Imperial College London


Faculty of EngineeringDepartment of Electrical and Electronic Engineering

Research Fellow in Market Design for LowCarbon EnergySystems







Electrical EngineeringSouth Kensington Campus





Dr. Dawei Qiu is a Research Fellow 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 in 2014, the M.Sc. degree in Power System Engineering from University College London in 2015, and the Ph.D. degree in Electrical Engineering from Imperial College London in 2020. After graduation, Dr. Qiu was employed as a Research Associate at Imperial College London. Since June 2023, Dr. Qiu has been promoted to be a Research Fellow in Market Design for Low-Carbon Energy Systems at Imperial College London.

His research mainly focuses on the development and application of decentralised and market-based approaches for the 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 more than 40 peer-reviewed papers, including 20 first-author or corresponding-author journal publications in IEEE Transactions, Applied Energy, and IET, as well as 1 first-authored paper published in the world A-class AI conference IJCAI2021.

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Qiu D, Baig AM, Wang Y, et al., 2024, Market design for ancillary service provisions of inertia and frequency response via virtual power plants: A non-convex bi-level optimisation approach, Applied Energy, Vol:361, ISSN:0306-2619

Qiu D, Wang Y, Wang J, et al., 2024, Resilience-oriented coordination of networked microgrids: a shapley Q-value learning approach, Ieee Transactions on Power Systems, Vol:39, ISSN:0885-8950, Pages:3401-3416

Wang Y, Qiu D, Wang Y, et al., 2024, Graph Learning-Based Voltage Regulation in Distribution Networks With Multi-Microgrids, Ieee Transactions on Power Systems, Vol:39, ISSN:0885-8950, Pages:1881-1895

Wang Y, Qiu D, Teng F, et al., 2024, Towards microgrid resilience enhancement via mobile power sources and repair crews: a multi-agent reinforcement learning approach, Ieee Transactions on Power Systems, Vol:39, ISSN:0885-8950, Pages:1329-1345

Qiu D, Wang Y, Ding Z, et al., 2024, Graph Reinforcement Learning for Carbon-Aware Electric Vehicles in Power-Transport Networks, Ieee Transactions on Smart Grid, ISSN:1949-3053, Pages:1-1

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