CPEEE 2026-Invited Speakers

Prof. Luigi Martirano, Dept. of Electrical and Energetic Engineering, Sapienza, University of Rome, Italy

• Luigi Martirano is a Full Professor of Electrical Power Systems, Faculty of Engineering, University of Rome "Sapienza". He received the M.S. and Ph.D. degrees in Electrical Engineering in 1998 and 2003, University of Rome, Italy. He’s actually the Chair of the Energy Engineering Master Degree, the Coordinator of the Electrical Area of the Department of Electrical and Energy Engineering (DIEE) and the responsible of the Laboratory of Electric Power Systems and Building Automation. He was the Coordinator of the PhD School "Engineering and Applied Science for Energy and Industry".

  He has authored more than 300 papers in international journals and conferences. His research activities cover power systems design, planning, safety, protection and coordination, microgrids, smart grids, industrial and commercial power systems, energy communities, renewables, building automation, lighting systems, and energy savings.

• Member of the boards of master’s degree programs: Electrical Engineering Degree, Energy Engineering Degree, Safety Engineering Degree and of the Master in Lighting Design MLD. Director of the advanced training course entitled "Management of electrical safety". Teaching activity: - Electric power systems of distribution and utilization; - Microgrids; - Power Systems in Smart Buildings; - Domotics and Building Automation; - Electrical Design in BIM.
  He’s Scientific Principal Investigator of many research projects.
  Active in the Institute of Electrical and Electronics Engineers IEEE.  Past member at large of the board of Industry Application Society IAS, Vice Chair of IAS Italy Section. Member of the AEIT (Italian Association of Electrical and Electronics Engineers).  He’s an Expert Member of the CEI (Italian Electrical Commission) Technical Committees CT 205 (Home and Building Electronic Systems) and CT3D (BIM in electrical systems). President of CT 315 (Energy Efficiency). Expert member of the European Technical Committees CENELEC CT 205 “Home and Building Electronic Systems (HBES)”, CEN CENELEC, CEN/CLC/JTC 11 “Accessibility in the built environment”, CEN/CLC/JTC 14 “Energy management, energy audits, energy savings” and CEN/CLC/JTC 15 “Energy Measurement Plan for Organization”.
  Associate Editor of IEEE Transactions on Industry Applications, and IEEE Open Journal of Industry Applications.

  Founder and partner of the university startup DREAM Domotic Renewable and Energy Management which operates in the field of building automation and smart buildings.
• Speech Title: Flexibility And User’S Aggregations. Microgrid Configurations For Energy Communities: The Power Sharing Model
• Abstract: Energy communities offer an effective model for facilitating renewable energy sharing and enhancing self- consumption within distributed systems. While traditional regulations accommodate virtual microgrids, they often lack fair and clear criteria for distributing shared energy and governing control. The speech proposes an innovative Power Sharing Model (PSM). It includes a comparative analysis of physical and virtual microgrids, presents the PSM's implementation, and reports on the first results obtained from the LAMBDA microgrid project. In the LAMBDA Lab flexibility is enhanced by a smart microgrid with PV, energy storage, vehicle to grid and smart solutions.
  The speech includes the Lab presentation and a comparison between physical and virtual microgrids; definition of the Power Sharing Model; implementation of a physical LVDC microgrid at LAMBDA; simulation and experimental validation using real consumption profiles and PV generation.
  Results confirm limitations of current virtual sharing models and demonstrate improved fairness, self- consumption, load-shifting, and stability using PSM. Physical tests at LAMBDA validate increased renewable utilization and effective real-time user engagement through Power Alert devices.
  The proposed PSM overcomes regulatory limitations of virtual communities, enabling fair energy allocation and increased renewable integration. Future work includes predictive models, scalability studies, and integration with EV charging strategies.

 

Assoc. Prof. Takuji Matsumoto, School of Environment and Society, Institute of Science Tokyo, Japan

• Takuji Matsumoto (Member, IEEE) is an Associate Professor at the School of Environment and Society, Institute of Science Tokyo, Japan. He received his B.Eng. from the University of Tokyo, Japan, in 2005, followed by an MS in Technology and Policy from the Massachusetts Institute of Technology, USA, in 2012, and a Ph.D. in Business Administration from the University of Tsukuba, Japan, in 2020. He was a visiting PhD student at the London Business School, UK. Previously, he was a senior researcher at the Central Research Institute of Electric Power Industry, Japan. He has about 15 years of work experience in government agencies and a private think tank, mainly in the energy sector, where his work included policy evaluation, market analysis, and risk management consulting. He is the first author of several peer-reviewed journal articles, particularly in top-tier journals such as IEEE Transactions on Power Systems and Energy Economics. His research interests include electricity market analysis, energy finance, statistical modeling, and forecasting.

• Speech Title: Forecasting Methods and Their Applications in Electricity Markets
• Abstract: Forecasting spot electricity prices has become increasingly important for power utilities, particularly in the face of growing market volatility and uncertainty. While advanced methods, including machine learning, are being widely applied, using complex algorithms alone does not guarantee practically effective or reliable forecasting.
  This talk explores the balance between interpretability and predictive performance, highlighting several representative models and their relevance to electricity market forecasting. These include regression-based approaches such as LASSO, Ridge Regression, and pcLasso, which builds on the strengths of the former methods to better capture structured information in the data.
  We will also examine probabilistic forecasting approaches, including Quantile Regression, the quantile prediction model as its extension, and GAMLSS, with a focus on their ability to model uncertainty in key market variables and events such as price spikes.Finally, I will present selected findings on the integration of forecasting and optimization in electricity trading, emphasizing how predictive models can support decision-making under uncertainty. The presentation aims to provide perspectives that inform both practical implementation and further research in electricity market forecasting.

 

Asst. Prof. Yu-Jen Chen, Southern Taiwan University of Science and Technology (STUST), Taiwan

• Dr. Yu-Jen Chen is an Assistant Professor in the Department of Mechanical Engineering at Southern Taiwan University of Science and Technology (STUST). He specializes in the design of fluid-based renewable energy systems, with research interests that include low-speed, high-torque axial flux permanent magnet (AFPM) generator design, fluid machinery energy conversion, digital twin, and intelligent predictive maintenance systems. Dr. Chen has led multiple interdisciplinary projects on green energy applications and aquaculture sustainability, integrating digital sensing and microgrid technologies into local communities. He has published extensively and presented his work in international journals and conferences related to renewable and clean energy. In addition to academic research, Dr. Chen actively promotes innovation and entrepreneurship education, guiding student teams to develop practical renewable energy solutions and organizing the Taiwan Collegiate Wind Competition (TCWC).
• Speech Title: Application, Development and Analysis of Low-Speed, High-Torque Axial Flux Permanent Magnet Generator for Fluid Machinery Renewable Energies
• Abstract: This presentation introduces the design, development, and performance analysis of a low-speed, high-torque axial flux permanent magnet (AFPM) generator. The proposed generator adopts a series-stator configuration equipped with air-cored windings and NdFeB permanent magnets, enabling direct-drive operation without a gearbox, compact structure, and high efficiency under variable-speed conditions. Experimental verification confirms its linear voltage–speed characteristics and stable electromagnetic behavior, achieving an efficiency range of 80% to 92% under various load conditions. The study further examines the generator’s modular structure and electromagnetic symmetry, demonstrating adaptability to multiple fluid machinery environments. By integrating digital-twin monitoring and predictive maintenance systems, this research aims to enhance the reliability and sustainability of distributed renewable energy systems. The findings contribute to advancing high-performance AFPM technology and its practical implementation in hybrid microgrid and net-zero energy applications.
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• Prof. Dr.-Ing. Amir Ebrahimi, Institute for Electrical Drives, Power Electronics and Devices – University of Bremen, Germany

  • Amir Ebrahimi received his PhD in the field of electrical machines and drives from the University of Stuttgart. Subsequently, he served as the group leader for electromechanical drive systems and wearable robotics at the Fraunhofer Institute IPA. From 2017 to 2023, he held the position of Junior Professor for Electrical Machines at Leibniz University Hannover, where he founded the Vector theory of rotating electrical machines. Since July 1, 2023, he has been appointed as full professor and is the head of the Department of Electrical Drives and Power Electronics at the Institute of Electrical Drives, Power Electronics and Devices at the University of Bremen. His research interests encompass electric drives, renewable energy (with a specific focus on wind turbines and hydrogenerators), mechatronics (particularly wearable robotics), electromobility (including electric vehicles and electric flying), electric machines, and power electronics.

  • Speech Title: AI-Driven Sensor Fusion for State and Fault Diagnostics in Electrical Machines
  • Abstract: Predictive maintenance of electrical machines using Artificial Intelligence (AI) represents a transformative approach to industrial reliability and efficiency. Instead of relying on scheduled maintenance or reacting to unexpected failures, AI-driven predictive maintenance enables early detection of faults and performance degradation through continuous data monitoring and intelligent analysis. This not only reduces downtime and maintenance costs but also extends the lifespan of critical equipment.A key factor in achieving accurate predictions lies in the proper design of the sensor system. Sensors serve as the primary data source, capturing essential parameters such as vibration, temperature and current signals. Defining the right sensor types, placements, and sampling strategies ensures that the collected data truly reflects the machine’s operational condition. Furthermore, sensor fusion— the integration of data from multiple sensors— enhances diagnostic accuracy by combining complementary information. Through AI algorithms, such fused data enables more reliable fault detection and classification, even in complex or noisy environments.

Asst. Prof. I-HSIEN LIU, National Cheng Kung University, Taiwan

• Dr. I-Hsien Liu is an assistant professor in the Department of Electrical Engineering and the Master Program in Cyber-Security Intelligence at National Cheng Kung University. His research focuses on industrial control systems, network security, computer networks, and cybersecurity testbeds. He is the deputy director of Taiwan Information Security Center at National Cheng Kung University (TWISC@NCKU). As a core member of the Taiwan Information Security Center at National Cheng Kung University, he plays a key role in developing critical infrastructure security testbed. Leveraging this testbed, his team has developed various protection technologies, acquired multiple invention patents, and assisted government agencies in strengthening the security of their control systems. His contributions have been widely recognized, including awards of excellence team, and the Best Popularity Award at the 2022 Annual Results Presentation from the National Science and Technology Council’s Advanced Information Security Technology Project. Moving forward, Dr. Liu continues to advance cybersecurity research, aiming to enhance the resilience and security of critical infrastructure systems.

• Speech Title: Blockchain-Driven Intelligence Reservoir Control and Safety
• Abstract: Reservoirs are vital for water resource management but face unprecedented challenges from extreme weather and cyberattacks targeting their critical control systems. Traditional operations, often reliant on manual processes, suffer from personnel shortages and inefficiencies, struggling to balance security, efficiency, and sustainability. To address these vulnerabilities, our team has developed some innovations technology base on  cybersecurity testbed to enhance the reliability, intelligence, and resilience of reservoir control systems.
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 Prof. Rajesh Karki, University of Saskatchewan, Canada

• Dr. Rajesh Karki is a professor in the department of Electrical & Computer Engineering at the University of Saskatchewan in Canada. He leads the Reliability Research Lab in the Power System Research Group. He is a Fellow of Engineers Canada. He specializes in the area of power system reliability and value-based reliability investment with over 30 years of experience in research, application, education and consulting work for electric power industries and academic/research institutions. His research includes renewable energy, storage, smart-grid initiatives and electricity market implications on the environmental compliance, efficiency, reliability and resiliency of power systems.  He has published over 160 technical papers, 5 books, numerous book chapters and technical reports in this field.

• Speech Title: Affordability and Reliability of Sustainable Energy Systems
• Abstract: A global sustainable future is being increasingly threatened by adverse environmental impacts of global warming. This is believed to be caused primarily by greenhouse gas emissions. Electricity generation and transportation are identified as the two major sectors responsible for green-house gas emissions. Governments around the world are, therefore, making considerable efforts to promote renewable energy and transition to electric vehicles to mitigate the adverse environmental effects. A significant growth in electricity generation is anticipated to meet the demands of population growth, economic growth, electrification of transportation sector, and consumption growth due to prosperity. The growth in demand must be accompanied by adequate investment in renewable energy generation technologies in order to ensure a reliable supply of electricity to the consumers. Lack of supply reliability results in excessive power outage costs to residential, commercial and industrial consumers, and “lost opportunity” costs for economic development. As renewable sources such as wind and solar are highly variable and uncertain, investment in supporting technologies, such as energy storage systems and smart monitoring/control of energy supply and demand are also necessary to maintain acceptable reliability. The investment needed in renewable energy resources and support technologies, to meet the target reliability, will substantially increase causing serious concerns on affordability of reliable power supply. This talk discusses the challenges in achieving reliable energy supply at affordable costs as we move towards a sustainable energy future.
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• Assoc. Prof. Ahmed Antar Mahmoud Hawwash, Benha University, Benha, Egypt

  • Ahmed Antar Mahmoud Hawwash is a JSPS Postdoctoral Fellow at the Department of Chemical Science and Engineering, Institute of Science Tokyo, Japan, where he has been working since June 2024 on the thermal management of lithium-ion batteries. He is also an Associate Professor in the Mechanical Engineering Department, Faculty of Engineering, Benha University, Egypt. His research group focuses on thermal management and energy storage systems. He received his Ph.D. in 2020 and my Master’s degree in 2016 from the Energy Resources Engineering Department at the Egypt-Japan University of Science and Technology (E-JUST). During his Ph.D. studies, he spent nine months as an exchange student at Tokyo Institute of Technology, conducting research on thermochemical energy storage. His Master’s research focused on enhancing the efficiency of solar thermal collectors. To date, he has published 22 peer-reviewed journal articles and 4 international conference papers.

  • Speech Title: Advanced Thermal Management Systems for Integrated Electric Vehicle Battery Packs
  • Abstract: The worldwide transition to sustainable transportation positions the electric vehicle (EV) as the basis of environmental mitigation strategies. The lithium-ion battery (LIB) technology is at the heart of this shift and is prized for its longevity and energy density. LIB is sensitive to the thermal fluctuation produced by charging and discharging. While the development focuses on high-performance output, ultra-fast charging, and long lifetime, advanced thermal management systems (TMS) have transitioned from a secondary design consideration to a prime engineering imperative. This keynote presentation provides a strategic overview of the thermal management landscape, analyzing the critical evolution of cooling and heating technologies. The presentation highlights the heat generation mechanism and thermal propagation, followed by state-of-the-art cooling approaches, including air, liquid, PCM, and hybrid thermal systems. Attendees will gain insights into passive, active, and hybrid thermal regulation, alongside an evaluation of how these advancements impact the economic and safety profiles of next-generation EVs. Challenges and future research directions towards cost-effective, scalable, and sustainable thermal management solutions for next-generation EV are discussed.
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  • Prof. Xiaodong Li, Macau University of Science and Technology, China
    • Xiaodong Li received the B.Eng. degree in electrical engineering from Shanghai Jiao Tong University, Shanghai, China, in1994, and the M.A.Sc. and Ph.D. degrees in electrical engineering from the University of Victoria, Victoria, BC, Canada, in 2004 and 2009, respectively. From 1994 to 2002, he was an Electrical Engineer with Hongwan Diesel Power Corporation, Zhuhai, China, where he conducted maintenance of the diesel power generation system. He joined the Faculty of Innovation Engineering, Macau University of Science and Technology, Macau, China, in 2009, where he is currently a Professor. His research interests include high-frequency power converters and its applications, AI applications in Smart Grid and Wind Power prediction. He has published more than 100 journal and conference papers with over 5000 citation (data from Google Scholar). He also holds four US patents and five Australia Innovation Patents. He is on the list of “the World's Top 2% Scientists” by Elsevier and Stanford University since 2022. He was a recipient of Industry Postgraduate Scholarship (IPS) from Natural Sciences and Engineering Research Council of Canada (NSERC) the IEEE Power and Energy Society Best Paper Prize in 2007 and the BOC Excellent Research Award from the Macau University of Science and Technology in 2013. Dr. Li is a senior member of IEEE, Chair of IEEE Macau Section in 2022-2026.
  • Speech Title: Optimal Transient Control in Dual-Active-Bridge Bidirectional Converters
  • Abstract: Dual active bridge (DAB) converters are widely used in electric vehicles and DC transmission networks to connect two DC buses with different voltage levels. Their operation often requires rapid changes in the magnitude and direction of power. Phase-shift control is commonly adopted in dual-bridge converters. Under an instantaneous power command change, improper adjustment of the phase shift may lead to transient overcurrent and a long settling time. Therefore, this study focuses on regulating the phase-shift angles of different switching devices during fast power and direction transitions, enabling them to switch from one steady state to another as quickly as possible, without introducing DC bias current, overcurrent, or overvoltage. In this research, we present a detailed methodology for both conventional dual-bridge converters and resonant dual-bridge converters, covering investigations from open-loop to closed-loop control.
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• Assoc. Prof. NOELYN M. DE JESUS, Batangas State University ARASOF-Nasugbu, Philippines
  • Dr. Noelyn M. De Jesus is an Associate Professor at the College of Informatics and Computing Sciences, Batangas State University ARASOF-Nasugbu. She holds a Doctor in Information Technology and is actively engaged in teaching, research, and extension services. Her scholarly work centers on the application of artificial intelligence, machine learning, data analytics, and intelligent systems across diverse domains. Her research contributions span predictive modeling, deep learning, decision-support systems, technology adoption, and optimization-driven analytics. She has authored and co-authored studies involving neural network-based forecasting, spatiotemporal machine learning models, AI-driven decision frameworks, educational technology acceptance, tourism analytics, and data-driven performance monitoring systems. Her interdisciplinary research reflects a strong focus on leveraging computational intelligence to address complex real-world challenges in energy systems, education, tourism, manufacturing, and organizational information systems. Dr. De Jesus continues to pursue research initiatives that integrate artificial intelligence with applied analytics, emphasizing predictive intelligence, system efficiency, and data-driven decision-making.

• Speech Title: The Role of AI-Based Load Forecasting in Enhancing Power System Efficiency in the Philippines
• Abstract: Electrical load forecasting is a well-established research domain that has been extensively studied, modeled, and applied. However, when examined within the context of developing power systems such as the Philippines, forecasting assumes a heightened level of operational significance. In such environments, forecasting accuracy extends beyond predictive performance, directly influencing procurement decisions, grid stability, reliability management, and operational risk. Even marginal forecasting errors may lead to tangible economic and system-level consequences. This presentation reframes load forecasting beyond algorithms and modeling techniques, emphasizing its role as a strategic intelligence function within power system operations. The discussion highlights the challenges encountered by utilities operating in dynamic, demand-sensitive environments, the operational implications of forecasting uncertainty, and the continuing importance of improving predictive reliability. In developing energy systems, forecasting accuracy is not merely a matter of prediction - it is a determinant of operational efficiency, economic stability, and system resilience.
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• Dr. Yulianta Siregar, Universitas Sumatera Utara, Indonesia
  • Yulianta Siregar was born July 09, 1978 in Medan, North Sumatera Utara, Indonesia. He did his undergraduate work at University of Sumatera Utara in Medan, North Sumatera Utara, Indonesia. He received a Bachelor of Engineering in 2004. After a while, he worked for a private company. He continued taking a master's program in Electrical Engineering at the Institute of Sepuluh Nopember, Surabaya, West Java, Indonesia, from 2007-2009. He was in a Ph.D. program at Kanazawa University, Japan, from 2016-2019. Until now, he lectured at Universitas Sumatera Utara.
• Speech Title: Analysis of the Effect of Salt Pollutants on Insulator Degradation and Evaluation of the Effectiveness of Epoxy Resin and Rtv Sir Based Protective Coatings
• Abstract: Power transmission and distribution system insulators function to isolate live and unlive parts. In Indonesia, outdoor insulators are generally made of ceramics and glass, which have weaknesses in humid and polluted conditions. Pollutants on the insulator surface and wet weather increase leakage current, which causes losses in the form of heating and insulation failure. The solution to insulator resistance to humidity and pollutants is hydrophobic coating. This study examines the effect of RTV silicone coating, epoxy resin, and air humidity on leakage current, voltage distribution of chain insulators in clean and polluted conditions, and the effect of pollutants on insulator performance. Laboratory experimental methods show that the leakage current of the insulator before being coated is in the range of 196.9 μA - 968.2 μA, after being coated with RTV silicone in the range of 92.5 μA - 645.3 μA, and epoxy resin in the range of 93.3 μA - 548.8 μA. The average reduction in leakage current by silicone RTV coating is 48.2%, and epoxy resin is 44%. Leakage current increases with air humidity and the weight of pollutants attached. The analysis shows that the coating worsens the voltage distribution, as evidenced by the increase in the flatness factor of the voltage distribution in polluted and humid conditions.