Biography

Pedro P. Vergara is an assistant professor at the Intelligent Electrical Power Grids (IEPG) group at the Delft University of Technology, in the Netherlands, since October 2020. Before joining TU Delft, he was with the Eindhoven University of Technology, in the Netherlands as a postdoctoral researcher, in which he was involved in several Dutch-funded projects with industry and academic partners. He received his B.Sc. degree (with honors) in electronic engineering from the Universidad Industrial de Santander, Bucaramanga, Colombia, in 2012, and the M.Sc. degree in electrical engineering from the University of Campinas, UNICAMP, Campinas, Brazil, in 2015. In 2019, he received his Ph.D. degree from both institutions, the University of Campinas, UNICAMP, Brazil, and the University of Southern Denmark, SDU, Denmark. Dr. Pedro P. Vergara devotes his research to developing new mathematical programming models and data-driven approaches to operate electrical distribution systems with high penetration of low carbon energy resources (i.e. PVs, EVs, electric heat-pumps, etc). 

Currently, he is involved in the RVO-funded ROBUST project, while leading developments in the H2020 MAGPIE project, NWO DATALESS project and NWO ALIG4Energy project, which involve several Dutch and European industry and academic partners. Dr. Vergara has received the Best Presentation Award in the Summer Optimization School in 2018 organized by the Technical University of Denmark (DTU) and the Best Paper Award in the 3rd IEEE International Conference on Smart Energy Systems and Technologies, Turkey, in 2020. He has more than 30 scientific publications in international leading conferences and journals. He also serve as Guest Editor in special issues related to active distribution networks in the IEEE MPCE Journal and IEEE Transactions on JPV Journal.

More information about Dr. Pedro P. Vergara's output is available in his personal webpage here.

Expertise

• Electrical distribution system planning and operation
• Mathematical programming applied to distribution systems
• Single- and multi-phase power flow formulations
• Machine Learning (ML) for distribution systems
• Integration of renewable energy resources at distribution level 

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