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.
Este curso hace parte del portafolio de materias de pregrado y posgrado de la Universidad abiertas a todo público.
Al participar en este curso podrás vivir la experiencia Uniandina, acceder a contenidos de calidad, tomar clases con estudiantes regulares, acceder al sistema de bibliotecas de Uniandes y participar en las actividades culturales que esta Universidad te ofrece.
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
- Course description.
- Cloud computing.
- Continuous integration and deployment (CI/CD).
- Energy Internet of Things (eIoT).
- Distributed Energy Resources (DERs)
- Technical fundamentals of main technologies.
- Integration of DERs in the power system.
- Workshop: Case studies.
- Optimization techniques
- Fundamentals.
- Principle of optimality.
- Dynamic programming.
- Convex programming.
- Workshop: Demand prediction, Bellman and CVXPY.
- Dimensioning
- Fundamentals.
- Software available.
- Sizing of isolated microgrids and DERs.
- Workshop: Sizing a storage system.
- Work: Sizing of a photovoltaic system with storage.
- Distributed optimization
- Fundamentals
- Main distributed optimization methodologies.
- Workshop: Consensus and ADMM
- Game theory
- Fundamentals.
- Cooperative and non-cooperative games.
- Application to oligopolistic competition: Cournot and Stackelberg equilibrium (price leader and quantity leader).
- Energy markets
- Fundamentals.
- Structure of energy markets.
- Primary electricity markets.
- Secondary electricity markets.
- Workshop: Example of energy auction.
- Stochastic optimization
- Information theory and systems entropy.
- Bayesian inference.
- Monte Carlo.
- Workshop: Prediction of demand with uncertainty.
- Modernization of the power system
- Facing complexity.
- Management of DERs using Transactional Energy.
- Management of DERs using multi-agent systems.
- Taller: Spade, Pymarkets.
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.