Decision and Control in Energy


1. The learning of control and decision methodologies and techniques with application in various energy areas.

2. Provide students with abstract thinking ability in the modeling of dynamical processes in the energy area, and in the design of control systems and decision systems, with the help of methodologies and technologies taught.

3. Develop in students the ability to accomplish specific solutions of control and decision, namely with the use of simulation and laboratory practice.

4. Raising awareness the students on the current and the future challenges related with the problems in the areas of energy, control and decision.

General characterization





Responsible teacher

Fernando José Almeida Vieira do Coito


Weekly - 4

Total - 56

Teaching language



Good understanding of the following subjects:

- modeling of dynamic systems;

- time and frequency behavior of linear dynamic systems;

- control of linear systems represented by transfer function;

- state model representation of dynamic systems (discrete time); state observers.


Liuping Wang, "Model Predictive Control System Desig and Implementation  Using MATLAB", Springer, 2009.

William S. Levine, "Control System Advanced Methods", CRC Press, 2011.

R.R. Negenborn, "Multi-Agent Model Predictive Control - with Applications to Power Networks", TRAIL Thesis Series T2007/14, The Netherlands TRAIL Research School, 2007.

John Lygeros, Claire Tomlin, and Shankar Sastry, "Hybrid Systems: Modeling, Analysis and Control",, 2008.

William S. Levine, "The Control Handbook", 2nd ed., CRC Press, 2010.

Teaching method

Oral exposition in theoretical-practical classes to introduce the topics and related problems.
Practical classes for experimental work.

Evaluation method

Evaluation Components (# 3):
a) theoretical and practical evaluation (TP);
b) laboratory evaluation (LAB);
c) summative assessment (SUM).

Individual theoretical and practical assessment (TP):
The TP evaluation may be performed by writing an article or by final exam.
The article must be 2 pages long with 2 columns per page. The paper must include two parts:
a) dynamic energy system model;
b) control and decision methodologies.

Group laboratory evaluation (LAB):
laboratory work with report, and individual oral assessment.

Summative assessment (SUM): 1 Moodle quiz.

Conditions for obtaining frequency:
a) attend at least 66% of the practical classes if the student is not a student worker;
b) obtain at least 10 points in the grade of the lab work weighted with the participation in the classes.
Frequency = 0.8 * Nota_LAB + 0.2 * Attendance. The frequency is valid for 1 year.

For approval in the course the final grade must be greater than or equal to 9.5 points.

Nota_TP = 0.5 * Version_1 (model) + 0.25 * Version_2 (control / decision) + 0.25 * oral presentation,
Nota_TP = Exam Grade.

Nota_LAB = 0.8 * Lab1 + 0.2 * Attendance.

Nota_SUM = 1.0 * Moodle quiz.

Final grade = 0.4 * Nota_TP + 0.4 * Nota_LAB + 0.2 * Nota_SUM.

Subject matter

1.  Introduction
2.  Energy challenges
3.  Discrete time LTIS state model representation
4.  Introduction to the design of Model Based Predictive Control systems (MBPC)
5.  Adding constraints to the MBPC project
6.  Decentralized control structures
7.  Control project in single-layer structures
8.  Control in discrete event systems
9.  Design of a supervisory controller
10. Networked control systems


Programs where the course is taught: