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
Diplôme(s) concerné(s)
- Echanges PEI
- Energy Environment : Science Technology & Management
- Economics for Smart Cities and Climate Policy
- Titre d’Ingénieur diplômé de l’École polytechnique
Parcours de rattachement
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 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 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.
Pour les étudiants du diplôme Titre d’Ingénieur diplômé de l’École 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.
Format des notes
Numérique sur 20Littérale/grade réduitPour les étudiants du diplôme Titre d’Ingénieur diplômé de l’École polytechnique
Le rattrapage est autorisé (Note de rattrapage conservée)- Crédits ECTS acquis : 5 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Energy Environment : Science Technology & Management
Le rattrapage est autorisé (Note de rattrapage conservée)- Crédits ECTS acquis : 4 ECTS
La note obtenue rentre dans le calcul de votre GPA.
Pour les étudiants du diplôme Echanges PEI
Le rattrapage est autorisé (Note de rattrapage conservée)- 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 Economics for Smart Cities and Climate Policy
Le rattrapage est autorisé (Note de rattrapage conservée)- Crédits ECTS acquis : 4 ECTS
La note obtenue rentre dans le calcul de votre GPA.
L'UE est évaluée par les étudiants.