Description

Theory
2

Theory/Practice
1

Laboratory
2

Instructors

Nuno Gomes

Contents

1. Optimization (2 weeks) CP1
1.1. Optimization problems: definition and characterization
1.2. Linear and Non-Linear Optimization
1.3. Problems Classes
2. Resolution Methods of Combinatorial Optimization Problems (8 Weeks) CP2
2.1. Simplex Method
2.2. Branch and Bound
2.3. Benders Decomposition
2.4. Meta Heuristics
2.5. Evolutionary Computing
2.6. Artificial Intelligence Methods
3. Power Systems Optimization Problems Solving Methods (4 Weeks) CP3
3.1. The unit commitment problem
3.2. The production scheduling problem
3.3. The maintenance scheduling problem
3.4. Power system planning
3.5. Optimal power flow problem

Learning Outcomes

The student should:
- Recognize an optimization Problem. OB1
- Know the main optimization problems solving methods, in
the area of Linear and Non-Linear Programming, Metaheuristics, Evolutionary Programming and Artificial Intelligence. OB2
- Know, formulate and identify solutions for the main optimization
problems of the Electric Power Systems area. OB3

The student should:
- When in an Electric Power System Organization, identify issues for
optimization. OB4
- When facing an optimization problem identify the requirements to their
resolution. OB5
- After the problem formulation choose an adequate optimization method
and coordinate the implementation of the solving tool. OB6