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Pedro P. Vergara, Ph.D. 

About me 

I was born in Colombia. I received my B.Sc. degree (with honors) in electronic engineering from the Universidad Industrial de Santander, Bucaramanga, Colombia, in 2012, and my M.Sc. degree in electrical engineering from the University of Campinas, UNICAMP, Campinas, Brazil, in 2015. In 2019, I received my Ph.D. degree from both institutions, the University of Campinas, UNICAMP, Brazil, and the University of Southern Denmark, SDU, Denmark.  In 2019, I joined Eindhoven University of Technology, TU/e, in The Netherlands as a Postdoctoral Researcher.


In 2020, I was appointed as Assistant Professor at the Intelligent Electrical Power Grids (IEPG) group at Delft University of Technology, also in The Netherlands. My main research interests include the development of methodologies for control, planning, and operation of electrical distribution systems with high penetration of low-carbon energy resources (e.g, electrical vehicles, PV systems, electric heat pumps) using optimization and machine learning algorithms. I have 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.

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Spanish         English   
Portuguese   Dutch

 Research Interests

  • Active Distribution Networks

  • Machine Learning

  • Mathematical Programming

  • Digital Twins


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PhD in Electrical Engineering, 2019

University of Campinas, Brazil - University of Southern Denmark, Denmark

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MSc in Electrical Engineering, 2015

University of Campinas, Brazil

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BSc in Electronic Engineering, 2012

Universidad Industrial de Santander, Colombia




We currently have one Ph.D. position within the HE DRIVE2X Project. Apply directly at the TU Delft vacancy website. 


Our paper together with Vinicius B. Braga (UFF, Brazil) has been accepted for publication in the IEEE Trans. Smart Grid. Congratulations to Vinicius. 


Zeynab Kaseb has join our IEPG group as a Ph.D. student within the DATALESS Project. Welcome Zeynab to TU Delft. 


Our Horizon Europe proposal DRIVE2X has been granted. This proposal is led by LUT (Finland). Congratulations to all. We will have a Ph.D. position opening soon. 


We currently have one Ph.D. position opening within the ALING4Energy project. Apply directly at the TU Delft vacancy website. 

Research Projects

Research Projects
DRIVE2X Project: Delivering Renewable and Innovation to mass Vehicle Electrification enabled by V2X Technologies

This multidisciplinary four year project aims to establish a solid scientific base and stakeholder awareness for mass deployment V2X solutions. DriVe2X will develop new knowledge, tools, models, and technologies to cope with a V2X-based mass EV deployment in future. It will study and consolidate the understanding on the behavioural uncertainties linked to V2X and develop policy tools to support increasingly complex decisions on V2X roll-out in European smart cities. DriVe2X will implement advanced artificial intelligence techniques that efficiently capture the flexible energy potential from smart charging in building parking lots, homes, and charging stations, and match it with the distribution networks's localized needs in order to research dynamic marketplaces for exchanging and trading V2X flexibility locally. This project is lead by LUT in Findland. 

LIFE Project: Local Inclusive Future Energy City Platform

With the rise of more solar panels, electric vehicles or even more houses or energy intensive commercial buildings – like the Johan Cruijff ArenA - there is an increasing stress on our electricity network, especially as locally demand and supply are not simultaneous. These situations can be resolved by increasing the capacity of the network – which is very costly and time consuming – or can be met by exploring smart energy management solutions. At the Amsterdam ZO area there are premises that have events during which they use a lot of energy – during a football match or music concert. Next to the ArenAPoort there are residential areas facing severe energy poverty (houses that can’t afford renewable energy solutions or afford energy usage at all). Against the backdrop of further urbanisation in the Amsterdam ZO-region, designing and operating urban energy systems in an inclusive fashion – simultaneously battling technical and social challenges – is a major challenge of the energy transition. The researchers aim to develop a scalable energy exchange platform to resolve both grid challenges and foster the participation of local residents into energy challenges. Through the platform they want to engage both large customers and residential energy users. By setting up an exchange where data, energy, and other assets such as football tickets and JC ArenA battery capacity can be traded the researchers want to create a setup that is inclusive and facilitates sustainable urban development. The insight into how the exchange and other energy services will work will be simulated by setting up a digital twin. In this digital representation of the area relevant buildings and their energy behaviour will be simulated. These simulations allow local stakeholders to see and learn what data or assets they can trade and when is the best moment for doing so aiming to decrease the peak utilisation of the local grid.

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ALING4Energy: Aligning citizens and systems - Combining digital citizen engagement and personalised behavioural interventions to enable system-optimal clean energy investments at scale

The “Aligning citizens and systems - Combining digital citizen engagement and personalised behavioural interventions to enable system-optimal clean energy investments at scale” (ALIGN4Energy) project unites scientists from the humanities and social sciences (economics, psychology, political sciences) and technical sciences (computer science, energy systems modelling), as well as companies, municipalities, and NGOs to jointly work towards a quick and low-cost transformation to natural gas-free homes in the Netherlands. The consortium develops an online platform that serves as an adaptive digital decision-support system for citizens and policymakers, to help them make energy-related investment decisions that are simultaneously optimal for each individual citizen and for the energy system. Citizens will receive information that will simplify investment decisions and help them coordinate with neighbours for collective investments.

DATALESS: DATA-analytics for enhanced operation of Local Energy Systems from cyber-physical-social perspectives

The integration of a large number of distributed energy resources (DERs), such as distributed wind and PVs systems as well as the increasing electrification of the heating and transport sector (e.g., by integrating electric heat pumps and electric vehicles (EVs)) poses great challenges on distribution and transmission network operation. In this sense, local energy systems (LESs) and green buildings can represent the major means to meet the 2030 emissions reduction targets of the Climate Agreement for both, the Netherlands and China. The unresolved challenge remaining is how to integrate the thousands of controllable elements in LESs and green buildings into traditional control or optimization frameworks while still guaranteeing optimal system-level objectives (e.g., reduce renewable energy curtailment, minimize cost). We aim to answer this question in this project. To do this, we aim to develop a new data-driven operational paradigm for LESs from cyber-physical-societal perspectives, with a special focus on DERs integration, LESs and green building flexibility exploring, and actor integration.

MAGPIE: Smart Green Ports as Integrated Efficent Multimodals Hubs

The MAGPIE consortium, consisting of 4 ports (Lighthouse Port of Rotterdam, Fellow ports DeltaPort (inland), Port of Sines and HAROPA), 9 research institutes and universities, 32 private companies and 4 other institutes, forms a unique collaboration addressing the missing link between green energy supply and green energy use in port-related transport and the implementation of digitisation, automation, and autonomy to increase transport efficiency. MAGPIE accelerates the introduction of green energy carriers (batteries, hydrogen, ammonia, BioLNG and methanol) combined with realisation of logistic optimisation in ports through automation and autonomous operations. The main objective of MAGPIE is to demonstrate technical, operational, and procedural energy supply and digital solutions in a living lab environment to stimulate green, smart, and integrated multimodal transport and ensure roll out through the European Green Port of the Future Master Plan and dissemination and exploitation activities.

ROBUST: Robust sustainable electricity system through regional flexibility

ROBUST envisions an integrated flexibility system at the city-region level that can facilitate more and more renewable energy and electric transport in cities. For this, flexibility is available from flex sources such as smart and bidirectional charging of EVs, stationary batteries, heat pumps and heat storage. This project contributes to MMIP 5 of the Climate Agreement by making the step from flexibility in the sub-functions of living, working and mobility, to an integral flexibility system at the city-region level, and from pioneering in separate consortia to knowledge building in a broader partnership. The city regions of Utrecht and Arnhem are participating with a number of districts as research locations. Utrecht offers unique research and datasets thanks to 400 bidirectional charging points in the city. Research is underway to determine what the flex system should be at the district level based on flex supply and demand, and what the preconditions are (user acceptance, data security, policy and regulations) for upscaling to the city region level and for application in other city-regions. The desire is - thanks to jointly developed systems and (inter)national protocols - to prevent region-specific flex systems in the Netherlands.

PhD students

Current PhD Students

Deep Reinforcement Learning-Based Optimal Energy Systems Scheduling
Hou Shengren

 Funding  Chinese Scholarship Council (CSC)

Congestion Management in (Multi-Energy) Distribution Systems: A Fair and Stochastic Approach
Neda Vahabzad

 Funding  MAGPIE Project

Deep Learning-based Distribution Networks Modeling and Analysis
Zeynab Kaseb

 Funding  DATALESS Project

Distribution System Digital Twins: Accuracy and Uncertainty in State Estimation Models
Wouter Zomerdijk

 Funding  LIFE Project

To be defined
Weijie Xia

 Funding  ALING4Energy Project

To be defined
Nan Lin

 Funding  ALING4Energy Project

Data-Driven Operation and Optimization of Low Voltage Distribution Networks
Dong Liu

 Funding  Chinese Scholarship Council (CSC)

Linear vs. Non-linear: Reinforcement Learning for Distribution Networks Optimization
Shuyi Gao

 Funding  Chinese Scholarship Council (CSC)

To be defined
Zhisheng Xion

 Funding  Chinese Scholarship Council (CSC)

To be defined
Stavros Orfanoudakis

 Funding  DRIVE2X Project

Master Students

Graduated MSc Students

Smart Grid Integrated Vessel Through a Shore Power System
Thomas Weenk
Ampacity Estimation of Medium Voltage Cables for Changing Load Profiles and Uncertain Site Conditions
Bastiaan Crooijmans
Modelling and Topology Optimisation of Medium Voltage Representative Networks for The Netherlands
Marcel Brouwers
Optimal Energy Resource Control in Congested Areas: A Stochastic Resource Dispatch Optimization Model Considering Flexible Robust Constraints
Victor van Doorn
Data-Driven Distributionally Robust Optimal Power Flow
Dimitrios Fouskidis (Co-supervision)
Interplay Between LV Grids & Electric Vehicles’ Charging Flexibility
Flore Verbist
Reinforcement Learning-based Energy Management System for Smart Buildings
Nick van den Bovenkamp
Mitigating the Impacts of the Electric Vehicle Charging Infrastructure on Residential Grids
Waleed Nasr

Graduated BSc Students

Bachelor students
Sustainable & Smart Distribution Networks
P. Gommers, L. Ottink, D. van Dingstee, R. Eland, T. Banares, R. Steman
Distribution of the Electricity Grid of a Tiny House Community 
P. Kluge, J. Richter, A. Roozendaal, P. Santvliet, I. Bellouki, L.A. Pijnenburg


Critical Data Visualization to Enhance Protection Schemes for State Estimation

 Vinicius B. Braga Flor, Milton B. do Coutto Filho, Julio C. Stacchini de Souza, Pedro P. Vergara ,in IEEE Trans. on Smart Grids, 2022.

A Fixed-Point Current Injection Power Flow for Electric Distribution Systems using Laurent Series

Juan S. Giraldo, Oscar Danilo Montoya, Pedro P. Vergara, Federico Milano, in Power Systems Compuation Conference (PSCC) & Electric Power Systems Research (Elsevier), 2022

Community Energy Storage Operation via Reinforcement Learning with Eligibility Traces

Mauricio Salazar, Juan S. Giraldo, Pedro P. Vergara, Phuong Nguyen, Anne van der Molen, Han Slootweg, in Power Systems Computation Conference (PSCC) & Electric Power Systems Research (Elsevier), 2022

Optimal Dispatch of PV Inverters in Unbalanced Distribution Systems using Reinforcement Learning 

Pedro P. Vergara, Juan S. Giraldo, Mauricio E. Salazar, Peter Palensky, in International Journal of Power and Energy Systems, September 2021 [Open Access]

Sizing of an Autonomous Hybrid Microgrid Considering Droop Control

Juan M. Rey, Ivan Jiménez-Vargas, Pedro P. Vergara, German Osma-Pinto, Javier Solano, in International Journal of Power and Energy Systems, September 2021

A Linear ACOPF Formulation for Unbalanced Distribution Networks

Juan S. Giraldo, Pedro P. Vergara, Juan C. López, Nikolaos G. Paterakis, in IEEE Transactions on Industry Applications, 2021

Conditional Multivariate Elliptical Copulas to Model Residential Load Profiles from Smart Meter Data

Mauricio Salazar, Pedro P. Vergara, Phuong H. Nguyen, Anne van der Molen, Han Slootweg, in IEEE Transactions on Smart Grids, 2021 [Open Access]

Adaptive Sequential Droop Control for Voltage Rise Mitigation in PV-Rich LV Distribution Networks

T. T. Mai, Niyam A. N. M. M., Pedro P. Vergara, Phuong H. Nguyen, Guus A. J. M. Pemen, in Electric Power System Research, November, 2020 [Open Access]

A Comprehensive Assessment of PV Inverters Operating with Droop Control for Overvoltage Mitigation in LV Distribution Networks

Pedro P. Vergara, Mauricio Salazar, Tam T. Mai, Phuong H. Nguyen, Han Slootweg, in Renewable Energy, 2020 [Open Access]

Gaussian Mixture Based Uncertainty Modeling to Optimize Energy Management of Heterogeneous Building Neighborhoods: A Case Study of a Dutch University Campus

D. S. Shafiullah, Pedro P. Vergara, A.N.M.M. Haque, Phuong Nguyen, A. J. M. Pemen, in Energy and Buildings, 2020 [Open Access]

Droop-Free Hierarchical Control Strategy for Inverter-Based AC Microgrids

Juan M Rey, Pedro P. Vergara, Miguel Castilla, Antonio Camacho, Manel Velasco, Pau Marti, and Jaume Miret, in IET Power Electronics, 2020

A Stochastic Programming Model For The Optimal Operation Of Unbalanced Three-Phase Islanded Microgrids

Vergara, Pedro P., López, Juan C., Rider, M. J., Shaker, H.R., Da Silva, Luiz C. P, Jorgensen, B.N, in International Journal of Electrical Power & Energy Systems, 2019

A Generalized Model for the Optimal Operation of Microgrids in Grid-Connected and Islanded Droop-Based Mode

Vergara, Pedro P., Rey, J. M., López, J. C., Rider, M. J., da Silva, L. C. P., Shaker, H. R., Jorgensen, B. N., in IEEE Trans. on Smart Grid, 2019

Distributed Strategy for Optimal Dispatch of Unbalanced Three-Phase Islanded Microgrids

Vergara, Pedro P., Rey, J. M., Shaker, H. R., Guerrero, J. M., Jorgensen, B. N., da Silva, L. C. P., in  IEEE Trans. on Smart Grid, 2019

Optimal Management of Energy Consumption and Comfort for Smart Buildings Operating in a Microgrid

Pinzon, J. A., Vergara, Pedro P, Silva, L. C. P.,  Rider, M. J., in IEEE Trans. on Smart Grid, 2019

Optimal Operation of Radial Distribution Systems Using Extended Dynamic Programming

López, Juan C., Vergara, Pedro P., Christiano Lyra, Rider, M. J., Da Silva, Luiz C. P., in I, IEEE Trans. on Power Systems, 2018

Optimal Operation of Unbalanced Three-Phase Islanded Droop-Based Microgrids

Vergara, Pedro P., López, Juan C., Rider, M. J., Da Silva, Luiz C. P., in I, IEEE Trans. on Smart Grids, 2019

Security-constrained Optimal Energy Management System for Three-Phase Residential Microgrids

Vergara, Pedro P., López, Juan C., Da Silva, Luiz C. P., Rider, M. J., in Electric Power System Research, 2017

Comparative Analysis of Design Criteria for Hybrid PV/Wind/Battery Systems

Vergara, Pedro P., Da Silva, Luiz C P., Rey Juan, Ordóñez-Plata, Gabriel, in IET Renewable Power Generation, 2017

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