Data-driven optimization for distributed energy resources

Curso

Data-driven optimization for distributed energy resources

Departamento de Ingeniería Eléctrica y Electrónica
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Data-driven optimization for distributed energy resources

Integration of Distributed Energy Resources (DER) contributes to the decarbonization of current power systems and allows customers to take an active role by consuming and providing grid services as prosumers.  These new scenarios require the modernization of distribution networks to increase their reliability, interoperability, security, flexibility, and efficiency.  In this context, decision-makers can combine advanced optimization modeling techniques to coordinate the integration of DERs properly and to guarantee the balance between supply and demand. This course aims to introduce general aspects of data-driven optimization techniques for the sizing and management of DERs in the context of modern power systems.

This course is the first module of the course Intelligent management of distributed energy resources (DER), participants will share classroom with regular Uniandes students. 
For more information of the complete course click here

Este curso hace parte del programa Intelligent management of distributed energy resources (DER). Ver más aquí.

Dirigido a

Professionals with degrees that involve a strong background in mathematics, probability, and programming such as engineering, economics, physics; senior undergraduate students and graduate students that are interested in applying data-driven optimization in distributed energy resource management. Background in probability, calculus, and programming is required.

Objetivos

At the end of this course the student will be able to: 
  • Know the basic concepts of distributed energy resources (DER).
  • Know how to use data-driven distributed and multi-objective optimization for resource energy management. 

Metodología

We have divided the course into 10 units. Two units will be presented in each four-hour section. The main idea is to present application cases of machine learning and optimization problems in the field of renewable energy. The application cases will be focused on energy management problems. Aspects related to low-level power electronics and control will not be addressed in detail.    
The student will receive a certificate if he/she attends at least 85% of the course. There is no performance evaluation of the student. 

 

Contenido

The content is proposed as follows:
  • Introduction 
  • Distributed energy resources (DER)
    • PEM electrolyzers
    • PEM fuel cells
    • PV 
    • Wind turbines
    • Batteries 
  • Overview about optimization techniques
  • Modelling for uncertainty
    • Stochastic optimization
    • Chance-constrained optimization
  • Modelling multi-objectivity
    • Pareto optimality
    • Scalarization
  • Sizing and operation of DER
  • Addressing complexity
  • Distributed optimization
    • Cost-coupled and constraint-coupled problems
    • Consensus 
  • Games theory framework
    • Non-cooperative games
    • Stackelberg model
  • Perspectives about smart electric power systems
    • Multi-agent systems 
    • Transactive systems

Profesores

Kodjo Agbossou

Kodjo Agbossou (Senior Member, IEEE) received the B.S., M.S., and PhD degrees in electronic measurements from the Université de Nancy I, France, in 1987, 1989, and 1992, respectively. He is currently the Hydro-Québec Research Chairholder on Transactive Management of Power and Energy in the Residential Sector, and the Chair of the Smart Energy Research and Innovation Laboratory of Université du Québec à Trois-Rivières (UQTR). He was the Head of Engineering School, UQTR, from 2011 to 2017. He was the Head of the Department of Electrical and Computer Engineering Department, UQTR, from 2007 to 2011. He was also the Director of Graduate Studies in Electrical Engineering, UQTR, from 2002 to 2004. He was a Postdoctoral Researcher (1993–1994) with the Electrical Engineering Department, UQTR, and was a Lecturer (1997–1998) at the same department. He is the author of more than 325 publications and has four patents and two Patent Pending. His present research activities are in the areas of renewable energy, the use of hydrogen, Home demand-side management (HDSM), integration of energy production, storage and electrical energy generation system, the connection of electrical vehicle to the grid, control and measurements. He is a member of the Hydrogen Research Institute and Research group “GREI” of UQTR. Since 2015, he has been the Sub-Committee Chair on Home and Building Energy Management of Smart Grid Technical Committee,” IEEE Industrial Electronics Society (IES).

Nilson Henao

Nilson Henao received his B.S. degree in Electronics Engineering from the Universidad de los Llanos, Villavicencio, Colombia, in 2010, his M.Sc. degree in 2013 and his PhD degree in 2018 in Electrical Engineering from the University of Quebec at Trois-Rivieres (UQTR), QC, Canada. His research interests are related to machine learning methods for power system applications like residential load optimization, load monitoring and diagnosis, and plug-in electric vehicles.

Juan Carlos Oviedo

He is an Engineer and PhD in electrical engineering from the Universidad Industrial de Santander. During his doctoral studies, he completed two research internships at the Université du Québec à Trois-Rivières, at the Hydrogen Research Institute (Canada) and one at the Université de Technologie Belfort-Montbéliard at La Fédération de Recherche FCLAB (France), as well as collaboration with the Ontario Tech University (Canada). His PhD thesis contributed to the sustainable energy topic of the Misión de Sabios organized by the Government of Colombia. He is currently working as an industry research scientist at the Laboratoire des technologies de l'énergie (LTE) of Hydro-Quebec. His research interests lie in the integration of distributed energy resources, optimal sizing and operation of autonomous microgrids, transactive energy markets, and machine learning applications for smart grids.

Condiciones

Eventualmente la Universidad puede verse obligada, por causas de fuerza mayor a cambiar sus profesores o cancelar el programa. En este caso el participante podrá optar por la devolución de su dinero o reinvertirlo en otro curso de Educación Continua que se ofrezca en ese momento, asumiendo la diferencia si la hubiere.

La apertura y desarrollo del programa estará sujeto al número de inscritos. El Departamento/Facultad (Unidad académica que ofrece el curso) de la Universidad de los Andes se reserva el derecho de admisión dependiendo del perfil académico de los aspirantes.