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PA - C1 - INF569 : Decision theory, with applications to energy systems

Domaine > Informatique.

Descriptif

Management of energy systems is one of the biggest challenges of our time. The daily demand for energy increases constantly for many reasons, including the worldwide spreading of the electrification/decarbonization of vehicles used for public and private transportation. Moreover, the wide use of renewable energies, also aimed at limiting polluting emissions, can create instability in the networks and uncertaintly in energy production. The current production sources and the current infrastructure for transmission and distribution are likely to soon become insufficient to cope with these changes. Decision makers will, thus, need efficient and effective tools aimed at helping them to optimize operational and strategic decisions to be taken in the short, medium, and long term. This course aims at providing the students with the background in mathematical optimization needed to play a fundamental role in the decision-making processes in energy systems. Mathematical optimization allows to formally state an extremely large variety of optimization problems as a so-called mathematical formulation. Once the problem is formalized, its optimal solution can be found by properly using mathematical optimization solvers or devising algorithms tailored for the specific problem. In this course, we will code the formulations and run solvers thanks for the modeling language AMPL. Each of the lectures will focus on a particular optimization aspect and one or more energy applications. The applications covered will be: production, transmission, distribution of energy; energy markets; renewable energies; smart grids.

Warning: this is a course offered by the Computer Science Department. Basic knowledge of Unix OS and of shell commands is requested. Moreover, the students will learn the AMPL modeling language.

Objectifs pédagogiques

The students will learn

- what a mathematical optimization problem is and how to formalize an optimization problem as a mathematical model

- a wide variety of applications in energy systems: optimization of energy production, energy transportation and distribution, optimal design of wind farms, energy markets, smart grids, ...

- AMPL, a modeling language 

- how to deal with optimization problem of increasing difficulty, e.g., linear programming, mixed integer linear programming, mixed non linear programming, bilevel problems, optimization problems with uncertainties, multiobjective problems

- how to use optimization solver

- how to devise simple heuristic algorithms

- how to deal with applications

Pour les étudiants du diplôme Energy Environment : Science Technology & Management

This is a course offered by the Computer Science Department. A basic knowledge of Unix OS and of shell commands of is requested. The concepts taught relate both to computer science (mainly algorithms) and applied mathematics. The math refresher course is a requirement.

Pour les étudiants du diplôme Echanges PEI

This is a course offered by the Computer Science Department. A basic knowledge of Unix OS and of shell commands of is requested. The concepts taught relate both to computer science (mainly algorithms) and applied mathematics. The math refresher course is a requirement.

Pour les étudiants du diplôme Diplôme d'ingénieur de l'Ecole polytechnique

This is a course offered by the Computer Science Department. A basic knowledge of Unix OS and of shell commands of is requested. The concepts taught relate both to computer science (mainly algorithms) and applied mathematics. The mathematical background of X students should be enough.

Pour les étudiants du diplôme Economics for Smart Cities and Climate Policy

This is a course offered by the Computer Science Department. A basic knowledge of Unix OS and of shell commands of is requested. The concepts taught relate both to computer science (mainly algorithms) and applied mathematics. The math refresher course is a requirement.

Format des notes

Numérique sur 20

Littérale/grade réduit

Pour les étudiants du diplôme Echanges PEI

Le rattrapage est autorisé (Note de rattrapage conservée)
    L'UE est acquise si note finale transposée >= C
    • Crédits ECTS acquis : 4 ECTS

    La note obtenue rentre dans le calcul de votre GPA.

    L'UE est évaluée par les étudiants.

    Pour les étudiants du diplôme Diplôme d'ingénieur de l'Ecole polytechnique

    Le rattrapage est autorisé (Note de rattrapage conservée)
      L'UE est acquise si note finale transposée >= C
      • Crédits ECTS acquis : 5 ECTS

      La note obtenue rentre dans le calcul de votre GPA.

      Pour les étudiants du diplôme Economics for Smart Cities and Climate Policy

      Le rattrapage est autorisé (Note de rattrapage conservée)
        L'UE est acquise si note finale transposée >= C
        • Crédits ECTS acquis : 4 ECTS

        La note obtenue rentre dans le calcul de votre GPA.

        L'UE est évaluée par les étudiants.

        Pour les étudiants du diplôme Energy Environment : Science Technology & Management

        Le rattrapage est autorisé (Note de rattrapage conservée)
          L'UE est acquise si note finale transposée >= C
          • Crédits ECTS acquis : 4 ECTS

          La note obtenue rentre dans le calcul de votre GPA.

          Mots clés

          mathematical optimization

          Méthodes pédagogiques

          mathematical optimization
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