Invite Speakers

Xing He
Shanghai Jiao Tong University, China

Title: Machine Intelligence for Complex Systems: Virtual Simulation and Machine Learning
Abstract: Complex systems like power grids and utility corridors face challenges in data integration, predictive maintenance, and Distributed Energy Resources (DER) scheduling, especially in the context of multi-stakeholder distribution network management. Current methods often struggle to incorporate multiphysics data for data-driven situational awareness, simulation, and autonomous decision-making, and to predict future scenarios effectively.
Our work leverages domain expertise, data science, AI, and systems engineering to drive digital transformation. The approach includes dynamic modeling for future-state prediction, multisource data integration for enhanced system insights, knowledge-data driven human-machine collaboration, and autonomous decision-making, all aimed at advancing smart and adaptive infrastructure management across complex systems.

Bio: Dr. Xing He is an Associate Researcher at Shanghai Jiao Tong University and an IEEE Senior Member. His research focuses on big data analysis in power systems, energy digital twins, and metaverse systems. He introduced Random Matrix Theory to power system studies, leading to high-impact publications. He has developed frameworks for multi-source heterogeneous data mining and built digital twin systems for the energy internet, advancing fields such as load forecasting, fault diagnosis, and optimization. Dr. He has led two National Natural Science Foundation projects and participated in eight national projects, including key sub-tasks. He has authored over 100 papers and one book on energy digital twins.

Jianwei Zhang
Inner Mongolia University of Technology, China

Title: Improved Model Predictive Control for Power Electronics Converters in Renewable Power Generation and Energy Storage Systems
Abstract: With the rapid development of power electronics technology, power electronic converters have played increasingly pivotal roles in various fields such as renewable energy generation, energy storage systems including electric vehicles, and industrial drives. Model Predictive Control (MPC), as an advanced control strategy, demonstrates immense potential in the control of power electronic converters due to its unique advantages. This presentation primarily discusses the application and optimization of MPC strategies in power electronic converters for renewable power generation and energy storage systems. The research work on reducing computational burden, removing weighting factors and improving robustness will be presented.

Bio: Jianwei Zhang received the bachelor’s degree in electrical engineering from Northwest A&F University, Shaanxi, China, in 2014, and the Ph.D. degree in electrical engineering from the University of Technology Sydney (UTS), Sydney, Australia, in 2018. He was a Casual Academic with the Faculty of Engineering and IT, UTS, from 2015 to 2018. In 2018, he joined Inner Mongolia University of Technology (IMUT), Hohhot, China, where he is currently an Associate Professor with the College of Electric Power. He was a Visiting Scholar with the Power Electronics, Machines and Control (PEMC) Research Group, Faculty of Engineering, University of Nottingham, U.K., from 2022 to 2023. His research interests include control of power electronic converters, microgrids, energy storage systems, and AC motor drives. He has worked as a principal investigator in multiple projects and has published over seventy technical papers.
 

Linfei Yin
Guangxi University, China

Title: Deep Reinforcement Learning for Smart Generation Control of Novel Power Systems
Abstract: With the continuous influx of massive renewable energy into the novel power system, the generation control of the novel power system encounters unprecedented challenges. Deep reinforcement learning can alleviate the active power imbalance problem of the novel power system to a certain extent and maintain the frequency stability of the system. In this study, based on various special scenarios of the novel power system, such as strong volatility, variable topology, variable parameters and other special scenarios, a more complex deep reinforcement learning method is designed and proposed to counteract these strong uncertainties of the novel power system, so as to ensure the balance of the power and electricity of the novel power system, and to promote the large-scale, safe, and stable operation of the novel power system on a long time scale.

Bio: Linfei Yin, Ph.D., Ph.D. supervisor, graduated from South China University of Technology in 2018, participated in 1 National Key Basic Research Development Program and 2 National Natural Science Foundation of China, presided over 2 National Natural Science Foundation of China, 2 Guangxi Natural Science Foundation of China, 5 Open Funds, and 2 lateral projects of the Southern Grid Digital Research Institute, published a book on “Intelligent Power Generation Control” as a third author, and selected to be in the 2024 annual list of top 2% top scientists in the world, published a total of 77 SCI-retrieved papers of CAS I/II/IEEE Journal as the first author/corresponding author, authorized 49 invention patents for the first inventor and 1 PCT patent, presided over 2 teaching reform projects, participated in more than 10 other teaching reform projects, served as a reviewer of international journals for more than 1,400 times, and was a CSEE JPE excellent reviewer, a member of Power Load Technology Sub-committee and Power System Security Defense and Recovery Control Technology Sub-committee, an expert of Science and Technology and Employee Innovation Incubator of Guangxi Power Grid Corporation, editor-in-chief of special issue for 7 times, second prize of excellent papers of Guangxi Electrical Engineering Society for 2 times and third prize for 1 time.
 

Shuaihu Li
Changsha University of Science & Technology, China

Title: Online voltage stability evaluation and preventive control methods for non analytical power systems based on local measurement information
Abstract: Power System with High Shares of Renewables and Power Electronics has stronger nonlinearity, time variability, uncertainty and other characteristics, and the system security and stability face great challenges. The numerical analysis method of complex dynamic system fixed point and its stability is difficult to meet the requirements of online stability analysis speed and accuracy. The stability assessment technology using local information is an effective method to improve its ability to adapt to new characteristics. This speech will introduce the challenges and current results of online voltage stability evaluation and preventive control methods for non analytical power systems based on local measurement information. Specifically including local voltage stability index considering the influence of load characteristics, the sensitivity analysis method between the arbitrary control parameters and voltage stability index, voltage stability preventive control model, will be presented and discussed.

Bio: Dr. Shuaihu Li received the Ph.D. degree in electrical engineering from Hunan University, in 2015. He has been working for a Postdoctoral Assistant Researcher at the University of Liverpool, U.K in 2016. From February 2017 to January 2021, he has been employed as an Associate Professor in Xiangtan University. From February 2021 to now, he is employed as a Professor and doctoral supervisor in the Changsha University of Science and Technology. And He serves as the head of the Smart Grid Fault Self healing Control Technology Team at the State Key Laboratory of Disaster Prevention and Reduction for Power Grid. His main research interests include online voltage stability monitoring and preventive control methods in large-scale power grids, as well as coordinating control decisions for multiple defense lines. He has published more than 30 peer-reviewed SCI/EI papers. he is also the reviewer of different academic journals, including IEEE Transactions on Power Systems, IET Generation Transmission & Distribution, IET Power Electronics, etc.
 

Yang Gao
Shanghai Jiao Tong University, China

Title: Cloud-edge collaborative energy management and operation control for virtual power plants
Abstract: With the rapid development and digital transformation in the energy field, virtual power plants, as an innovative energy management model, are playing an increasingly important role in optimizing the allocation of energy resources and improving the efficiency and reliability of the energy system. The introduction of cloud-edge collaboration technology brings new opportunities and challenges to the energy management and operation control of virtual power plants.

Bio: Dr. Yang Gao is now an assistant professor with Key Laboratory of Control of Power Transmission and Conversion, Shanghai Jiao Tong University, Shanghai, China. In the past, he worked as a postdoctoral fellow at the KTH Royal Institute of Technology, Sweden for one year in 2019, a senior research associate at the University of Manchester and University of Bristol, United Kingdom in 2021 and 2022, and a research fellow at the National University of Singapore, Singapore in 2022 and 2023. His main interests include digital twin optimization and control of integrated energy system, microgrid, and multi-agent technology in Energy-internet. His main interests include digital twin modelling and swarm intelligence control of integrated energy system and microgrid.
 

Linyun Xiong
Chongqing University, China

Title: Distributed Control of Energy Storage Systems for Power System Frequency and Voltage Regulation
Abstract: The penetration of renewable energy in the power grid has significantly impacted the grid’s frequency and voltage stability. The energy storage system (ESS) based grid frequency and voltage regulation scheme has become a feasible solution. By optimally allocating the ESSs in the wind farm (WF), the overall frequency support capability of the WF can be enhanced, even in the presence of wake effects or individual turbine operation difference. Moreover, the coordinated ESSs are regulated by the distributed consensus controller such that they can collectively respond to frequency violation situations, and maintain high coherence in state-or-charge across the whole time frame. For the power distribution network, the ESSs find their application in mitigating the issue of overvoltage caused by reverse power flow, while maintaining serving as frequency responsive units. A novel consensus control scheme called containment control is applied for the ESSs, where the mission of voltage regulation and frequency control are treated as two vertical control objectives, and can be achieved via a single control mechanism.

Bio: Dr. Linyun Xiong received the B.S. degree from Sichuan University in 2015, and Ph.D. degree in electrical engineering from Shanghai Jiao Tong University in 2019. He is currently an Associate Professor with the School of Electrical Engineering, Chongqing University, Chongqing, China. His research interests include power system nonlinear control, sliding mode control, energy storage, and grid-forming control. Dr. Xiong has authored or co-authored over 60 journal papers, including 24 IEEE Transaction papers; he has leaded 2 projects from the NSFC, 2 key projects from Chongqing government and over 10 projects from State Grid company.
 

Shunbo Lei
The Chinese University of Hong Kong, Shenzhen, China

Title: Enhancing Urban Grid Resilience based on Massive Building Flexibility Resources
Abstract: The lack of flexibility resources and coordination capabilities in urban grids limits resilience, particularly in terms of rapid recovery after disasters. The vast number of buildings in cities harbors significant flexibility resources; for example, virtual energy storage can be achieved through the thermal capacity of buildings and their HVAC systems. When aggregated, these resources provide substantial capacity, a wide modulation range, and quick response times, enhancing the spatiotemporal coordination of various resources and offering new opportunities and methods for improving urban grid resilience. The presenter will introduce their research on building-grid interaction flexibility and enhancing power system resilience, focusing on: experimental studies and data analysis of commercial building load responses; energy efficiency analysis and improvement methods for HVAC systems participating in grid ancillary services; post-disaster recovery methods for urban grids utilizing spatiotemporal flexibility from HVAC systems based on deep reinforcement learning; and a resource trading method to alleviate the "duck curve" using distributed Nash bargaining theory to incentivize building-grid interaction flexibility.

Bio: Dr. Shunbo Lei received his B.Eng. degree from Huazhong University of Science and Technology in 2013, and his Ph.D. degree from The University of Hong Kong (HKU) in 2017. He was a visiting scholar at Argonne National Laboratory from 2015 to 2017, a postdoctoral researcher at HKU from 2017 to 2019, and a research fellow at the University of Michigan-Ann Arbor from 2019 to 2021. He is currently an assistant professor (associate researcher and PhD supervisor) and a presidential young scholar at The Chinese University of Hong Kong, Shenzhen.

His research interests broadly encompass power and energy, optimization, and learning. Dr. Lei serves as an associate editor for several journals, including IEEE Transactions on Power Systems, IEEE Transactions on Smart Grid, IEEE Power Engineering Letters, Energy Reports, and Protection and Control of Modern Power Systems. He is also the vice-chair of the IEEE PES Loads Subcommittee, the chair of the IEEE PES Task Force on FlexGEB to Enhance Electric Service Resilience, and an expert for the China Huadian Corporation.

He has received several awards, including the IEEE Transactions on Smart Grid Top 5 Outstanding Papers Award (2019-2021), the IEEE PES General Meeting Best Conference Paper Award (2022 and 2024), the IEEE PCCC Outstanding Young Engineer Award (2023), and the IEEE PES Technical Council Young Professional Award (2024).

Chao Long
University of Liverpool, UK

Title: Deep Reinforcement Learning for Smart Electric Vehicle Charging
Abstract: Aggregating a large volume of electric vehicles (EVs) can provide considerable capacity for frequency regulation (FR) services. However, disaggregating real-time FR commands to individual EVs results in both immediate impacts to EVs and cascading impacts on future FR bidding. It is crucial to manage the trade-off between these two impacts during disaggregation. This paper proposes a deep reinforcement learning (DRL)-based FR provision method that balances user charging anxiety and aggregate flexibility in real-time disaggregation. The immediate and cascading impacts of disaggregation are modelled through two objectives: 1) user compensation costs related to charging anxiety, and 2) the preservation of EV aggregate flexibility. Incorporating these two objectives, a multi-objective disaggregation optimization problem is formulated with a learnable soft switching factor to balance these objectives. To determine the optimal soft switching factor and achieve coordination between bidding and disaggregation, the reinforcement learning algorithm is utilized to jointly optimize biddings and the soft switching factor.

Bio: Dr Chao Long received the B.Sc. degree from Wuhan University, China, in 2008 and Ph.D. degree from Glasgow Caledonian University, UK, in 2014. Chao is currently a Lecturer at University of Liverpool. Chao’s research focuses on modelling, analysis and optimization of smart power distribution networks and community energy systems. These include management of local energy from solar photovoltaic (PV) systems, batteries and electric vehicles using a combination of novel concepts and techniques, including peer to peer (P2P) energy trading, V2G/V2X technologies, AI/machine learning and Blockchain distributed ledger technology. Chao is the PI and Co-I of 14 research projects funded by EPSRC, EU, Innovate UK, Department for Transport, Royal Academy Engineering, Royal Society, with a total portfolio of ~£1.3M. Chao is a highly cited author. He has got Google Scholar Citations 6006 (by Apr 2024), with H-index of 29. Chao is a senior member of IEEE.

Tianguang Lv
Shandong University, China

Title: Capacity Expansion of High Renewable Penetrated Energy Systems Considering Concentrating Solar Power for Seasonal Energy Balance
Abstract: With the increasing proportion of variable renewable energy which owns fluctuation characteristics and the promotion of the “Clean Heating” policy, the seasonal energy imbalance of the system has been more and more challenging. There is a lack of effective means to mitigate this challenge under the background of gradual compression of the traditional thermal unit construction. Concentrating solar power (CSP) is a promising technology to replace thermal units by integrating emergency boilers to cope with extreme weather, and can meet long-time energy balance as a seasonal peak regulation source. In this paper, we propose a long-term high-resolution expansion planning model of energy systems under high renewable penetration which integrates CSP technology for seasonal energy balance. By taking the energy system in Xinjiang province which is a typical area of the “Clean Heating” project with rich irradiance as a case study, it shows that the optimal deployment of CSP and electric boiler (EB) can reduce the cost, peak-valley difference of net load and renewable curtailment by 8.73%, 19.72% and 58.24% respectively at 65% renewable penetration compared to the base scenario.

Bio: TIANGUANG LU (Member, IEEE) is a Professor with Shandong University and an Associate with Harvard University. He received the B.S. degree in electrical engineering from Shandong University, Shandong, China, in 2013, M.S. degree in Computer Science from Georgia Institute of Technology, GA, USA, and the Ph.D. degree in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in 2018. He was a Postdoctoral Researcher at Harvard University, Cambridge, MA, USA. His research interests include optimization for low-carbon power systems and interaction between energy systems and atmosphere. Dr. Lu is the primary investigator of NSFC funded project with joint funds (U22B20102). He has published over 60 papers on different journals, including Nature Communications (Frist Author), and 3 books. Dr. Lu was a recipient of the IEEE Industrial Application Society Prize Paper Award, in 2017 and 2019. He is an Associate Editor of the CSEE Journal of Power and Energy Systems and IET Renewable Power Generation, and a Review Editor of Frontiers in Sustainable Cites.

Zhi Wu
Southeast University, China

Title: Resilience Enhancement Techniques for Integrated Energy Systems Against Extreme Events
Abstract: The increasing frequency of extreme events poses severe challenges to the security of integrated energy systems (IES). To enhance IES resilience against extreme events, we focus on three critical dimensions: resilience assessment, resilience planning, and resilience restoration, to develop effective resilience enhancement strategies. Firstly, we propose a resilience assessment method for IES. In this work we analyze the specific impact of gas and heat inertia on IES resilience, and identify system vulnerabilities arising from multi-energy interdependencies. Then, we introduce a resilient-oriented IES planning method from an economic-risk balancing perspective, to improve system resistance against extreme events. Finally, we present an energy supply restoration method for IES considering multi-stage and multi-energy coordination, to develop IES emergency response and critical load restoration strategies after extreme events. The goal is to provide technical support for improving IES resilience and ensuring energy security during emergencies.

Bio: Dr. Wu Zhi received his Ph.D. in Electrical and Electronics Engineering from the University of Birmingham in 2016. He is now an Associate Professor at the School of Electrical Engineering, Southeast University. Dr. Wu’s research areas include planning and operation of integrated energy system, resilience, and distribution network optimization. He has been the principal investigator for three projects funded by the National Natural Science Foundation of China (NSFC), including two General Programs and one Young Scholar Program. He has also taken charge of a sub-task under the National Key R&D Program. In 2022, he was awarded the first-class Jiangsu Province Science and Technology Award (ranked 2/11). In 2021, he won the first-class Electric Power Science and Technology Progress Award (ranked 3/15). He has published more than 60 SCI/EI papers, including three ESI Highly Cited Papers (Top 1%), with a total citation count of 3310 in Google Scholar, an H-index of 34, and 58 authorized national patents. He is the reviewer of different academic journals, including IEEE Transactions on Smart Grid/Power Systems/Sustainable Energy, Applied Energy, etc. He also serves as an editor for the Journal of Modern Power Systems and Clean Energy and a youth editor for Power System Protection and Control.

Ying Wang
Sichuan University, China

Title: The Modeling and Analysis of Power Quality by the Data-driven Method
Abstract: The presentation focuses on the modeling and analysis of power quality (PQ) using data-driven methods. It covers three main areas: 1. the current state of power quality monitoring in China's power system, highlighting the use of intelligent fusion terminals for real-time monitoring of low-voltage transformers; 2. harmonic source modeling techniques based on monitoring data, utilizing machine learning and probabilistic statistical models to understand harmonic behavior under different operating conditions; 3. the development of a General Harmonic Emission Probability Model (GHPM) using measured harmonic current data. The talk emphasizes leveraging big data and advanced algorithms to enhance power quality analysis and explores the potential for expanding PQ capabilities in distribution network devices.

Bio: Ying Wang (Senior Member, IEEE) is a professor in the Sichuan University, China. She obtained PhD from the Sichuan University, in 2014. Dr.Wang has working on the field of power quality and premium power, for over 20 years, especially in the field of voltage sag. She is the vice chair of IEEE P2938 and IEEE P3453 and the member of IEC TC 8/WG11.

Weiye Zheng
South China University of Technology, China

Title: Trading mechanism for social welfare maximization in integrated electricity and heat systems with multiple self-interested stakeholders
Abstract: Cooperation across multiple energy sectors is capable of maximizing social welfare as revealed by existing research, but such holistic dispatch can be hardly implemented in a reality where energy sectors are run by different stakeholders respectively. To fill this gap, energy trading among multiple self-interested stakeholders is investigated in this paper. To maximize the social welfare of the trading results, an effective pricing method, referred to as network-constrained generalized locational marginal prices, is proposed. A trading mechanism is then carefully designed to encourage stakeholders to trade at network-constrained generalized locational marginal prices, while economic properties of the resultant generalized Nash equilibrium are analyzed in depth. The effectiveness of the mechanism is validated in two integrated electricity and heat systems with different scales. Numerical comparison demonstrates that existing non-cooperative mechanisms may result in undesirable deadweight losses, while surplus sharing is a tricky issue in cooperative mechanisms, making the cooperation unstable and infeasible. On the contrary, each stakeholder may optimize its subsystem in a self-interested manner in the proposed mechanism, but the overall outcome strikingly coincides with social welfare maximization.

Bio: Weiye Zheng (Senior Member, IEEE) received the B.S. and Ph.D degrees from the Department of Electrical Engineering, Tsinghua University, Beijing, China. He was a recipient of the excellent graduate with thesis award from Tsinghua University, the Outstanding PhD of Beijing City, and the Natural Science First Class Award from Ministry of Education, China. His research interests include analysis, operation and market in integrated energy systems. He has been an Editorial Board Member for several journals including Applied Energy and IEEE Systems Journal.

Yiyan Sang
Shanghai University of Electric Power, China

Title: Nonlinear control approach for direct-drive wave energy conversion systems
Abstract: The wave energy is regarded as the most prominent marine energy form and an essential part of the offshore renewable energy. The direct-drive wave energy conversion system (DDWECS) is widely used in practical projects due to its superior configuration and significant operational efficiency. The DDWECS adopts direct-drive power takeoff technique with a liner permanent magnet generator (LPMG). Nonlinear adaptive controller is developed for effective coordination in the grid-connection operation with supplementary energy storage system (SESS) due to its distinct intermittency characteristics. Firstly, feedback linearization technique is implemented in the generator-side converter controller, grid-side converter controller and SESS-side converter controller. Then, the specific coordination scheme among multiple converters is proposed considering various conditions. Finally, the improved dynamical responses of the DDWECSs with the proposed control strategies are demonstrated under Matlab/Simulink simulation environment. The current research results are illustrated and discussed.

Bio: Dr. Yiyan Sang (IEEE Member, IET Member) was born in November 1991, Shanghai, China. He received the B.Eng. and Ph.D. degrees from University of Liverpool (Uol) in 2014 and 2019 respectively, majoring in Electrical Engineering and Electronics. Currently, he is a lecturer in the College of Electric Power Engineering of Shanghai University of Electric Power (SUEP), Shanghai, China. He has published over 20 peer-reviewed SCI/EI papers. He is now working on the advanced nonlinear control in emerging power electronic converters, renewable energy source generations and large-scale AC/DC power gird.

Li Ding
Wuhan University, China

Title: Advanced Technologies for Demand-Side Flexible Resource Control Based on Distributed Frameworks
Abstract: With global energy demand increasing and environmental considerations becoming ever more critical, demand-side management has emerged as a key approach to improving energy efficiency and reducing carbon emissions. In this talk, I will highlight advanced technologies for distributed control and optimization in demand response. First, a leaderless coordination framework for inverter air conditioners is introduced, leveraging the "comfort state" concept to balance individual temperature preferences with fair power allocation. For thermostatically controlled loads, a distributed control strategy is presented, featuring temperature-based priority and asynchronous communication. Additionally, a robust low-carbon energy management scheme is proposed to optimize economic returns while curbing carbon emissions within microgrid systems. These technologies significantly enhance the flexibility and responsiveness of demand-side resources, providing essential technical foundations for smart grid evolution.

Bio: Li Ding received his Ph.D. in Control Science and Engineering from Huazhong University of Science and Technology, Wuhan, China, in 2010. He is currently a Professor at the School of Electrical Engineering and Automation, Wuhan University. His research focuses on intelligent power utilization technology, particularly in demand-side management and electrical safety monitoring. In recent years, he has led four projects funded by the National Natural Science Foundation of China, and has both led and participated in more than 20 corporate research projects. He has authored two academic monographs and contributed to the development of several national and industry standards.

Lefeng Cheng
Guangzhou University, China

Title: Evolutionary Game Strategies Investigation for Government and Corporate Participation in Wind and Photovoltaic Project Investments Driven by the Carbon Market
Abstract: Under the "Carbon Peak and Carbon Neutrality" policies, China is actively promoting the development of renewable energy projects while gradually establishing and improving its carbon trading mechanism. A key question that arises is how different stakeholders in the electricity market, particularly the government and enterprises, will adjust their behaviors in response to carbon market drivers, and what patterns emerge from these adjustments. In this context, this study investigates the investment game strategies between government and enterprises in wind and solar power projects, considering the influence of the carbon market. The study first provides an overview of the carbon market, the carbon trading mechanism, and the current status of China's renewable energy development. It then reviews the application of evolutionary game theory in the power and clean energy markets. To model the investment behaviors of government and enterprises, evolutionary game theory is employed, with a focus on two-population and three-population models. The study explores the evolutionary stability of different strategies and the conditions for equilibrium. Additionally, multiple rounds of simulations are conducted to analyze the sensitivity of key parameters, such as government subsidies and carbon pricing. The relationship between average expected returns and strategic choices is also examined, offering new insights into the investment behavior of stakeholders in wind and solar projects under the carbon market's influence.

Bio: Cheng Lefeng (born 1990) is an Associate Professor at Guangzhou University, holding a Ph.D. and serving as a master's supervisor. He completed his Ph.D. at South China University of Technology in 2019. In 2023, Dr. Cheng was listed in the sixth edition of the "World’s Top 2% Scientists" ranking for scientific impact, compiled by Stanford University. He is a member of IEEE and serves as the secretary of the IEEE PES Energy Storage Technical Committee (China) Subcommittee on Flexible Resource Interaction in Smart Grids. He is also an executive member of the IEEE PES Smart Buildings, Loads, and Customer Systems Technical Committee (China) Power Load Technology Subcommittee. Additionally, Dr. Cheng is an expert in Guangzhou's Science and Technology Project Expert Database and an invited editorial board member of the journal New Industrialization. His research focuses on artificial intelligence, game theory (particularly evolutionary game theory), and their applications in power markets, smart grids, and integrated energy systems. Dr. Cheng has participated in over 20 projects, including key and major projects with China Southern Power Grid, and has led five national and regional research projects. He has published over 100 academic papers, including more than 20 SCI-indexed journal articles and 60 EI/Chinese core journal articles, and holds 10 patents. His accolades include the 2019 F5000—China’s Top Academic Papers by Leading Technology Journals (First Author), the 2023 Second Prize at the Comprehensive Smart Energy Conference (First Author), the 2018 South China University of Technology Outstanding Doctoral Dissertation Award, the 2018 China Electric Power Enterprise Federation Innovation Award (First Prize, Technology), and other national and provincial awards for scientific and technological innovation. He has also been recognized as an Excellent Author by Proceedings of the CSEE and Electrical Measurement & Instrumentation. In addition, Dr. Cheng has served as a Guest Editor for several special issues in renowned international SCI journals, including Mathematics and Energies. These special issues focus on game theory, decision optimization, and their applications in power systems and integrated energy systems.

Jian Zhao
Shanghai University of Electric Power, China

Title: Situation Awareness for New-type Distribution System: From Estimation to Self-evolution
Abstract: The complexity and dynamics of new distribution systems pose challenges to conventional situation -awareness approaches and hinder the development of new distribution systems. We introduce an innovative approach that enables the progression of system from awareness to self-evolution. Firstly, the Markov-based topology identification technique is proposed to identify the global topological connectivity of the distribution system. Then, the situation awareness method is proposed based on trend analysis to acquire comprehensive situation parameters such as voltage, current, reactive power and active power for each node. Finally, the big data analysis and artificial intelligence algorithms are applied to estimate the future situation of the distribution system, thereby enabling the transition from awareness to self-evolution awareness.

Bio: Prof. Jian Zhao received the B.Eng. degree from Zhejiang University, Hangzhou, China, in 2013, and the Ph.D. degree from The Hong Kong Polytechnic University, Hong Kong, China, in 2017. He is currently a Professor with the College of Electrical Engineering, Shanghai University of Electric Power, Shanghai, China. He was a Research Associate with The Hong Kong Polytechnic University and a Visiting Scholar with the Argonne National Laboratory, Argonne, IL, USA. Prof. Zhao’s research interests include intelligent distribution system, flexible resource scheduling, distributed energy resource control, and etc. He has published more than 50 SCI/EI papers and applied for more than 50 national invention patents. He was the recipient of IEEE Power and Energy Society General Meeting Best Paper Award and Best Reviewer 2018 of IEEE Trans. Smart Grid.

Qian Xiao
Tianjin University, China

Title: Research on optimal operation and control of AC/DC hybrid distribution systems with integration of high-ratio distributed generations
Abstract: Large-scale grid integration of distributed generations is an important measure to accelerate the construction of novel power systems. As a key link between the transmission power system and the distribution system, AC/DC hybrid distribution system will play an important role in the process of absorbing distributed generations. The distributed generations have strong randomness and volatility, and there is space-time imbalance between the load and the supply, and the uncertainty increases in the process of power generation balance and real-time scheduling, which affects the operation economy of the system. On the other hand, the characteristics of "high proportion of power electronic equipment" are becoming more and more obvious, and the moment of inertia and damping are constantly reduced, threatening the safe and stable operation of the system. In view of the above hot issues, combining our latest research progress, this report will first put forward a new type of source-network-load-storage cooperative operation technology. Secondly, the coordination control technology of multiple converters in AC/DC hybrid distribution system is discussed. Then, the local control technology of energy storage units and converters in distribution system is deeply studied. Finally, the future development direction of the AC/DC hybrid distribution system with integration of high-ratio distributed generations will be discussed so as to contribute to the communication and sharing of research results in the field of novel power system.

Bio: Qian Xiao received the Ph.D. degree and worked as a postdoctoral researcher at Tianjin University, where he is currently an associate professor (exceptional promotion) and Ph.D. supervisor. He is on the “World's Top 2% Scientists List” issued by Stanford University and Elsevier database, Candidates of Youth Talent Program of China Association for Science and Technology, Deputy Director/Secretary of the China-British Joint Research Center for Electric Vehicles and Energy Internet of Tianjin University (Tianjin “Belt and Road Initiative” Joint Laboratory), Core backbone of Energy Storage Equipment and System Center of Tianjin University’s National Energy Storage Technology Industry-Education Integration Innovation Platform, Expert for Tianjin Academician Expert Collaborative Innovation Center. He has been awarded 2020 (the First Session) Excellent Doctoral Dissertation Nomination Award of China Power Supply Society (7 items in China, the only one in Tianjin), 2022 Selective Introduction of Postdoctoral Science Funding Winners (100 items in all majors in China, the only one in Tianjin), long-term visiting scholar of Tsinghua University (2023.09-2024.10) and Aalborg University, Denmark (2018.10-2019.11), IEEE Senior Member, Fan-Ke Outstanding Review Experts for Postgraduate Dissertations, and Advanced Society Worker of Tianjin Science and Technology Association.

Mingdong Lei
China Southern Power Grid EHV Transmission Company, China

Title: Design and Application of Remote Centralized Control Center for Multicircuit and Multi-technical HVDC System
Abstract: The development planning and implementing scheme of the remote HVDC centralized control center about to be established in South China Power Grid are discussed. Such related key technologies are analyzed as functions, management, automatic management mode, SCADA system, communication and information exchange, digitalization, intelligent algorithm. As a conclusion, it is feasible to realize the centralized control of multi circuit HVDC system, and the construction process can be implemented in three steps. Moreover, its construction will facilitate the HVDC control mode updating from local level to high access control and AC& DC cooperation control, and improve the stability of the whole power gird.

Bio: Mingdong Lei, Senior engineer，Senior manager of substation management department of CSG EHV Transmission Company, Member of a council of IEEE PES DC System Planning and Design Sub-committee, Executive Member of a council of IEEE PES Substation Intelligent Inspection Sub-committee. He's research fields include HVDC transmission system design, equipment manufacturing, production, operation and maintenance, converter station intelligence, digital, etc. Responsible for 10 scientific and technological projects of seismic resistance, composite insulators and capacitors of converter station equipment of CSG. He is the chief editor of "Operation and Maintenance Technology of Seismic Facilities of Converter Stations" published by China Electric Power Publishing House. He has published more than 10 SCI / EI and domestic core journal papers, and obtained more than 10 invention patents.

Ning Yang
University of Strathclyde, UK

Title: Effect of Grid Forming and Grid Following Control on Firewall Capability of MMC-HVDC Systems
Abstract: The control methods of MMC-HVDC affect the inherent “Firewall” capability, leading to concerns over fault and oscillation prevention from one AC grid to another. We presented a thorough small signal stability comparative analysis between grid following control and grid forming control of a point-to-point HVDC-MMC system, focusing on the key control parameters and short circuit ratio impact on firewall capability. The small signal model of the study system is validated by the electromagnetic transient simulations in MATLAB/Simulink. Based on the small signal model, the grid voltage and current oscillation prevention threshold have been identified through eigenvalue analysis and participation factors. The ranges of critical control parameters and short circuit ratio that significantly contribute to the instability of the MMC-HVDC system are shown in heat maps, which is a valuable guidance for parametric selection and adjustment in practical engineering.

Bio: Dr Ning Yang obtained his PhD degree from the University of Liverpool, Liverpool, U.K., in 2023. He was a Research Fellow with the University of Birmingham in 2023. He is currently a Research Associate with the University of Strathclyde. His current research interests include the control and operation of high-voltage direct current systems, renewable generation systems, and control and analysis of power systems. He has been involved in 5 research projects, an accumulated portfolio of over £1,000,000, including one European Project in Horizon Europe and one Network Rail Project. He has published 3 SCI journal papers. He is also the reviewer of different academic journals, including IEEE Transactions on Industry Informatics, IEEE Transactions on Power Delivery, IEEE Transactions, etc.

Xin Yin
Wuhan University of Technology, China

Title: Dynamic Digital-modeling and Loss-of-excitation Fault Protection Technology of Large Double-fed Variable Speed Pumped Storage Unit
Abstract: This study addresses the dynamic modeling, simulation, and fault protection of variable-speed pumped storage units (VSPSUs) connected to the grid, focusing on accurately capturing their operational characteristics to enhance system reliability. It first develops a dynamic simulation model for VSPSUs, reflecting their fault characteristics and dynamic behavior. The model, built using Matlab/Simulink, was validated through comparisons with real operational data. The research also examines symmetrical demagnetization fault characteristics, distinguishing between symmetrical and asymmetrical demagnetization by comparing DC and AC excitation systems. For symmetrical faults, a dropping impedance circle criterion for generator-side faults and a DC voltage criterion for grid-side faults were proposed. Simulations confirmed the reliability of these protection methods, offering valuable insights into improving fault detection and system protection for VSPSUs.

Bio: Dr. Xin Yin is an Associate Professor at the School of Automation, Wuhan University of Technology, and a member of the Relay Protection Professional Committee of the China Society for Hydropower Engineering. His research expertise lies in power routers, fault characteristics, and safety protection strategies for key equipment in clean energy systems and their integration with power grids. Dr. Yin earned his Ph.D. from the University of Manchester and has held research positions at both the School of Electrical and Electronic Engineering, University of Manchester, and the Department of Electrical Engineering and Electronics, University of Liverpool. In 2023, he joined Wuhan University of Technology, where he has since led several high-profile national research projects, including a Youth Project funded by the National Natural Science Foundation of China (NSFC) and a sub-project within a Key National Project. Dr. Yin has published more than 30 papers in prestigious SCI-indexed journals, and his research innovations, particularly in the protection of hybrid power routers and large-scale clean energy storage systems, have been successfully implemented in demonstration projects and products across multiple enterprises, delivering substantial societal and economic benefits.

Shuai Zhou
Auckland University of Technology, New Zeland

Title: Exploring the Role of Offshore Wind Energy in Achieving New Zealand’s Renewable Energy Goals
Abstract: Offshore wind energy presents a transformative opportunity for New Zealand to achieve its ambitious renewable energy goals and meet its target of net-zero emissions by 2050. With extensive coastlines and abundant wind resources, New Zealand is uniquely positioned to harness the potential of offshore wind farms. This speech will explore the current state of offshore wind energy globally and its relevance to New Zealand’s energy landscape. This talk will include an overview of offshore wind energy technologies, challenges and opportunities of forecasting, developing and integrating wind energy into the national grid in New Zealand.

Bio: Dr. Zoey is a Lecturer in the Department of Electrical Engineering at Auckland University of Technology (AUT). She holds a bachelor’s degree in Electrical and Electronic Engineering from Huazhong University of Science and Technology (HUST) and a master’s degree in Power Systems Engineering from South China University of Technology (SCUT), where she developed a strong foundation in power systems and energy technologies. She later pursued her PhD at The University of Auckland in New Zealand, focusing on reinforcement learning for integrating renewable energy sources into modern power grids. Her research expertise includes developing innovative approaches for incorporating renewable energy sources, such as offshore wind and PV systems into modern power grids using advanced methods, like machine learning, intelligent control. Dr. Zoey has published widely in high-impact journals and international conferences. She is actively engaged in interdisciplinary projects, collaborating with academic and industry partners to develop cutting-edge technologies and strategies that drive the transition toward a zero-carbon energy future.
 

Xiaolong Jin
Tianjin University, China

Title: Aggregation, Control, and Market Mechanisms for Public Buildings-to-Grid
Abstract: This invited talk delves into the aggregation, control, and market mechanisms that enable effective interaction between public buildings and the power grid, emphasizing their critical role in enhancing grid flexibility and energy efficiency. Public buildings, equipped with distributed generation, energy storage, and demand response capabilities, hold significant potential as energy prosumers. This presentation addresses strategies for aggregating these diverse resources into unified, controllable entities and explores advanced control methods to optimize energy flows while ensuring reliable grid integration. Furthermore, the talk highlights practical applications of building-to-grid (B2G) integration, showcasing successful implementations and pilot projects that demonstrate its feasibility and impact. Market mechanisms designed to incentivize public building participation in energy and ancillary service markets will also be discussed. By balancing technical, economic, and regulatory considerations, this presentation offers insights into realizing resilient and sustainable energy systems through innovative B2G frameworks.

Bio: Dr. Xiaolong Jin is an Associate Professor in the Department of Electrical Engineering at Tianjin University. He also serves as the Deputy Director of the UK-China Joint Research Center on Electric Vehicles and Energy Internet at Tianjin University. He received the B.S., M.S. and Ph.D. degrees from Tianjin University, China in 2012, 2015 and 2018, respectively. From 2019 to 2022, he was a postdoc researcher with Technical University of Denmark. From 2017 to 2019, he was a joint Ph.D. student with Cardiff University, UK. He has long been engaged in research on the regulation and interaction strategies of demand-side resources and their associated market mechanisms.
He has led a package of a Smart Grid-National Science and Technology Major Project (2030) and a National Natural Science Foundation of China (NSFC) project, in addition to serving as the principal investigator for five provincial and ministerial-level projects, including the Tianjin Natural Science Foundation. He has published over 90 academic papers, including two ESI Top 1% Highly Cited Papers. He has received one Second Prize of the Tianjin Science and Technology Progress Award and was recognized as a “Top 2% Scientist Worldwide” in both 2023 and 2024. He serves as a Youth Editorial Board member for journals. He has organized special issues for several international academic journals and chaired sessions at international conferences, including CIEEC 2023, APPEEC 2024, and CUE 2023, where he also delivered invited keynote speeches.

Xinlei Cai
Power Dispatch Control Center of Guangdong Power Grid Co., Ltd., China

Title: Machine Learning Based Approach for Social Behavior Analysis in Electricity Markets
Abstract: A modern electricity market is essentially a complex network, characterized by complicated interactions among cyber communications, physical systems, and social agents. Trading behavior modeling has always been complicated in the physical system-based market. In this paper, trading behavior modeling in the electricity market is solved by a data-driven method combining experimental economics and machine learning, called Hybrid Experimental Learning (HEL). Based on the historical and experiment simulated data, HEL models the trading behavior by a machine learning generative model which will be interpreted by a post hoc interpretation approach. Taking a simulated electricity market based on the trial spot market rule in Guangdong, China as an example, a generative adversarial network (GAN) is employed to generate the offering strategies of a gas generator. Local interpretable model-agnostic explanation (LIME) as a post hoc interpretation approach is applied to explain the relationship between the output of GAN and some of the inputs of HEL, which can be described as offering mechanisms for the gas generator.

Bio: Cai Xinlei graduated from Xi'an Jiaotong University in 2012 with a master's degree in Power System and Automation. He is currently a leading technical expert at Power Dispatch Control Center of Guangdong Power Grid Co., Ltd, specializing in the operation and control of complex large-scale power grids, as well as in technology innovation and promotion. His research areas include real-time dispatching technology for new types of power systems and the application of artificial intelligence in power systems. In the past five years, he has received 12 industry and provincial company awards, won six prizes for employee innovation achievements and results transformation, obtained ten authorized invention patents, and presided over the publication of three sets of monographs. He has been honored as "Technical Expert" by Guangdong Power Grid Corporation, "Youth Job Skilled Hand" by Southern Power Grid Corporation, and serves as the technical director for Guangdong Power Grid Corporation's national project on "Multi-scene Smart Unmanned Operation System". Currently, he is a member of the National Electricity Demand Side Management Standardization Technical Committee, participating in the drafting of several national and enterprise standards. He has published more than 20 peer reviewed SCI/EI papers and also serves as a reviewer for several IEEE academic journals.

Xianan Du
Xi’an Jiaotong University, China

Title: The Numerical Method for Liquid-metal Cooled Fast Reactor Neutronic Analysis and its Application
Abstract: Liquid-metal cooled fast reactors (LMFRs) have garnered significant attention due to their high thermal efficiency, excellent neutron economy, and potential for closed fuel cycles. At the same time, LMFR is a key link between the preceding and the next in the "three steps" strategy of nuclear energy development in China. High-precision numerical analysis software of liquid-metal cooled fast reactor is the basis for improving the research and development level of fast reactor in China. However, the neutronic analysis of LMFRs is highly complex, primarily due to the unique characteristics of liquid-metal coolants, such as their high density and low neutron absorption, as well as the fast neutron spectrum and heterogeneous core structure.
This report introduces a comprehensive numerical method for the neutronic analysis of LMFRs, integrating advanced computational techniques and validated models to tackle the unique challenges posed by these reactors. Based on this method, a fully self-controlled code system, named NECP-SARAX, has been developed. Through rigorous verification and validation processes, NECP-SARAX has demonstrated its capability to meet stringent engineering requirements. It has been successfully applied to several major nuclear energy engineering projects in China, providing a reliable and efficient tool for neutronic analysis and reactor design optimization.

Bio:  Xianan Du, an associate professor and master's degree supervisor at Xi'an Jiaotong University. He graduated from the School of Nuclear Science and Technology at Xi'an Jiaotong University in 2018 with a Ph.D. in Nuclear Reactor Physics. His research focuses on core design of advanced nuclear energy systems and neutron calculation methods. He also serves as the project manager of the Loong System Department. From 2018 to 2019, he conducted postdoctoral work at the Ulsan National Institute of Science and Technology (UNIST) in South Korea, where he was primarily responsible for the development of physical calculation programs for lead-cooled fast reactors. To date, he has presided over one National Natural Science Foundation of China (NSFC) Youth Project, one General Project, and one Key Fund in collaboration, as well as more than 10 collaborative research projects. He has also participated in over 15 research projects. He has published over 50 scientific papers in various journals, among which more than 20 have been indexed by SCI, and he has obtained four national invention patents.

Zijie Meng
Power Dispatch Control Center of Guangdong Power Grid Co., Ltd., China

Title: Security verification of the power grid dispatch operation with LLM
Abstract: The security verification of the power grid dispatch operation is crucial to ensure the security and stability of the power system. The traditional security verification of operation involves two aspects: system state verification and operation regulations verification. The system state verification assesses the post-action power system state using simulation models and software, and the operation regulations verification judges whether the operations comply with the natural language-based operation regulations. The latter relies primarily on the knowledge of dispatchers, which suffers from inefficiencies and lacks accuracy. Although existing automated verification methods, such as rule-based validation, have alleviated these issues to some extent, the generalization ability and reliability to frequent update regulations remain a challenge. We propose a Retrieval-Augmented Generation (RAG) enhanced Large Language Model to achieve automated operation regulations verification to power grid dispatch operation with unconstrained natural language. The proposed method possesses natural language understanding capabilities to comprehend the semantic information of dispatch operation regulations accurately and updates the regulatory knowledge in real time without additional training. To improve validation accuracy, a hierarchical document retrieval method for RAG is designed, which retrieves regulatory entries related to dispatch operations and provides LLM with the necessary contextual information. Additionally, an Operation AutoPrompt Generation technique is introduced to automatically generate prompts for different dispatch operation actions, further enhancing the framework’s performance. Experimental results with both simulation data and the South China Power Grid data show that the proposed method achieves excellent performance and high generalization ability in the task of operation regulations verification, reaching an average verification accuracy of 89% across multiple datasets.

Bio: Meng Zijie graduated from Xi'an Jiaotong University in 2011 with a master's degree in Power System and Automation. He is currently an expert at Power Dispatch Control Center of Guangdong Power Grid Co., Ltd, specializing in the operation and control of complex large-scale power grids. His research areas include real-time dispatching technology for new types of power systems and the application of artificial intelligence in power systems.He has published more than 30 peer-reviewed SCI/EI papers and also serves as a reviewer for several IEEE academic journals.

Jianfeng Wen
University of Liverpool, UK

Title: Learning from and Surpass Human Demonstrations: A Hybrid Augmented Intelligence Approach for Multi-Stage Power Dispatch
Abstract: Due to the recent rapid developments in fast charg ing technology for electric vehicles (EVs), these flexible mobile storage resources can provide auxiliary services to the power grid in emergency circumstances. Therefore, it is imperative to develop a resilient enhancement planning scheme for this coupled network under severe contingencies. To this end, this paper investigates a novel robust resilient enhancement scheme for planning charging infrastructure in coupled networks. The objective is to minimize both (i) the investment and operation cost of the coupled network under uncertain traffic demands, and (ii) the EV participation cost for the grid support scheme during contingencies. The investment scheme for power distribution lines and charging stations is determined before the uncertainty realization in the first stage, while the objective function is minimized in the worst possible manner within a specified uncertainty set in the second stage. The nested column-and constraint generation (NC&CG) algorithm is applied to solve this robust optimization problem. Numerical simulations of two coupled networks are conducted to demonstrate the effectiveness of the proposed robust resilience enhancement scheme.

Bio:  Jianfeng Wen received the B.S. and M.Eng. degrees in electrical engineering from the Huazhong University of Science and Technology, Wuhan, China, in 2018 and 2020, respectively. Then he obtained the PhD in electrical engineering from the University of Liverpool, Liverpool, U.K. He is currently working as a postdoctoral in The University of Liverpool. His research interests include control and energy management of micro-grid systems, and electrified transportation optimization.

Yongping Wang
Xi’an Jiaotong University, China

Title: R&D on the Analysis Method of the Gen-IV Nuclear Reactor - the Pebble-Bed High Temperature Gas-cooled Reactor
Abstract: As one of the Generation IV nuclear reactors, the Pebble-Bed High Temperature Gas-cooled Reactor (PB-HTGR) shows promising prospects. Aiming to address the emerging requirements in the design improvement of the existing PB-HTGR in China, the Nuclear Engineering Computational Physics (NECP) Laboratory of Xi'an Jiaotong University carried out extensive researches on the neutronics and thermal-hydraulics analysis methods and developed a code named NECP-Panda. This presentation gives a brief introduction of NECP-Panda and shows its application on the world's first Generation IV nuclear power plant - Huaneng Shidao Bay High Temperature Gas Cooled Reactor Demonstration Project of China.

Bio:  Yongping Wang is an Associate Professor and Doctoral Supervisor of Xi'an Jiaotong University, he is also awarded the ‘Young Star Talent’ for scientific and technological innovation of China National Nuclear Corporation (CNNC). He is currently engaged in research on the design and analysis methods of high-temperature gas cooled reactor core and reactor shielding. He has leaded two China National Natural Science Foundation projects, and participated in the projects of National Key Research and Development, Nuclear Energy Development, etc. He has published over 50 journal papers and has been granted more than 10 invention patents. At the same time, he is serving as the Associate Editor of the internationally famous journal ‘Nuclear Engineering and Radiation Science’ and the SI Editor of ‘Annals of Nuclear Energy’. He has also won several academic awards such as the Special Prize and the First Prize for Outstanding Achievements in Science and Technology Research of High Education Institutions in Shaanxi Province.

Shan He
Aalborg University, Denmark

Title: Passivity-Based Control of Grid-Connected Converters
Abstract: Increasing the integration of renewable energy sources has become a crucial pathway for decarbonizing power  systems. As the interface between renewable generation and the power grid, grid-connected converters play a vital role in ensuring efficient and reliable power conversion. However, control delay and outer loops lead to wideband negative damping when operating under widely varying grid impedances. Passivity-based control presents a promising solution for shaping the output impedance of grid-connected converters. Specifically, the resistance of the output impedance should remain nonnegative across all frequencies, thus ensuring stable operation according to the Nyquist stability criterion. Over the past decade, the application of passivity-based control in grid-connected converters has seen significant progress. Early efforts focused on capacitor current damping, while recent advancements have expanded toward wideband damping and cost effective implementations. To further drive advancements in passivity-based control, this presentation will explore recent applications in various contexts including harmonic control with infinite controllers, voltage control, multi-sampling control, and weak grid operations.

Bio: Shan He (Senior Member, IEEE) received the B.S. degree from Northeast Electric Power University, Jilin, China, in 2015, the M.S. degree from Zhejiang University, Hangzhou, China, in 2018, and the Ph.D. from Aalborg University, Aalborg, Denmark, in 2022, all in electrical engineering. His research interests include gridconnected renewable system, power-to-x, reliability, battery management, and motor drive. Since 2022, he has been with the Department of Energy, Aalborg University as a postdoc. In 2021, he was a visiting researcher with Institute for Power Generation and Storage Systems, RWTH Aachen University, Aachen, Germany. In 2023, he was a research associate in Kiel University, Kiel, Germany. He serves as Associate Editor for Protection and Control of Modern Power Systems, and guest associate editor of IEEE Transactions on Power Electronics.

Hai Lu
Electrical Power Research Institute, Ynunan Power Grid Co. Ltd., China

Title: Practice and Exploration of International Cooperation Platform on Power Technology in the Great Mekong Subregion
Abstract: The development of Lancang-Mekong countries' power interconnection supports regional energy allocation and will strongly stand for the integrated and coordinated development of the Lancang-Mekong region. In order to support the power technology integration in the Greater Mekong Subregion, Yunnan Power Grid Co. Ltd., together with China Southern Power Grid Lancang-Mekong International Company and Southeast University, established the Yunnan Lancang-Mekong Countries Power Technology International Cooperation Joint Laboratory. The laboratory mainly conducts four technical studies, including standard differentiation, cross-border power grid planning simulation, technology localization promotion and cross-border power grid regulation. It has built four technical demonstration bases in Vientiane (Laos), Ruili (Dehong, Yunan), Jiegao (Mujie, Maynmar) and Hekou (Honghe, Yunnan). Relying on 12 industry-university-research cooperation units, it has built 6 overseas technology promotion centers, gradually forming a new model of connecting industry-university-research to serve the coordinated development of energy in the Greater Mekong Subregion. Yunnan Power Grid Company will continue to carry out power technology cooperation in the Lancang-Mekong region, promote technical cooperation, standard output and cross-regional cooperation, and help the economic integration and development of the Greater Mekong Subregion.

Bio: Dr. Hai Lu obtained his PhD in 2012 from the Royal Institute of Technology in Sweden. He is currently professor-level senior engineer, Director of Lancang-Mekong Joint Laboratory for International Cooperation in Power Technology, young and middle-aged reserve talent of Yunnan Province, international commissioner of Yunnan Province, and member of the China Southern Power Grid Precision Talent Support Program, Representative of the China region of the SC 8A (Renewable Energy Grid Connection Technology) Working Group of the International Electrotechnical Commission (IEC), representative of the China region of TC 117 (Concentrated Solar Power Generation), member of the National Energy Industry Wind Power Grid Connection Standard Subcommittee, member of the Household Photovoltaic Power Generation Standardization Committee of the China Electricity Council, and member of the National Smart Power Generation Standardization Working Group. He is responsible for one major national strategic international cooperation project, one National Natural Science Foundation, and two key projects of China Southern Power Grid, won three provincial and ministerial awards. He published more than 40 papers, applied for 45 invention patents, presided over the compilation of one international standard, participated in the compilation of three international standards and one national standard. He has been engaged in renewable energy grid connection control for a long time.

Weiyu Wang
Changsha University of Science and Technology, China

Title: Koopman Operator-Based Data-Driven Methods for Frequency Stability Analysis and Hybrid Wideband Oscillation Source Identification
Abstract: With the increasing integration of power electronic-interfaced devices, modern power system inertia and damping are continuously decreasing, which result in more complex frequency stability and oscillation characteristics. Classical state-space models struggle to accurately capture the nonlinear dynamics of power-electronics-dominated systems, which introduces substantial challenges for frequency stability analysis and oscillation identification. To address these challenges, this report introduces a Koopman operator-based data-driven approach for frequency stability analysis and hybrid wideband oscillation source identification. The Koopman operator enables the transformation of nonlinear system dynamics into an equivalent finite-dimensional linear model in an observable space by real-time measurements. Under the Koopman framework, a rolling prediction-based EDMD method is proposed for predicting the system frequency trajectories and frequency nadir. Moreover, an oscillation modal energy analysis method is proposed to identify the dominant oscillatory modes and their respective sources. By examining the oscillation energy distribution across these modes, the contribution of each device to specific oscillations can be evaluated and oscillation sources can be identified by tracking modal energy propagation pathways. Time-domain simulations validate the effectiveness of the proposed method.

Bio: Dr. Weiyu Wang (Member, IEEE) is currently an Associate Professor at the School of Electrical and Information Engineering, Changsha University of Science and Technology, and a member of State Key Laboratory of Disaster Prevention & Reduction for Power Grid. He received the B.S. degree (2014) and Ph.D. degree (2020) from Hunan University. From 2018 to 2019, he was also a visiting researcher at the University of Liverpool, UK. He currently leads one research project funded by the National Natural Science Foundation of China, two projects supported by the Natural Science Foundation of Hunan Province, and five projects in collaboration with grid enterprises. He serves as a reviewer for several IEEE Transactions journals and a guest editor of the Energies special issue. He was recognized as an Outstanding Reviewer for IEEE Transactions on Power Systems in 2022 and 2023. He received the Best Paper Award at the 16th China Smart Grid Seminar. He has published more than 20 papers in the SCI/EI journals. His research interests include the grid forming control techniques, and the stability analysis and control of hybrid ac/dc power systems.