Description

Theory
2

Theory/Practice
2

Instructors

Nuno Gomes

Contents

P1. Optimization (10 Hours)
P1.1. Optimization problems: definition and characterization
P1.2. Linear and Non-Linear Optimization
P1.3. Problems Classes
P2. Resolution Methods of Combinatorial Optimization Problems (32 Hours)
P2.1. Simplex Method
P2.2. Branch and Bound
P2.3. Benders Decomposition
P2.4. Meta Heuristics
P2.5. Evolutionary Computing
P2.6. Artificial Intelligence Methods
P3. Power Systems Optimization Problems Solving Methods (18 Hours)
P3.1. The unit commitment problem
P3.2. The production scheduling problem
P3.3. The maintenance scheduling problem
P3.4. Power system planning
P3.5. Optimal power flow problem

Learning Outcomes

The student should:
- Recognize an optimization Problem. O1
- Know the main optimization problems solving methods, in
the area of Linear and Non-Linear Programming, Metaheuristics, Evolutionary Programming and Artificial Intelligence. O2
- Know, formulate and identify solutions for the main optimization
problems of the Electric Power Systems area. O3

The student should:
- When in an Electric Power System Organization, identify issues for
optimization. O4
- When facing an optimization problem identify the requirements to their
resolution. O5
- After the problem formulation choose an adequate optimization method
and coordinate the implementation of the solving tool. O6