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  Dr Weiqi Hua  Applications accepted all year round  Self-Funded PhD Students Only

About the Project

As the world grapples with the urgent need to mitigate climate change and achieve a net-zero carbon emissions future, understanding and reshaping energy markets is of paramount importance. This interdisciplinary research project aims to investigate and model various aspects of energy systems to facilitate the transition towards a sustainable and low-carbon economy. By addressing key challenges such as carbon trading, capacity auctions, pricing mechanisms, consumer behaviour analysis, local energy design, and peer-to-peer energy trading, this project will contribute to shaping effective policies and strategies for a net-zero future. Research Objectives: The primary objective of this project is to analyse and model energy market transitions within the context of achieving a net-zero carbon emissions target.

Through detailed investigations, the successful candidate will delve into the following areas:

1. Carbon Trading: Explore the intricacies of carbon markets, analyse their effectiveness in reducing greenhouse gas emissions, and propose improvements or alternative mechanisms.

2. Capacity Auctions: Investigate the design and impact of capacity auctions in incentivising the deployment of renewable energy sources, storage technologies, and grid flexibility measures.

3. Pricing Mechanisms: Develop novel pricing models that account for the intermittency of renewable energy sources, grid congestion, and other market conditions to achieve an efficient and sustainable energy market.

4. Consumer Behaviour Analysis: Examine consumer attitudes, preferences, and decision-making processes related to energy consumption and explore strategies to encourage sustainable energy choices.

5. Local Energy Design: Explore the potential of localised energy systems, including microgrids, energy communities, and district heating/cooling, and develop models to optimise their integration into the wider energy network.

6. Peer-to-Peer Energy Trading: Investigate the feasibility and impact of peer-to-peer energy trading platforms, analyse their benefits, challenges, and potential for fostering local energy resilience.

Requirements: 1. Applications are open to students that have a 1st class degree (or equivalent) or a master degree in a wide variety of scientific disciplines including Electrical Engineering, Environmental Engineering, Control Engineering, and Computer Science. 2. Applicants whose first language is not English will be required to demonstrate proficiency in the English language (IELTS 6.5 or equivalent). 3. Good programming skill, e.g., Python, Matlab, or Java, etc, and academic writting skill are necessary for this project. 

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 Address Removed]

Business & Management (5) Economics (10) Engineering (12)

Funding Notes

This is a self-funded post. However, applicants who are willing to apply findings 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.

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