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Data Driven Optimal Pricing and Operation of Low Carbon Integrated Energy Systems with Enhanced Privacy Preservation

School of Electronics, Electrical Engineering and Computer Science | PHD

Applications are now CLOSED
Funding
Funded
Reference Number
EEECS/2025/QW3
Application Deadline
28 February 2025
Start Date
1 October 2025

Overview

Integration of different energy sectors such as the power system, heating sector and gas systems can improve the utilization of renewable energy production and energy efficiency, reduce cost of energy supply, and provide enhanced flexibility to ensure secure operation of the low carbon energy system. The optimal operation of the integrated energy system (IES) has been extensively studied. However, it is challenging to determine the optimal pricing and operation strategies due to the interests of different stakeholders, complexity of the optimization problems, privacy protection, and computational burdens.

This project is to develop data driven optimal pricing and operation strategies for the low carbon IES. The optimal energy and carbon pricing will be studied in order to maximize the social welfare and economical benefits of all stakeholders.

The PhD student is expected to develop algorithms for optimal energy and carbon pricing, and operation of the IES. These algorithms will improve the economic benefits for the whole energy system and stakeholders, the computation efficiency, and privacy protection. The tasks will be focused on optimal pricing strategy using Game theory and data driven solution methods such as learn to optimize and model free deep reinforcement learning (DRL). A comparative analysis will evaluate the merits of the proposed optimal pricing and operation strategies. Case studies will be conducted using MATLAB/Python to assess system performance under various scenarios. The detailed tasks of the proposed research include:

1) To develop optimal energy and carbon pricing strategies for the IES with electricity, heat and gas sectors using Game theory.
2) To develop optimal operation strategies for the IES with electricity, heat and gas sectors using data driven methods such as learn to optimize and leaning based methods.
3) To conduct a comparative analysis of the proposed optimal pricing and operation strategies against conventional ones.
5) To validate the performance of the proposed pricing and operation strategies through simulation and demonstrate its feasibility for real-world applications.

Funding Information

To be eligible for consideration for a Home DfE or EPSRC Studentship (covering tuition fees and maintenance stipend of approx. £19,237 per annum), a candidate must satisfy all the eligibility criteria based on nationality, residency and academic qualifications.

To be classed as a Home student, candidates must meet the following criteria and the associated residency requirements:

• Be a UK National,
or • Have settled status,
or • Have pre-settled status,
or • Have indefinite leave to remain or enter the UK.

Candidates from ROI may also qualify for Home student funding.

Previous PhD study MAY make you ineligible to be considered for funding.

Please note that other terms and conditions also apply.

Please note that any available PhD studentships will be allocated on a competitive basis across a number of projects currently being advertised by the School.

A small number of international awards will be available for allocation across the School. An international award is not guaranteed to be available for this project, and competition across the School for these awards will be highly competitive.

Academic Requirements:
The minimum academic requirement for admission is normally an Upper Second Class Honours degree from a UK or ROI Higher Education provider in a relevant discipline, or an equivalent qualification acceptable to the University.

Project Summary
Supervisor

Qiuwei Wu

More Information

qiuwei.wu@qub.ac.uk

Research Profile


Mode of Study

Full-time: 3 or 3.5 years


Funding Body
Funding TBC
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