Dr. Caixin Yan | Power System Optimization | Best Researcher Award

Dr. Caixin Yan | Power System Optimization | Best Researcher Award

PhD Student, Central South University, China

Dr. Caixin Yan is a distinguished researcher at the National Engineering Research Centre of Advanced Energy Storage Materials in Changsha, China. With a deep passion for energy systems and artificial intelligence applications in power grids, Dr. Yan has contributed significantly to the field of energy optimization and power market strategies. His expertise in reinforcement learning and grid stability has made him a prominent figure in the domain of advanced energy storage and smart grid technologies.

Publication Profile

ORCID

🎓 Education:

Dr. Yan pursued his higher education in automation and electrical engineering, focusing on intelligent power grid management and optimization. His academic journey has equipped him with extensive knowledge in multi-energy systems, deep reinforcement learning, and industrial load flexibility.

💼 Experience:

Currently associated with the National Engineering Research Centre of Advanced Energy Storage Materials, Dr. Yan has also collaborated with institutions like the School of Automation at Central South University and the Hunan Xiangjiang Artificial Intelligence Academy. His research focuses on optimizing power systems through artificial intelligence and developing cutting-edge solutions for market-based power regulation.

🏆 Awards and Honors:

While specific awards and honors are not listed, Dr. Yan’s impactful contributions to energy storage, power market strategies, and reinforcement learning applications have been recognized through his publications and collaborations. His research is gaining traction, as evidenced by his growing citation count.

🔍 Research Focus:

Dr. Yan’s research revolves around power grid optimization, energy storage integration, and AI-driven solutions for smart grids. His work on hierarchical reinforcement learning for power grid topology regulation and multi-energy systems operation strategies has been instrumental in advancing the field of intelligent energy management.

🔚 Conclusion:

Dr. Caixin Yan is a rising expert in energy storage and AI-driven power grid optimization. His contributions to power market strategies, reinforcement learning applications, and energy system integration are paving the way for a smarter and more efficient electricity landscape. With growing recognition and impactful research, he continues to make significant strides in the field of intelligent energy solutions. 🚀

📚 Publications :

Review of Power Market Optimization Strategies Based on Industrial Load Flexibility – Analyzing the role of industrial flexibility in power markets.

Power Grid Topology Regulation Method Based on Hierarchical Reinforcement Learning – Exploring AI-driven strategies for grid topology adjustments.

Deep Reinforcement Learning for Strategic Bidding in Incomplete Information Market – Applying AI to strategic bidding in uncertain energy markets.

Optimal Operation Strategies of Multi-Energy Systems Integrated with Liquid Air Energy Storage Using Information Gap Decision Theory – Investigating operational strategies for multi-energy systems.

Load Frequency Control of Photovoltaic Generation-Integrated Multi-Area Interconnected Power Systems Based on Double Equivalent-Input-Disturbance Controllers – Developing control mechanisms for PV-integrated power systems.

Jian Sun | Smart Grid Control | Best Researcher Award

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. 🌐🔋

Publications 

Load Forecasting for Commercial Buildings Using BiLSTM–Transformer Network and Cyber–Physical Cognitive Control Systems
Published Year: 2024
Journal: Symmetry
Cited by: Crossref

An Adaptive V2G Capacity-Based Frequency Regulation Scheme With Integral Reinforcement Learning Against DoS Attacks
Published Year: 2024
Journal: IEEE Transactions on Smart Grid
Cited by: Crossref

Cooperative Grid Frequency Control Under Asymmetric V2G Capacity via Switched Integral Reinforcement Learning
Published Year: 2024
Journal: International Journal of Electrical Power & Energy Systems
Cited by: Crossref

Resilient Frequency Regulation for DoS Attack Intensity Adaptation via Predictive Reinforcement V2G Control Learning
Published Year: 2024
Journal: IEEE Transactions on Smart Grid
Cited by: Crossref

Safe Online Integral Reinforcement Learning for Control Systems via Controller Decomposition
Published Year: 2023
Journal: Arabian Journal for Science and Engineering
Cited by: Crossref

A DoS Attack-Resilient Grid Frequency Regulation Scheme via Adaptive V2G Capacity-Based Integral Sliding Mode Control
Published Year: 2023
Journal: IEEE Transactions on Smart Grid
Cited by: Crossref

A DoS Attack Intensity-Aware Adaptive Critic Design of Frequency Regulation for EV-Integrated Power Grids
Published Year: 2023
Journal: International Journal of Electrical Power & Energy Systems
Cited by: Crossref

Structural Scheduling of Transient Control Under Energy Storage Systems by Sparse-Promoting Reinforcement Learning
Published Year: 2022
Journal: IEEE Transactions on Industrial Informatics
Cited by: Crossref

A Sparse Neural Network-Based Control Structure Optimization Game under DoS Attacks for DES Frequency Regulation of Power Grid
Published Year: 2019
Journal: Applied Sciences
Cited by: Crossref

A Stable Distributed Neural Controller for Physically Coupled Networked Discrete-Time System via Online Reinforcement Learning
Published Year: 2018
Journal: Complexity
Cited by: Crossref

Frequency Regulation of Power Systems with Self-Triggered Control under the Consideration of Communication Costs
Published Year: 2017
Journal: Applied Sciences
Cited by: Crossref

 

Sisil Kumarawadu | Energy systems applications | Excellence in Innovation

Prof. Sisil Kumarawadu | Energy systems applications | Excellence in Innovation

Senior Professor, University of Moratuwa, Sri Lanka

Sisil Kumarawadu, Ph.D., is a Senior Professor in Electrical Engineering at the University of Moratuwa, Sri Lanka, and a distinguished professor at Shanghai University of Electric Power. With expertise in smart energy-efficient systems, systems automation, and AI applications, he has made significant contributions to robotics, intelligent systems, and applied statistics. His leadership extends to his past roles as the Head of the Department of Electrical Engineering and Chairman of the Board of Governors at the Arthur C. Clarke Institute for Modern Technologies. Dr. Kumarawadu’s career includes research, teaching, and mentorship, further enhancing his stature as a leading academic and innovator in electrical engineering. 📚💡🌍

Publication Profile

Scopus

Education:

Dr. Kumarawadu holds a Ph.D. in Robotics and Intelligent Systems from Saga National University, Japan (2003), a Master’s degree in Advanced Systems Control Engineering from the same institution (2000), and a First Class Honours BSc in Electrical Engineering from the University of Moratuwa, Sri Lanka (1996). 🎓📘

Experience:

Dr. Kumarawadu has over two decades of experience in academia, having served as a Senior Professor, Professor, and Associate Professor in Electrical Engineering at the University of Moratuwa. He has held notable positions including the Exetel Endowed Professor in Artificial Intelligence and Postdoctoral Research Fellow at the National Central University, Taiwan. He has delivered keynote speeches at major international conferences and has contributed to several research projects in automation, energy systems, and AI. 🏫🌍📈

Research Interests:

Dr. Kumarawadu’s research interests include smart energy-efficient systems, systems automation and control, AI applications, and applied statistics. He is particularly focused on innovations in robotics, intelligent transportation systems, and sustainable energy solutions, contributing to various cutting-edge advancements in these fields. ⚡🤖🔬

Awards:

Dr. Kumarawadu has received numerous prestigious awards, including the Sri Lanka Education Leadership Award (2019), the National Research Council Merit Award for Scientific Publications (2010), and the Presidential Award for Scientific Research Publications (2007, 2008). He has also been honored as an Overseas Distinguished Professor by Shanghai University of Electric Power and recognized in the Marquis Who’s Who in the World (25th Edition). 🏆🥇🎖️

Publications:

“NILM for Commercial Buildings: Deep Neural Networks Tackling Non-Linear and Multi-Phase Loads,” Energies (Section F: Electrical Engineering), Vol. 17, Issue 15, August 2024.

“A Biphasic Machine Learning Approach for Detecting Electricity Theft Cyberattacks in Smart Grids,” IEEE Trans. Smart Grids (under review).
Link to publication

“Dijkstra Method Based Zone Temperature Management Strategy for Optimal Energy Saving with Guaranteed Thermal Comfort,” The International Journal of Building Science and its Applications (under review).
Link to publication

“Review on Li-Ion Battery Parameter Extraction Methods,” IEEE Access, Vol. 11, 2023.
Link to publication

“Deep Learning-based Non-Intrusive Load Monitoring for a Three-Phase System,” IEEE Access, Vol. 11, 2023.
Link to publication