Prof. Gianfranco Chicco
Politecnico di Torino, Italy
Gianfranco Chicco holds a Ph.D. in Electrotechnics Engineering and is a Full Professor of Electrical Energy Systems at Politecnico di Torino (POLITO), Italy. He is a Fellow of the IEEE and the past Chair of the IEEE Italy Section (2023-2024). He received the title of Doctor Honoris Causa from the Universities Politehnica of Bucharest and Technical University “Gheorghe Asachi” of Iasi (Romania) in 2017 and 2018, respectively. He is the Scientific Responsible of the research group on Power and Energy Systems at POLITO, and the Responsible of the Torino unit of the Italian Inter-University Consortium ENSIEL. He is the Editor-in-Chief of Sustainable Energy Grids and Networks. He was the Conference Chair or a co-Chair of WESC 2006, IEEE ISGT Europe 2017, UPEC 2020, IEEE Eurocon 2023, SEST 2024 and IEEE SmartGridComm 2024. His research topics include Power System Analysis, Distribution System Analysis and Optimization, Electrical Load Management, Multi-Energy Systems, Data Analytics, Artificial Intelligence Applications to Power and Energy Systems, Renewable Energy Sources and Distributed Generation, and Power Quality. His International scientific production includes two books, five book chapters, over 120 journal publications, and over 150 publications in conference proceedings.
Title: Data Handling for Photovoltaic System Analysis
Abstract: The diffusion of renewable energy sources is a substantial part of the energy transition in progress. Photovoltaic (PV) systems are being developed in various sizes, from small rooftop solutions to large systems connected to transmission grids. Data collection and processing are essential for analysing PV systems for a variety of purposes, from assessing solar irradiance to verifying the effective PV production by taking into account all the data streams that contribute to determining the output power and energy delivered to the grid. The data relevant to PV system analysis are provided in multiple formats, with variable temporal resolution, possible incomplete time series, and missing inputs concerning the installation of the PV arrays in the system. Furthermore, managing PV-related data requires specific attention to comparing data from different seasons. For this purpose, normalization procedures can be used to make irradiance data comparable across seasons and days, considering clear-sky amplitudes and sunrise and sunset times to define normalized values. Under comparable conditions, it is possible to summarize the types of days by applying clustering algorithms. Further attention has to be paid to the specific selection of error indicators for analysing the performance of forecasting approaches, avoiding amplifying error rates during periods of low irradiance or production. Data handling aspects will be addressed with reference to measured and forecast values, as well as the outcomes of completed and ongoing projects.
Prof. Dr. Reinaldo Tonkoski Junior, Germany
Prof. Dr. Reinaldo Tonkoski Junior holds the Chair of Electric Power Transmission and Distribution at the Technical University of Munich, a position he has held since 2023. His career spans North American academia and national laboratories, including endowed professorships at South Dakota State University and the University of Maine, and a visiting faculty appointment at Sandia National Laboratories focused on grid integration of renewables and energy storage. He received his Ph.D. in Electrical Engineering from Concordia University, Canada (2011), following graduate and undergraduate degrees from PUC-RS, Brazil. His research addresses the modeling and control challenges introduced by high penetrations of power electronic converters in modern power grids. Prof. Tonkoski serves as Editor of the IEEE Transactions on Sustainable Energy and as Associate Editor of the IEEE Systems Journal and IEEE Access.
Enabling the Energy Transition: Data-Driven Modeling and Control of Converter-Dominated Grids
Abstract: The widespread deployment of power electronic converters is fundamentally reshaping grid operating conditions — yet the controls and EMT representations of grid-following and grid-forming inverters remain largely proprietary, leaving system operators to assess stability with incomplete models. Structural gaps in converter behavior and voltage control were highlighted in ENTSO-E’s investigation of the April 2025 Iberian blackout, underscoring the urgency of the problem.
This keynote presents probing-based system identification as a bridge between physics-based and data-driven approaches: targeted excitation at converter terminals, measurement of the dynamic response, and extraction of validated reduced-order models suitable for stability studies and controller design. Drawing on work spanning automated inverter dynamics extraction, dynamic phasor model validation, and real-time power hardware-in-the-loop simulation, Prof. Tonkoski discusses where these methods have matured, where open challenges remain, and what coordination across TSOs, OEMs, and academia is required for deployment at scale.