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Management and control of Smartgrids and microgrids

General details of the subject

Face-to-face degree course

Description and contextualization of the subject

Smartgrids should prove to adequately work for tomorrow networks. This requires technical solutions supported by adequate data analysis and mining so that the future network able to face the new requirements, including the integration of renewable based generation, energy storage systems, electric vehicles, and demand response.

Therefore, data mining techniques are introduced in this subject, including supervised and unsupervised classification algorithms applied to Smartgrids concepts. Furthermore, data based decision making will be also covered by means of mathematical optimization.

Teaching staff

NameInstitutionCategoryDoctorTeaching profileAreaE-mail
AGINAKO BENGOA, NAIARAUniversity of the Basque CountryProfesorado Adjunto (Ayudante Doctor/A)DoctorBilingualScience of Computation and Artificial
ALDASORO MARCELLAN, UNAIUniversity of the Basque CountryProfesorado Adjunto (Ayudante Doctor/A)DoctorBilingualApplied


Students should have updated knowledge about the advanced working techniques and methodologies related to the field of Smartgrids and distributed generation, particularly from the point of view of their control. 15.0 %
Awareness and application of the concepts and specifications of Smartgrids, their topologies, constituent components and basic dimensioning. 10.0 %
Developing operational and management strategies, including advanced techniques, for the grid-level regulation of Smartgrids. 30.0 %
Assessing and comparing the behaviour of Smartgrids and Microgrids obtained through simulation with different operational and management strategies, and justifying the results obtained. 20.0 %
Applying computing and telecommunications tools as a support for control in Smartgrids and Distributed Generation. 10.0 %
Students should be able to communicate about the projects carried out working in multidisciplinary and multilingual national and international teams of professionals and researchers operating in the field of Smartgrids. 10.0 %
Students should be trained to understand and analyse technical documents, standards and scientific articles on the topic of the Master, and to apply them in the creation of work and research related to the field of Smartgrids. 5.0 %

Study types

TypeFace-to-face hoursNon face-to-face hoursTotal hours
Applied classroom-based groups6511
Applied laboratory-based groups81523
Applied computer-based groups47.511.5

Training activities

NameHoursPercentage of classroom teaching
Drawing up reports and presentations30.00 %
Exercises4.0100 %
Expositive classes24.0100 %
Presentation of projects3.0100 %
Solving practical cases21.066 %
Systematised study30.50 %

Assessment systems

NameMinimum weightingMaximum weighting
Drawing up reports and presentations40.0 % 40.0 %
Practical tasks20.0 % 20.0 %
Written examination40.0 % 40.0 %

Ordinary call: orientations and renunciation

Group project:

A group project will be carried out where the contents learned in the course will be applied. Each group will deliver a report and will make an oral defense of the work done.

Learning process:

Proposed exercises both face-to-face and non-face-to-face. The the exercises will be carried out individually or in groups.

Written exam:

Theoretical and practical questions that will evaluate the competences to be acquired in the subject.

Final grade for the ordinary call:

40% group project.

20% learning process.

40% written exam.

MINIMUM GRADE: To pass the course it is necessary that the grades of both the project and the written exam are at least 4 points out of 10. If these minimum grades are not obtained, the grade for the ordinary call will be a maximum of 4 (out of 10).

In any case, any student who does not take the written exam will have a grade of NOT PRESENTED.

Extraordinary call: orientations and renunciation

A written exam consisting of theoretical and practical questions that will evaluate the content of the course, including the content from the laboratory practices and group project.

Final grade for the extraordinary call:

100% grade of the written exam


Introduction to Data Mining

Application of supervised and unsupervised classification algorithms in SmartGrids

Introduction to optimization

Linear programming

Integer programming


Compulsory materials

Documentation of the course:

Basic bibliography

H. Lee Willis Distributed Power Generation: Planning and Evaluation. Marcel Dekker, Inc

R.S. Michalski, I. Bratko, M. Kubat (1998). Machine Learning and Data Mining. Methods and Applications. Wiley.

B. Sierra (2006). Aprendizaje Automático: Conceptos Básicos y Avanzados. Pearson ¿ Prentice Hall.

I.H. Witten, E. Frank (2005). Data Mining: Practical Machine Learning Tools and Techniques. Elsevier, 2nd edition.

B. Korte, J. Vygen (2018) Combinatorial Optimization. Theory and Algorithms. Springer.

In-depth bibliography

Carol L. Stimmel (2014). Big Data Analytics Strategies for the Smart Grid. Auerbach Publications.

M. Conforti, G. Cornujols, G. Zambelli (2014). Interger Programming. Springer


Smart Grid, IEEE Transactions on Renewable Energy (Elsevier)

Applied Energy (Elsevier)

Information Sciences (Elsevier)

Artificial Intelligence (Elsevier)

Computers and Operations Research (Elsevier)


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