Youree is at the forefront of distributed power storage technologies.
As a member of the FrenchTech, we are fiercely committed to a vision of a future unburdened by carbon emissions, powered by sustainable energy solutions.
Our strength lies in our team – a collective of innovative thinkers deeply invested in driving real-world impact through advanced technological solutions.
At Youree, we are not just advocates for the transformative potential of electric vehicles and renewable energy integration; we are the architects of technologies that will make these systems more efficient, reliable, and widely adopted.
Our aspirations extend beyond technical excellence – we are also deeply invested in shaping policy and market structures that favor the transition to sustainable energy systems.
The emergence of Electric Vehicles (EVs) signals a promising shift towards sustainable transportation solutions.
Powered by rechargeable batteries, these vehicles not only offer a cleaner alternative to traditional internal combustion engine vehicles but also carry the potential for energy storage and demand response grid integration.
Despite the benefits, uncertainties arising from weather conditions, demand fluctuations, geo-political forces, and supply variability present significant challenges to efficient energy utilization.
This PhD research opportunity is a call to join Youree and the Laboratory of Signals and Systems in developing a data-driven optimization framework for EVs to navigate these uncertainties.
The Laboratory of Signals and Systems is located in Paris-Saclay University, and jointly operated by the CNRS, CentraleSupélec and the University of Paris-Saclay. Research at L2S focuses on fundamental and applied mathematical aspects of control theory, AI, data science, information, signal and image processing, communication, and network theory.
The primary objectives of this research are threefold:
- Investigate the application of stochastic game theory, stochastic geometric programming, and related optimization methods to understand, model, and enhance network effects within the context of electric vehicle (EV) distributed storage systems.
- Design optimization algorithms that utilize the uncertain data to optimize incentives for EV owners to participate in the distributed storage system while ensuring the overall efficiency and resilience of the grid.
- Evaluate the performance of the proposed framework in realistic EVs platforms, considering various uncertainty scenarios.
In this research, we will focus on developing robust and flexible models to represent the uncertain parameters in the energy generation and EV integration (charging and/or discharging).
This will involve statistical techniques, machine learning algorithms, and stochastic optimization approaches to capture the uncertainty associated with energy generation, demand, and storage, where the goal is to be able to provide a framework for vehicle-to-grid integration.
We will explore methods such as chance constraints, two-stage stochastic optimization, multistage optimization, Markov decision processes, and scenario generation to quantify and represent uncertainty accurately.
We will develop optimization algorithms that can effectively handle the uncertainty in EVs. The algorithms will integrate the uncertain data into mathematical programming models, allowing for the determination of optimal EV storage strategies, and demand response actions.
To assess the effectiveness of the proposed framework, we will evaluate its performance using real-world EV systems. Case studies will be conducted on representative power grids or microgrids, considering different uncertainty scenarios and system configurations. Performance metrics such as cost minimization, reliability improvement, and carbon footprint reduction will be analyzed to demonstrate the advantages of the proposed approach compared to existing methods.
The anticipated contributions of this research include:
- A comprehensive understanding of uncertainty sources in EV systems and their impact on energy management.
- Advanced modeling techniques to accurately represent uncertainty in EV together with energy generation, demand, and storage.
- Development of optimization algorithms that integrate uncertain data into decision-making processes for optimal EV.
- Practical insights and guidelines for policymakers and energy managers to enhance EV system performance under uncertainty.
Profiles and skills
We are seeking a candidate with a robust background in game theory and optimization and a Master’s degree in Applied Mathematics.
Experience in probability theory and proficiency in Machine Learning and Python are prerequisites.
Fluency in English, coupled with excellent general communication skills, is essential.
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