Dr. Dawei Qiu | Smart Grid | Best Researcher Award

Lecturer, University of Exeter, United Kingdom

Dr. Dawei Qiu is a distinguished scholar in smart energy systems, currently serving as a Lecturer at the University of Exeter, UK . With a strong background in electrical engineering and power systems, he specializes in AI-driven reinforcement learning, market design for low-carbon energy transition, and resilience enhancement of energy systems . His extensive research contributions in smart grids and power systems have earned him recognition in academia, with a Google Scholar citation count of 2,109, an h-index of 24, and an h10-index of 35 .

Publication Profile

Google Scholar

Education

Dr. Qiu holds a Ph.D. in Electrical Engineering from Imperial College London (2016–2020) , where he conducted pioneering research on local flexibility’s impact on electricity retailers under the supervision of Prof. Goran Strbac. Prior to this, he completed his M.Sc. in Power System Engineering from University College London (2014–2015) and obtained his B.Eng. in Electrical and Electronic Engineering from Northumbria University at Newcastle (2010–2014) . His academic journey has been shaped by esteemed mentors, including Dr. Ben Hanson and Dr. Zhiwei (David) Gao, IEEE Fellow.

Experience

Dr. Qiu’s professional career spans academia and research institutions, where he has contributed significantly to energy systems innovation . Before joining the University of Exeter in 2024, he was a Research Fellow at Imperial College London (2023–2024), specializing in market design for low-carbon energy systems. He also served as a Research Associate at the same institution from 2020 to 2023 . His work in smart grids and energy resilience has been instrumental in shaping sustainable and intelligent power infrastructure.

Awards and Honors

Dr. Qiu’s research excellence has been acknowledged through various accolades . His contributions to smart energy systems, AI-driven reinforcement learning, and low-carbon market design have positioned him as a leading researcher in the field. His studies have been published in top-tier journals, and his work has received high citations, demonstrating its impact on the global research community .

Research Focus

Dr. Qiu’s research is centered on leveraging artificial intelligence and reinforcement learning for power and energy applications . His work explores market mechanisms for cost-effective and sustainable energy transitions, as well as the resilience enhancement of energy systems in response to climate change . His expertise in AI-driven optimization and machine learning applications in energy systems makes him a key contributor to the advancement of smart grid technologies.

Conclusion

Dr. Dawei Qiu is a leading researcher in smart energy systems, with a strong academic background and impactful contributions to power systems engineering . His expertise in AI-driven market optimization, reinforcement learning, and resilient energy systems has made him a valuable asset to the research community . With his ongoing work at the University of Exeter, he continues to drive innovation in low-carbon and intelligent energy solutions .

Publications

A knowledge-based safe reinforcement learning approach for real-time automatic control in a smart energy hub – Applied Energy (Under review, 2025) Link

Enhanced Meta Reinforcement Learning for Resilient Transient Stabilization – IEEE Transactions on Power Systems (Under review, 2025) Link

Machine learning-based economic model predictive control for energy hubs with variable energy efficiencies – Energy (First round revision, 2024) Link

A Review of Resilience Enhancement Measures for Hydrogen-penetrated Multi-energy Systems – Proceedings of the IEEE (Under review, 2025) Link

Coordinated Optimal Dispatch Based on Dynamic Feasible Operation Region Aggregation – IEEE Transactions on Smart Grid (First round revision, 2024) Link

A Sequential Multi-Agent Reinforcement Learning Method for Coordinated Reconfiguration of Substation and MV Distribution Networks – IEEE Transactions on Power Systems (Under review, 2024) Link

Enhancing Microgrid Resilience through a Two-Layer Control Framework for Electric Vehicle Integration and Communication Load Management – IEEE Internet of Things Journal (Under review, 2024) Link

Coordinated Electric Vehicle Control in Microgrids Towards Multi-Service Provisions: A Transformer Learning-based Risk Management Strategy – Energy (Under review, 2024) Link

Adaptive Resilient Control Against False Data Injection Attacks for a Multi-Energy Microgrid Using Deep Reinforcement Learning – IEEE Transactions on Network Science and Engineering (Under review, 2024) Link

Assoc. Prof. Dr. Jian Sun | Smart Grid Control | Best Researcher Award

Associate Professor, Southwest University, China

Jian Sun is an Associate Professor in the School of Electronic and Information Engineering at Southwest University, Chongqing, China. With a strong academic and research background in automation and electrical engineering, his work focuses on control systems, reinforcement learning, and grid frequency regulation. Over the years, he has made significant contributions to the field through his publications and innovative approaches to tackling complex power grid challenges.

Publication Profile

ORCID

Education

Jian Sun earned his Ph.D. in Automation from Chongqing University in December 2014. He also completed a visiting Ph.D. program at the University of Wisconsin-Madison, USA, in 2014, specializing in Electrical and Computer Engineering. Prior to his doctoral studies, he obtained a Master’s degree in Automation and a Bachelor’s degree in the same field from Chongqing University.

Experience

Jian Sun has extensive academic and research experience, currently serving as an Associate Professor at Southwest University. His expertise spans areas like frequency regulation in power systems, energy storage systems, and adaptive control techniques. He has published numerous papers in prestigious journals and has contributed to several interdisciplinary research projects. His work often combines advanced reinforcement learning techniques with cyber-physical systems.

Awards and Honors

Throughout his career, Jian Sun has received recognition for his outstanding research and contributions to the field. His work has been widely cited and appreciated by both academic and industry professionals. He continues to push the boundaries of research in smart grids, energy management, and reinforcement learning.

Research Focus

Jian Sun’s research focuses on developing adaptive and resilient control strategies for smart grids, particularly in the context of frequency regulation. His work includes the integration of Vehicle-to-Grid (V2G) technologies, reinforcement learning for DoS attack resilience, and advanced control systems for energy-efficient power grids. He aims to improve the stability and security of power systems in the face of cyber threats and dynamic load conditions.

Conclusion

Jian Sun’s academic journey and research have contributed to advancements in smart grid technology, power system regulation, and control theory. His continued dedication to addressing critical challenges in energy systems positions him as a leading figure in his field. His research aims to make power systems smarter, more efficient, and resilient to emerging threats.