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Data Centre Modelling and Energy System Integration

  • DeadlineDeadline: 30/08/2025
  • West Midlands, All EnglandWest Midlands, All England

Description

1. Project Overview

Data centres are now critical infrastructure for digital services and artificial intelligence applications. Their rapid expansion creates substantial energy demand, leading to challenges in electricity system stability, decarbonisation, and integration with local energy resources. At the same time, data centres can offer opportunities to provide flexibility, utilise waste heat, and support renewable integration.

This PhD project will investigate the modelling and optimisation of data centre operations within the wider energy system. The research will develop methods to characterise data centre load profiles, cooling and heat recovery systems, and interactions with electricity, heating, and cooling networks. It will also assess how data centres can participate in demand response markets, integrate with renewable energy, and contribute to local decarbonisation targets.

2. Research Objectives

The project aims to:

i. Develop high-resolution models of data centre energy demand, including IT loads, cooling systems, and backup generation.

ii. Explore integration strategies with local and national energy systems, including electricity, district heating, and hydrogen networks.

iii. Evaluate operational strategies for cost reduction, carbon mitigation, and resilience enhancement.

iv. Investigate business models and policy frameworks that enable data centres to support energy transition goals.

 

The project will be supervised by Dr Weiqi Hua ([email protected]).

 


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.

Entry Requirements

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.

Fees

 

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.

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].

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