Invite Speakers

Invite Speaker I

Hua Ye
Shandong University, China

Title: Efficient Eigen-analysis of Grid-connected MMC Characterized by Linear Time-Periodicity
Abstract: The linear time-periodic (LTP) theory is essential to the stability assessment of the grid-connected modular multilevel converter (MMC) system with multiple harmonic components in steady state. Based on the Floquet theory, this paper presents a semi-analytical expression for the system state transition matrix over a period (that is, the monodromy matrix) of LTP system based on the Chebyshev collocation method. Specifically, an explicit expression for the system state after a period is derived in terms of the initial condition, whose coefficient matrix is the approximated monodromy matrix. Its eigenvalues, i.e., Floquet characteristic multipliers, have high accuracy. Compared with the commonly-used time-domain integration method, the presented method obtains greater efficiency by avoiding numerically integrating the studied system itself.

 

Biodata: Hua Ye is currently a professor and the vice dean of School of Electrical and Electronic Engineering, Shandong University, Jinan, China. He is also the vice director of Key Laboratory of Power System Intelligent Dispatch and Control of Ministry of Education. Prof. Ye obtained his Bachelor and Ph. D degrees in both electrical engineering in 2003 and 2009, respectively. He has taken in charge of three grants from National Science Foundation of China and has published more than 50 peer-reviewed journal papers and 3 monographs. He was the recipient of the Second-Class Award for Scientific and Technological Advancement of Shandong Province in 2021. His research areas including power system small signal stability analysis and control, MTDC grid.

 

Invite Speaker II

Hongcai Zhang
University of Macau, China

Title: Utilizing Demand-side Generalized Energy Storage to Decarbonize Future Smart Cities
Abstract: Our modern urban power system is experiencing the Third Energy Revolution that features the integration of high penetration of renewables, e.g., wind and solar. However, renewable generation is known to be intermittent and stochastic which cannot match the electricity demand well. This “mismatch” may significantly deteriorate the security operation of power systems. Widely installing energy storage systems is a possible solution; but is extremely expensive. This talk discusses how existing & cheap demand-side resources can be utilized as generalized storage systems to balance the electricity supply and demand, which will further promote the integration of renewables and decarbonize our future smart cities.
 

Biodata: Hongcai Zhang received the B.S. and Ph.D. degree in electrical engineering from Tsinghua University, Beijing, China, in 2013 and 2018, respectively. In 2018-2019, he was a postdoctoral scholar with the University of California, Berkeley, and also a research affiliate with the Lawrence Berkeley National Laboratory, Berkeley, California, USA. In 2019, he joined the State Key Laboratory of Internet of Things for Smart City of University of Macau, Macao, China, where he is currently an Assistant Professor in Smart Energy. His research interests include integrated energy systems, Internet of Things for energy systems, transportation electrification etc. Hongcai Zhang has published over 60 peer-reviewed SCI-indexed journal papers including 2 ESI highly cited papers. He received the Second Prize of the Macao Natural Science Award, the "Excellent Paper Award" in EVS34, the “Best Paper Award” in iSPEC 2021, and the “Best Paper Award” in EI2 2022. He is currently an associate editor of Journal of Modern Power Systems and Clean Energy, associate editor of IET Electrical Systems in Transportation, associate editor of iEnergy, a member of China Electrotechnical Society Young Scholar Committee and Secretary-General of IEEE PES China Energy and Transportation Nexus Subcommittee.

 

Invite Speaker III

Xueqian Fu
China Agricultural University, China

Title: Key Technologies and Prospects of Agricultural Energy Internet.
Abstract: In the context of modern agricultural production mode and domestic energy consumption, profound changes have taken place in agricultural and rural energy consumption, resulting in the demand for new technology development in various sectors of source, network, and load in rural energy systems. Agricultural energy internet (AEI) has promoted the development of renewable energy and agricultural electrification in villages. The construction of the AEI is crucial for achieving the synergistic development of agriculture, energy, and the environment. We investigate the basic theory and key technologies of AEI, and conduct the prospects for the direction of agricultural energy technology. Our research investigation shows that the AEI framework proposed by China Agricultural University is of great significance for realizing agricultural electrification and reducing agricultural carbon emissions. We will present the key technologies and prospects of AEI in our report. Scholars who are interested in rural energy are welcome to come and listen and discuss the application of modern energy technology in the agricultural field together.
 

Biodata: Xueqian Fu (Member, IEEE) received his B.S. and M.S. degrees from North China Electric Power University in 2008 and 2011, respectively. He received his Ph.D. degree from South China University of Technology in 2015. From 2011 to 2015, he was an electrical engineer with Guangzhou Power Supply Co. Ltd.. From 2015 to 2017, he was a Post-Doctoral Researcher at Tsinghua University. He is currently an Associate Professor at China Agricultural University. His current research interests include statistical machine learning, Agricultural Energy Internet, and PV system integration.
He is an associate Editor-in-Chief of “Information Processing in Agriculture", an associate editor of “Protection and Control of Modern Power Systems”, Lead editor of “International Transactions on Electrical Energy Systems", Guest Associate Editor of “Frontiers in Energy Research", and Guest Editor of “Applied Sciences". He served as the Technical Program Chair of the ICEEEE 2023, Session chair of the IEEE AEEES 2020/2022/2023, Workshop Chair of the SEGRE 2023, Special Session organizer of the ICPET 2023, an Invite Speaker and the Session chair of ICPE 2022.
 

Invite Speaker IV

Leijiao Ge
Tianjing University, China

Title: Virtual Collection Technology for Distributed PV Data Empowered by Artificial Intelligence
Abstract: In recent years, with the rapid development of distributed photovoltaic systems (DPVS), the shortage of data monitoring devices and the difficulty of comprehensive coverage of measurement equipment has become more significant, bringing great challenges to the efficient management and maintenance of DPVS. For this reason, it is crucial and beneficial to develop a cost-effective and computationally efficient data collection method for large-scale DPVS clusters with relatively small numbers of sensing devices deployed at strategic locations. Virtual collection is a new DPVS data collection scheme we propose, whose core idea is to use the power data of selected reference power stations (RPSs) in the region as input to infer the power data of other stations through artificial intelligence algorithms. If deployed at strategic locations with proper redundancy, the reduced sensing network can still provide low-cost yet highly sufficiently accurate measurements of the DPVS networks for various power operations.

 

Biodata: The research team led by Associate Professor Ge Leijiao focuses on smart distribution network situational awareness, new energy grid-connected optimization control, cloud computing technology, and power distribution big data technology. In the past five years, the team has won 24 provincial and ministerial awards, including one first prize for energy innovation from China Energy Research Society and one-second prize for scientific and technological progress from Tianjin Municipal Government; published 50 SCI papers as the first/corresponding authors, including top journals such as IEEE TSG, IEEE TPS, IEEE TSTE, ACS Nano, etc.; authorized 34 invention patents; authored five books of international and national monographs (chapters) and six international and domestic industry standards and specifications. His team intends to study the popularization and application of artificial intelligence and intelligent manufacturing technologies in smart distribution grids and provide theoretical support and technological leadership for implementing the "double carbon" goal in electric power and energy.

 

Invite Speaker V

Dongran Song
Central South University, China

Title: Some Works on Optimization of Large Offshore Wind Farm Operation
Abstract: Considering the wake effects, developing optimal operation strategy is essential for large offshore wind farms. In this report, we introduce some works on recent years by our group. The main contents include three aspects: Wake Effect and Wake Prediction Modeling of Large Offshore Wind Farm, Construction of Directed Network Structure and Intelligent Clustering for Large Offshore Wind Farm, and Real time optimized scheduling strategy for wind farms based on intelligent clustering network. On this basis, differences between the fixed-bottom and floating wind turbines are clarified, and some research results were shown. Finally, future research directions are put forward.

Biodata: Dongran Song received the B.S., M.S. and Ph.D. degrees from the School of Information Science and Engineering, Central South University (CSU), Changsha, China, in 2006, 2009 and 2016, respectively; worked for 7 years (between 2009-2016) in Mingyang Smart Energy, a world top-class wind turbine manufacturer, and completed R&D of the first SCD wind turbine in China (MySE 3.0-110). Since 2018, he has been as an associate professor at School of Automation, Central South University, and served as an executive director of IEEE PES Technical Committee, member of the special committee of sustainable energy control group of Chinese Society of Automation, and member of the special committee of dynamic planning and intelligent adaptive learning of Chinese Society of Artificial Intelligence, Editorial board and Associate editor of some know journals, such as Protection and Control of Modern Power Systems (SCI, Q1), Journal of Marine Science and Engineering(SCI, Q1), Frontiers in Energy Research(SCI, Q2), Energies(SCI, Q3), Technolgies(ESCI), Energy Engineering (EI).

 

Invite Speaker VI

Yixuan Chen
The University of Hong Kong, Hong Kong, China

Title: A Physics-informed Deep k-medoid Scenario Clustering Method for Transmission Network Expansion Planning
Abstract: Transmission network expansion planning (TNEP) is driven to consider far more scenarios than ever as the diversity of injections into power systems increases significantly with the ever-increasing penetration of renewable energy. By selecting a representative scenario subset, scenario clustering is more and more important to realize the computational feasibility of TNEP, which aims at ensuring the TNEPs over the full scenario set and over the subset have the same line addition strategies. In this study, we focus on k-medoid scenario clustering, which quantifies dissimilarities between scenarios by Euclidean distance in a certain clustering space. However, because of the obscure relationship between injections and the line addition strategies, how to define the clustering space and whether Euclidean distance is suitable to measure the dissimilarity are still left open. Herein, we formally give sufficient conditions (SCs) for the clustering aim accomplishment. Then, using multi-parametric mix-integer linear programming (mp-MILP), we disclose the non-linear relationship between injections and the line addition strategies and show the input space, where the injection data lies, is not qualified for the clustering space. Because of the difficulty of a pure physics-based method to define a suitable clustering space, we establish a deep k-medoid clustering network (DKCN) and transform the SCs into a physics-informed loss function (PLF). Driven by the PLF, a clustering space is found by the DKCN, which not only satisfies the SCs but also ensures Euclidean distance is suitable. Finally, an analytical condition is provided which guarantees the clustering aim can be achieved if k-medoid scenario clustering is taken place in the clustering space found by the DKCN.
 

Biodata: Yixuan Chen received the B.E. degree and the M.E. degree from South China University of Technology, Guangzhou, China, in 2016 and in 2019, respectively, both in electrical engineering. She received the Ph.D. degree in 2023 and is now working as a postdoctoral researcher with the Department of Electrical and Electronic Engineering, The University of Hong Kong, Hong Kong, China. Her research interests include multi-objective optimization, economic-environmental dispatch, physics-informed artificial intelligence, and data-aided operation and planning of energy systems.

 

Invite Speaker VII

Yunfeng Yan
Zhejiang University, China

Title: Key Technologies of Electric Power Knowledge Graph
Abstract: With the rapid development of power infrastructure construction in China, a large amount of daily operation and fault data has been accumulated in the field of power operation and maintenance, but these data have not been fully utilized. Traditional manual operation and maintenance methods have many drawbacks, such as low data utilization rate, strong dependence on expert experience, high labor cost, low data correlation, and lack of intelligent disposal means. Solving data mining and efficient utilization is of great significance for improving the operation and maintenance level of power facilities. As a new artificial intelligence method, knowledge graph has significant application prospects in the field of power facility operation and maintenance. This report will explain the relevant fundamental technologies involved in the knowledge graph in the field of power operation and maintenance.

 

Biodata: Dr. Yunfeng Yan has been engaged in research in the fields of artificial intelligence, video/image processing, and power security for a long time. She has led a total of 7 projects, including the National Natural Science Foundation of China and the "Jianbing" Program of Zhejiang Province. She has published over 40 academic papers indexed by SCI/EI and has been granted 18 invention patents in China/the United States. Dr. Yan has won two provincial and ministerial level science and technology awards, including the first prize of science and technology award of Zhejiang province.

 

Invite Speaker VIII

Tao Chen
Southeast University, China

Title: Learning-assisted Management and Pricing Methods for Demand Side Resources and Transactive Energy
Abstract: The key of wholesale market mechanism design is using the price signal to guide reasonable allocation of power flow, and there should be an energy-price coupling mechanism at the distribution side as well, improving the utilization efficiency of distributed resources. By considering the imperfection of intelligent algorithm applications in energy transactions at distribution side and its complex interaction relationships, which refers to the cutting-edge concept of transactive energy and utilizes deep reinforcement learning technology to study the intelligent decision-making problems in building a local transactive energy system. The solution to such problems refers to the following tasks: 1) study the improvement method for deep reinforcement learning algorithms, proposing a constrained Markov Decision Process model suitable for transactive energy system modeling; 2) study the framework of intelligent decision-making, proposing a transactive energy system model that consists of customer intelligent decision-making submodule and customized pricing submodule; 3) studies the multi-agent mechanism that is able to support the interaction of multiple entities, proposing a high efficient verification and evaluation method based on Internet-of-things platform.

 

Biodata: Tao Chen is currently an Assistant Professor in School of Electrical Engineering, Southeast University, China. He is also affiliated with Tampere University, Finland, working as an adjunct researcher. His research interests are about demand side management, electricity market and machine learning applications. He worked as a Postdoctoral Associate in Advanced Research Institute (ARI), Virginia Tech, Washington D.C., USA, 2018-2019. He also worked as an Intern Engineer in Global Energy Interconnection Research Institute North America (GEIRINA), California, USA, 2017-2018 and Project Researcher in Tampere University of Technology, Finland, 2013-2015. He received the Best Paper Award for IEEE ISGT-Asia 2019, IEEE iSPEC 2021, IEEE CIEEC 2022 and the Best Reviewer Award for IEEE Transaction on Smart Grids 2020. He was a Lead Editor for IET Renewable Power Generation and (co)authored more than 100 publications and PI for several R&D projects, including National Natural Science Foundation of China (NSFC), Natural Science Foundation of Jiangsu Province, and Science and Technology Project of the State Grid Corporation of China (SGCC).

 

Invite Speaker IX

Zhenning Pan
South China University of Technology, China

Title: Learning From and Surpass Human Demonstrations: A Hybrid Augmented Intelligence Approach For Multi-stage Power Dispatch
Abstract: The low learning efficiency and feasibility hinder practicability of artificial intelligence based power dispatch. We introduce a hybrid augmented intelligence approach to tackle multi-stage power dispatch under uncertainty. Firstly, inverse reinforcement learning with trajectory ranking is employed to deduce the latent reward function from human demonstration. Then, expert demonstrations guided learning is proposed. Behavior cloning is adopted to transfer human knowledge into guided policy. This policy is used to guided RL dispatcher to a safe and fast learning process at the early stage, which avoids frequent trial and error. A smooth switch mechanism then is applied which allows RL to conduct free exploration to seek for better policy surpassing expert demonstrations. Finally, numerical results are discussed.

 

Biodata: Dr. Zhenning Pan obtained his Ph. D in Power System and Automation from South China University of Technology (SCUT), China in 2021. He is now a postdoctoral research associate in the school of electric power engineering, SCUT. Dr. Pan’s research areas include learning and optimization of smart energy systems, demand response, transactive energy, and machine learning. He has taken charge of 6 research projects, including a project of National natural science foundation of China, Postdoctoral program of international training plan for young talents of Guangdong, Guangdong basic and applied basic research foundation, China postdoctoral science foundation, and so on. He has published more than 40 peer-reviewed SCI/EI papers. he is also the reviewer of different academic journals, including IEEE Transactions on Smart Grid, Applied Energy, International Journal of Electrical Power & Energy Systems, Energy, etc.

 

Invite Speaker X

Pulin Cao
Kunming University of Science and Technology, China

Title: The Application of Traveling Wave Wideband Feature: Identification of Fault Induced Traveling Wave in Complex Multi-section Grid
Abstract: As the speedy expansion of power gird, the traveling wave fault locator in substation may not always connect to newly added transmission lines in time, which reduces the effectiveness of fault locator. In this research project, an adjacent fault-free line based fault location scheme for newly additional line is proposed. Since the traveling wave from adjacent fault-free line results in severe difficulty for identification of traveling waves from faulty line, the time-frequency matrix is performed to screen traveling waves. Firstly, in order to remove the influence of waves from remote terminals of adjacent fault-free line, the current refraction coefficient is theoretically analyzed. Due to the negative refraction coefficient, traveling waves from remote terminals of adjacent fault-free line can be reduced by the proposed composite traveling wave. Secondly, the time-frequency matrices of composite traveling wave and original traveling wave is obtained by S-transform. Then, these matrices are applied to form ratio matrix which thoroughly eliminated waves from remote terminals of fault-free lines. Moreover, the robustness of the proposed fault location scheme is thoroughly validated by the simulation of PSCAD/EMTDC. Finally, the proposed fault location method has the great accuracy in simulation tests and field data test.

 

Biodata: Pulin Cao received the B.Eng. degree in electrical engineering from the South China University of Technology, Guangzhou, China, in 2009, and the Ph.D. degree in electrical engineering from the Kunming University of Science and Technology, Kunming, China, in 2015. He is currently an associate professor in the Faculty of Electric Power with the Kunming University of Science and Technology. His current research interests are fault location, overvoltage, and data fusion. He has taken charge two projects of National natural science foundation of China, and a Yunnan Fundamental Research Project, and so on.

 

Invite Speaker XI

Yu Liu
Shanghai Tech University, China

Title: Modeling, Protection and Fault Location of Transmission Lines Considering Frequency Dependent Line Parameters
Abstract: With increasing penetration of renewables, the electromagnetic transients during faults become more severe and unusual. These transients contain information within a wide range of frequency. In this case, frequency dependent line parameters will greatly affect the modeling accuracy and also the performances of protection and fault location of transmission lines. First, several modeling ideas to consider the frequency dependent parameters are introduced. Next, various designs of protection and fault location principles considering line frequency dependent parameters are presented. The results validate the importance to consider frequency dependent line parameters.

 

Biodata: Yu Liu received the B.S. and M.S. degrees in electrical engineering from Shanghai Jiao Tong University, in 2011 and 2013, respectively, and the Ph.D degrees in electrical engineering from Georgia Institute of Technology, in 2017. He is currently a tenured associate professor with ShanghaiTech University, Shanghai, China. He is also the chair of the Center of intelligent Power and Energy Systems (CiPES) in School of Information Science and Technology, ShanghaiTech University. His research interests include power system modeling, protection, fault location and state/parameter estimation. He has published more than 110 peer-reviewed SCI/EI papers. He serves as the Associate Editor of IET Renewable Power Generation and the Guest Editor of MPCE. He is recipient of the Shanghai Eastern Scholar Professorship and Shanghai Pujiang Scholar. He is the course director of Shanghai Municipal "First Class" Undergraduate Course "Electric Circuits". He is the PI of the research projects funded by the general program and the youth program of the National Natural Science Foundation of China.

 

Invite Speaker XII

Xing He
Shanghai Jiao Tong University, China

Title: Spatial-temporal Data Utilization Based on DT and Metaverse for Power Grid
Abstract: Digital twin (DT) has been proved as one of the most promising technologies on routine monitoring and management of a complex system with high-uncertainties. Our research gives an exploration on DT and virtual twin (metaverse) in our power system domain. We, therefore, provide a concise yet comprehensive tutorial on DT/metaverse overarching framework as a fully functioning template involving engineering background, basic features, technical roadmap, key technologies, main ingredients, advanced functions, and potential applications. Concerning with heterogenous spatial-temporal data, data utilization methodology based on random matrix theory (RMT) is highly focused. The superiorities of our work are discussed concerning with intelligent DER scheduling, including model-free mode, solid theoretical foundation, and robustness against ubiquitous uncertainty and partial data error. It serves as a powerful approach to achieving digitalization and intelligence in power systems.

 

Biodata: Xing He received his PhD in electrical engineering from Shanghai Jiao Tong University in 2017. He is currently an associate research fellow at the department of Electrical Engineering, Shanghai Jiao Tong University. His research work in the field digital transformation of power system concerning with spatial-temporal data analytics, digital twin/metaverse technology. Dr. He has published more than 10 papers on IEEE Trans. He is the corresponding author for two international book chapters published by IET, Cambridge U. Press, respectively. In addition, Dr. He also completes 1 book on DT. In 2020, Dr. He is the convener and chair of the 3rd Youth Forum on Energy Innovation (special issue on DT). He is also one of the Guest Editors for the journal of Power System Technology’s special issue on Digital twin technology and its application in Power systems. In 2021, he is awarded by IEEE PES China Chapter Council as Outstanding Young Engineer.