

We are recruiting one PhD student to contribute to the project. The candidates are expected to have solid knowledge in Energy, Electrical/Electronic Engineering, AI & Machine Learning, demonstrably experiences of programming/simulations, strong analytic skills, and excellent communication skills, both written and oral, in English.

The PhD student will also be involved in international collaborations with world-leading academics and industries through the project TESTBED2 under H2020 (https://www.testbed2.org, 2020-2025). TESTBED2 supports research secondments to its industry and international partners in the EU and US, which will support the student in gaining useful transferable skills and fostering career development.

Academic Enquiries

This project is supervised by Dr Jing Jiang. For informal queries, please contact [Email Address Removed] . For all other enquiries relating to eligibility or application process please use the email form below to contact Admissions. 

Funding Information

Home and International students (inc. EU) are welcome to apply. The studentship is available to Home and International (including EU) students and includes a full stipend at UKRI rates (for 2022/23 full-time study this is £17,668 per year) and full tuition fees. Studentships are also available for applicants who wish to study on a part-time basis over 5 years (0.6 FTE, stipend £10,600 per year and full tuition fees) in combination with work or personal responsibilities).  

Please also see further advice below of additional costs that may apply to international applicants.

Eligibility Requirements:

  • Academic excellence of the proposed student i.e. 2:1 (or equivalent GPA from non-UK universities [preference for 1st class honours]); or a Masters (preference for Merit or above); or APEL evidence of substantial practitioner achievement.
  • Appropriate IELTS score, if required.
  • Applicants cannot apply for this funding if they are already a PhD holder or if currently engaged in Doctoral study at Northumbria or elsewhere.

Please note: to be classed as a Home student, candidates must meet the following criteria:

  • Be a UK National (meeting residency requirements), or
  • have settled status, or
  • have pre-settled status (meeting residency requirements), or
  • have indefinite leave to remain or enter.

If a candidate does not meet the criteria above, they would be classed as an International student.  Applicants will need to be in the UK and fully enrolled before stipend payments can commence, and be aware of the following additional costs that may be incurred, as these are not covered by the studentship.

  • Immigration Health Surcharge https://www.gov.uk/healthcare-immigration-application
  • If you need to apply for a Student Visa to enter the UK, please refer to the information on https://www.gov.uk/student-visa. It is important that you read this information very carefully as it is your responsibility to ensure that you hold the correct funds required for your visa application otherwise your visa may be refused.
  • Check what COVID-19 tests you need to take and the quarantine rules for travel to England https://www.gov.uk/guidance/travel-to-england-from-another-country-during-coronavirus-covid-19
  • Costs associated with English Language requirements which may be required for students not having completed a first degree in English, will not be borne by the university. Please see individual adverts for further details of the English Language requirements for the university you are applying to.

How to Apply

For further details of how to apply, entry requirements and the application form, see

https://www.northumbria.ac.uk/research/postgraduate-research-degrees/how-to-apply/   

For applications to be considered for interview, please include a research proposal of approximately 1,000 words and the advert reference (e.g. RDF23/…).

Deadline for applications: 27 January 2023

Start date of course: 1 October 2023 tbc

Northumbria University is committed to creating an inclusive culture where we take pride in, and value, the diversity of our doctoral students. We encourage and welcome applications from all members of the community. The University holds a bronze Athena Swan award in recognition of our commitment to advancing gender equality, we are a Disability Confident Employer, a member of the Race Equality Charter and are participating in the Stonewall Diversity Champion Programme. We also hold the HR Excellence in Research award for implementing the concordat supporting the career Development of Researchers.


References

1. W. Hua, Y. Chen, M. Qadrdan, J. Jiang, H. Sun and J. Wu, “Applications of blockchain and artificial intelligence technologies for enabling prosumers in smart grids: A review”, in Renewable and Sustainable Energy Reviews, vol. 161, 112308, June 2022.
2. W. Hua, J. Jiang, H. Sun, A. Tonello, M. Qadrdan and J. Wu, “Data-driven prosumer-centric energy scheduling using convolutional neural networks”, in Applied Energy, 308, 118361, 15 Feb 2022.
3. W. Hua, J. Jiang, H. Sun, F. Teng and G. Strbac, “Consumer-centric decarbonization framework using Stackelberg game and Blockchain”, in Applied Energy, 309, 118384, 1 Mar 2022.
4. M. You, Q. Wang, H. Sun, I. Castro and J. Jiang, “Digital twins based day-ahead integrated energy system scheduling under load and renewable energy uncertainties”, in Applied Energy, 305, 117899, 1 Jan 2022.
5. M. J. Thompson, H. Sun and J. Jiang, "Blockchain-based peer-to-peer energy trading method," in CSEE Journal of Power and Energy Systems, vol. 8, no. 5, pp.1318-1326, Sep 2022.
6. W. Hua, J. Jiang, H. Sun and J. Wu, “A blockchain based peer-to-peer trading framework integrating energy and carbon markets”, in Applied Energy, 279, 115539, Dec 2020.
7. A. Alnasser, H. Sun and J. Jiang, "Recommendation-Based Trust Model for Vehicle-to-Everything (V2X)," in IEEE Internet of Things Journal, vol. 7, no. 1, pp. 440-450, Jan 2020.
8. M. You, X. Zhang, G. Zheng, J. Jiang and H. Sun, "A Versatile Software Defined Smart Grid Testbed: Artificial Intelligence Enhanced Real-Time Co-Evaluation of ICT Systems and Power Systems," in IEEE Access, vol. 8, pp. 88651-88663, 2020.

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