Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Associate







Electrical EngineeringSouth Kensington Campus





Dr Dawei Qiu is a Research Associate in the Department of Electrical and Electronic Engineering, Imperial College London.

Dr Qiu received the B.Eng. degree in Electrical and Electronic Engineering from Northumbria University, Newcastle upon Tyne, UK in 2014, the M.Sc. degree in Power System Engineering from University College London, London, UK, in 2015, and the Ph.D. degree in Electrical Engineering from Imperial College London, London, UK in 2020.

His research mainly focuses on the development and application of decentralized and market-based approaches for electricity market, peer-to-peer energy trading, multi-energy system integration, and microgrid resilience control. In particular, he has a strong background in game theoretic modelling and reinforcement learning approaches. He has published nearly 20 peer-reviewed papers, including 8 first-author/corresponding-author journal publications in Applied Energy, IEEE Transactions, and IET, as well as 1 first-authored paper published in the world A-class AI conference IJCAI2021.

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Qiu D, Xue J, Zhang T, et al., 2023, Federated reinforcement learning for smart building joint peer-to-peer energy and carbon allowance trading, Applied Energy, Vol:333, ISSN:0306-2619

Qiu D, Wang Y, Zhang T, et al., 2022, Hybrid Multiagent Reinforcement Learning for Electric Vehicle Resilience Control Towards a Low-Carbon Transition, Ieee Transactions on Industrial Informatics, Vol:18, ISSN:1551-3203, Pages:8258-8269

Zeng L, Qiu D, Sun M, 2022, Resilience enhancement of multi-agent reinforcement learning-based demand response against adversarial attacks, Applied Energy, Vol:324, ISSN:0306-2619

Bellizio F, Xu W, Qiu D, et al., 2022, Transition to Digitalized Paradigms for Security Control and Decentralized Electricity Market, Proceedings of the Ieee, ISSN:0018-9219

Wang Y, Qiu D, Strbac G, 2022, Multi-agent deep reinforcement learning for resilience-driven routing and scheduling of mobile energy storage systems, Applied Energy, Vol:310, ISSN:0306-2619

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