To meet the challenges and ambitions of TotalEnergies, the Hybrid and Storage R&D proposes solutions to optimize the sizing and operation of energy systems that couple multiple, often intermittent, energy sources for various applications. It also evaluates key performance indicators during the sizing phase and prepares for the commissioning phase of such projects. With the increasing complexity of new energy systems and the diversity of technologies, the OptimHYSe project aims to build a library of models for these technologies, with an in-depth analysis of their design and operation.

The objective is to enhance the existing optimization tool for sizing and operation of hybrid systems by integrating Monte Carlo simulation functionality. This will improve the tool's ability to handle uncertainties and provide more robust optimization results.

Hybrid systems, which combine multiple energy sources, are increasingly used to improve energy efficiency and reliability. However, the optimization of such systems is complex due to the variability and uncertainty in factors like energy demand, weather conditions, and component performance. Monte Carlo simulations can address these uncertainties by performing probabilistic analysis, thus providing a more comprehensive optimization solution.

Main activities:

1. Perform comprehensive litrature review on existing methodologies for Monte Carlo simulations in energy systems
2. Identify best practices and potential challenges in integrating Monte Carlo simulations with optimization tools
3. Develop algorithms to incorporate Monte Carlo simulations into the existing optimization framework ensuring compatibility and efficiency
4. Compare results with traditional deterministic optimization methods to demonstrate improvements and Improve decision-making capabilities for users through probabilistic analysis
5. Document all findings in a comprehensive report and prepare presentations for progress and final results

The intern will interact with several teams involved in optimization topics for hybrid systems (R&D team, Technical Lines and Customer Lines).

You will join the R&D team based on the Paris-Saclay Campus in the NEXT building. To strengthen the on-site team, TotalEnergies is looking for young talents who will bring new ideas and meet R&D challenges.

You will evolve within a team of experienced professionals and with a tutor-coach, the reference for your future profession. Individualized support will help you develop your autonomy and lead you to your diploma!

Activités principales :

1. Réaliser une revue de littérature complète sur les méthodologies existantes pour les simulations de Monte Carlo dans les systèmes énergétiques.
2. Identifier les meilleures pratiques et les défis potentiels de l'intégration des simulations de Monte Carlo avec les outils d'optimisation.
3. Développer des algorithmes pour intégrer les simulations de Monte Carlo dans le cadre d'optimisation existant, en assurant la compatibilité et l'efficacité.
4. Comparer les résultats avec les méthodes d'optimisation déterministes traditionnelles pour démontrer les améliorations et améliorer les capacités de prise de décision des utilisateurs grâce à l'analyse probabiliste.
5. Documenter toutes les conclusions dans un rapport complet et préparer des présentations pour les progrès et les résultats finaux.

Currently enrolled in an engineering school or Master's program in the Research & development field, are you looking for an 6-month end-of-study internship starting in March 2025?

Do you have experience in in computer science, electrical engineering, mechanical engineering, energy systems, or a related field and knowledge of of optimization techniques (e.g., MILP, LP, MINLP...)? Do you understand renewable hybrid energy systems?

Are you comfortable with office automation and familiar with the Office suite? You'll be using Excel, Word and Powerpoint in particular and proficiency in Python for modeling and simulation, data analysis tools. You understand probability theory and statistical methods, and familiarity with stochastic processes. Experience with Monte Carlo methods is a plus.

Are you a self-starter, rigorous and a team player? Can you take the initiative? A professional command of French will be essential for this position. Good writing skills are a plus.

So don't wait any longer, apply to join our team!