Energy System Digitalisation

1. Project Overview
Emerging smart grid technologies including artificial intelligence, distributed ledger, and data analytics drive the decarbonisation of energy systems with the least costs. The digitalisation of modern power grids enables large volumes of data to be exploited for assisting accurate prediction, strategic planning, smart control, and massive trading in energy systems.

Prospective research topics include, but are not limited to: i) Energy system long-term planning and short-term operational control; ii) Analysis of decision making and interaction among stakeholders in energy systems, e.g., policy makers, generators, and consumers through using optimisation, game theory, or agent-based modelling; iii) Energy data analytics through using machine learning; iv) Energy policy, economics and market design.

2. Candidate Profile

A master’s degree (or equivalent) in engineering, computer science, applied mathematics, or a related discipline. Strong programming skills (e.g., Python, MATLAB, or similar). Knowledge of energy systems, thermodynamics, or building services engineering is desirable. Ability to work independently and engage with academic and industrial stakeholders.

3. How to apply:

Those interested should send a CV, personal statement (outlining how their relevant experience would make them a strong candidate for the project), transcripts, and contact details of two referees to [email protected].

Funding notes:
This is a self-funded post. However, applicants who are willing to apply fundings by themselves will be supported, e.g., government funding or industry funding.

References:

• Weiqi Hua, Bruce Stephen, David C.H. Wallom, "Digital twin based reinforcement learning for extracting network structures and load patterns in planning and operation of distribution systems," Applied Energy.
• Weiqi Hua, Ying Chen, Meysam Qadrdan, Jing Jiang, Hongjian Sun and Jianzhong Wu, "Applications of Blockchain and Artificial Intelligence to Enable Prosumers in Smart Grids: A Review," Renewable & Sustainable Energy Reviews, Volume 161, 2022, Article Number: 112308.
• Weiqi Hua, Yue Zhou, Meysam Qadrdan, Jianzhong Wu and Nick Jenkins, "Blockchain Enabled Decentralized Local Electricity Markets with Flexibility from Heat Sources," IEEE Transactions on Smart Grid, Volume 14, No. 2, pp. 1607-1620, March 2023.
• Weiqi Hua, Jing Jiang, Hongjian Sun, Andrea M. Tonello, Meysam Qadrdan and Jianzhong Wu, "Data-Driven Prosumer-Centric Energy Scheduling using Convolutional Neural Networks," Applied Energy, Volume 308, 2022, Article Number: 118361.
• Weiqi Hua, Jing Jiang, Hongjian Sun, Fei Teng and Goran Strbac, "Consumer-Centric Decarbonization Framework using Stackelberg Game and Blockchain," Applied Energy, Volume 309, 2022, Article Number: 118384.
• Yue Zhou, Andrei Manea, Weiqi Hua, Jianzhong Wu, Wei Zhou, James Yu and Saifur Rahman, "Application of Distributed Ledger Technology in Distribution Networks, " Proceedings of the IEEE, Volume 110, No. 12, pp. 1963-1975, Dec. 2022.
• Dawei Qiu, Yi Wang, Weiqi Hua, and Goran Strbac, "Reinforcement learning for electric vehicle applications in power systems: a critical review. " Renewable & Sustainable Energy Reviews, 173, 2023, Article Number: 113052.