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AI-supported stochastic optimization distillation for decision-makers in the energy transition

AI-supported stochastic optimization distillation for decision-makers in the energy transition

AI-supported stochastic optimization distillation for decision-makers in the energy transition

Spécialité

Energétique et génie des procédés

Ecole doctorale

ISMME - Ingénierie des Systèmes, Matériaux, Mécanique, Énergétique

Directeur de thèse

KARINIOTAKIS Georges

Co-encadrant

CAMAL Simon

Unité de recherche

Energétique et Procédés

Contact
Date de validité

24/05/2024

Date de début de thèse

01/07/2024

Site Web
Mots-clés

iIntelligence artificielle, science de données, Digitalisation d'énergie, Prise de décision sous incertitude, Interaction entre humains et systèmes complèxes, Knowledge distillation

Artificial intelligence, data science, Energy digitalisation, Decision making under uncertainty, Interaction of humans with complex systems, Knowledge distillation

Résumé

...

Context and background:

The stochastic optimization framework has been extensively studied in energy systems, mainly driven to handle the variability and uncertainty of renewable energy sources (RES). Despite the advances in tractability and computational performance of these methods, they are not widely adopted by the industry due to a complex understanding from the decision-maker, which leads to algorithmic aversion. Recently, data-driven prescriptive models (e.g., based on decision trees or reinforcement learning) have been proposed to remove model chain complexity and offer explainability in terms of input features importance, yet they may remain as a “black-box” model to decision-makers and may be negatively affected by data quality. Moreover, in cases where uncertainty is represented by a large number of input scenarios, the interpretability of mathematical optimization models or data-driven techniques is significantly diminished and makes ex-ante and ex-post analysis from humans very complex. An alternative interesting approach in the literature proposes solution sets from the stochastic optimization problem and lets the operators chose the solution according to their priorities and in the region close to the optimum rather than proposing them a hard solution.

Scientific objectives & Methodology:

This Ph.D. topic is inspired by the concept of knowledge distillation (i.e., the process of transferring knowledge from a large model to a smaller one), and the goal is to build reduced or data-driven models that can explain the decisions suggested by stochastic optimization tools. For instance, these models can be trained with data generated by solving multiple instances of the stochastic optimization problem and with several objectives, such as a) explaining how the decision-maker preferences and risk profile affect the optimization output or b) deriving a set of rules that relate input data (e.g., scenarios) and decisions, or c) support the decision-maker in choosing between deterministic and stochastic optimization for a particular context.

Expected results:

The results of this thesis are expected to contribute in increase the acceptability of AI solutions by the different actors of the energy systems. The AI-solutions developed will enhance human capabilities (rather than replacement of human intelligence) in control and trading centers. It will bring to the industry alternative paradigm for accounting for uncertainties in power system management.

Contexte

...

Encadrement

Une codirection internationale (pas une cotutelle) avec Dr. Ricardo Bessa est prévue.
Quotités d'encadrement:
Directeur de recherche : Georges Kariniotakis 30%
Co-Directeur: Ricardo Bessa (INESC TEC) 40%
Maître de thèse: Simon Camal: 30%

Profil candidat

...

Engineer and / or Master of Science degree (candidates may apply prior to obtaining their master's degree. The PhD will start though after the degree is succesfully obtained). Good level of general and scientific culture. Good analytical, synthesis, innovation and communication skills. Qualities of adaptability and creativity. Motivation for research activity. Coherent professional project. Skills in programming (eg,Python, R, Julia,…) and knowledge of optimization tools (e.g, Gurobi, CPLEX).

A succesful candidate will have a solid background in two or more of the following competencies:
* optimisation
* applied mathematics, statistics and probabilities
* data science, machine learning, artificial intelligence
* power system management, integration of renewables

Expected level in french : Good level
Expected level in english : Proficiency

Desired starting date : Fall 2024. Duration 36 months. Full-time paid position.


APPLICATION:

Please send the following elements (in pdf format) by email to Prof. George Kariniotakis (georges.kariniotakis_AT_minesparis.psl.eu) AND to Dr. Simon Camal (simon.camal_AT_minesparis.psl.eu):

* Curriculum vitae (CV).
* Motivational letter for the application (cover letter).
* Contact details of two individuals that can provide a letter of reference (and eventually available already letters of reference).
* Copy of grade transcripts and last diploma (in English or French).

Use in the title of your email the acronym of this PhD topic “PHD-2024-ERSEI-ITN-ADUM”

Deadline for applications: 20/05/2024

For more information and applications please contact Prof. Georges Kariniotakis and Dr. Simon Camal

Références

...

Type financement

Concours pour un contrat doctoral

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