Curso actualmente no disponible
Data-driven optimization for distributed energy resources
Curso
Virtual
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ESIBE Escuela Iberoamericana de Postgrado
Maestría Virtual$ 5.729.166,67 $ 15.000.000,00*Precio estimado
Importe original en EUR:
1.375 € 3.600 €
-
International Career Institute
Curso Virtual$ 1.500.001-3.000.000 -
UIC Barcelona
Maestría Virtual$ 35.500.000
Descripción
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Tipología
Curso
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Nivel
Nivel intermedio
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Metodología
Virtual
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Horas lectivas
20h
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Duración
1 Semana
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Campus online
Sí
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Servicio de consultas
Sí
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Clases virtuales
Sí
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.
A tener en cuenta
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.
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.
Participó
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.
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Materias
- Management
- Introduction
- Integration
- Studies
- System
- Power
- Technologies
- Fundamentals
- Dimensioning
- Software available
- Distributed
Profesores
Ivan D. Serna
Profesores
Juan Carlos Oviedo
Profesores
Nilson Henao
Profesores
Programa académico
The content is proposed as follows:
Introduction- Course description.
- Cloud computing.
- Continuous integration and deployment (CI/CD).
- Energy Internet of Things (eIoT).
- Technical fundamentals of main technologies.
- Integration of DERs in the power system.
- Workshop: Case studies.
- Fundamentals.
- Principle of optimality.
- Dynamic programming.
- Convex programming.
- Workshop: Demand prediction, Bellman and CVXPY.
- Fundamentals.
- Software available.
- Sizing of isolated microgrids and DERs.
- Workshop: Sizing a storage system.
- Work: Sizing of a photovoltaic system with storage.
- Fundamentals
- Main distributed optimization methodologies.
- Workshop: Consensus and ADMM
- Fundamentals.
- Cooperative and non-cooperative games.
- Application to oligopolistic competition: Cournot and Stackelberg equilibrium (price leader and quantity leader).
- Fundamentals.
- Structure of energy markets.
- Primary electricity markets.
- Secondary electricity markets.
- Workshop: Example of energy auction.
- Information theory and systems entropy.
- Bayesian inference.
- Monte Carlo.
- Workshop: Prediction of demand with uncertainty.
- Facing complexity.
- Management of DERs using Transactional Energy.
- Management of DERs using multi-agent systems.
- Taller: Spade, Pymarkets.
Data-driven optimization for distributed energy resources
-
ESIBE Escuela Iberoamericana de Postgrado
Maestría Virtual$ 5.729.166,67 $ 15.000.000,00*Precio estimado
Importe original en EUR:
1.375 € 3.600 €
-
International Career Institute
Curso Virtual$ 1.500.001-3.000.000 -
UIC Barcelona
Maestría Virtual$ 35.500.000