Context
The transition to a net-zero electricity grid presents complex challenges. Integrating renewable energy sources necessitates advanced storage solutions and robust grid balancing mechanisms. Grid-scale batteries offer a promising solution to these challenges, but their operation and optimization are multifaceted.
A battery can participate in various revenue streams, each with unique characteristics. However, no single revenue stream is sufficient to guarantee economic viability. To ensure profitability and facilitate rapid deployment, it is essential to aggregate and optimize all revenue streams.
Our algorithmic trading solution addresses this challenge by providing real-time bids and battery dispatch instructions. This solution effectively integrates and optimizes battery revenue streams, promoting their sustainable development. We are seeking to further enhance this automated system.
Abous us
StackEase is a deeptech spinoff from the INRIA (French Institute for Research in Computer Science). We work with talented people located in Paris and Marseille. Though a relatively recent establishment, we have earned the trust and backing of significant players in the energy storage industry. Leveraging the capabilities of algorithms, our mission is to facilitate the transition towards clean and sustainable energy sources. Our values are innovation, unwavering customer satisfaction, meritocracy, and a commitment to environmental sustainability.
Missions
- Develop prediction tools for electricity market prices using signal processing and supervised learning techniques.
- Design and implement trading and optimization algorithms to maximize profits from grid-scale batteries.
- Create automated unit and non-regression tests for the developed algorithms.
- Validate and document the results of the algorithms on real-world case scenario
- Deploy the algorithms on a cloud platform.
Skills
- Currently enrolled in the final year of a master’s degree in computer science, applied mathematics, or electrical engineering.
- Strong mathematical, optimization, and computer science skills.
- Practical experience with machine learning techniques (supervised and RL).
- Knowledge of the French ancillary services and short-term electricity markets.
- Enthusiastic, rigorous, and autonomous, with a desire to explore entrepreneurship and renewable energies.
- Fluent in English; proficiency in French is an asset.
Compensations
- Paid internship
- Partial remote work is possible
You do not need to meet 100% of the requirements to apply, we will study all applications.
References and a cover letter are welcome but not mandatory.