Spring 2026 Master Thesis - Quantitative Hedging of Flexible Renewable Energy Portfolios

⚡️ What We DoFlower is Flexible Power. We are a next-gen energy company leveraging AI and machine learning to make renewable energy stable and always available – even when the sun isn’t shining and the wind isn’t blowing.Through smart optimization and trading of energy assets like wind and solar farms, battery systems, and EV chargers, we make renewable energy reliable and predictable, leading the charge towards the energy system of tomorrow.🌟Who We AreTech company at heart – purpose in our DNA. Flower consists of a diverse group of innovative individuals with a strong desire to improve the state of the world.At Flower, we believe trust, collaboration and diversity are essential to not only create an inclusive work environment, but also drive career growth. By embracing varying perspectives, we allow creativity and progress to flourish.👩‍💻 About The Master Thesis SubjectPower Purchase Agreements (PPAs) have been instrumental tools in the shift towards renewable energy. However, previous generation’s PPAs have demonstrated scalability limitations that have restrained the growth of renewables.The Green Baseload (GBL) is Flower’s next-generation PPA: by assuming the profile risks of renewable generation and using AI-driven optimization across a large portfolio of flexible assets (e.g., wind farms, EV chargers, battery systems), Flower delivers stable, plannable green electricity for enterprises. This model enables long, secure, and locally sourced contracts at lower total cost while addressing the limitations of traditional PPAs.GBL internalizes renewable profile risk, while Battery Energy Storage Systems (BESS) acts as a physical hedge, reshaping volume, shape, and imbalance exposures. As a result, Flower assumes exposure to volume imbalances between intermittent production and consumption and profile-driven market risk, where low renewable output can coincide with high prices. These dynamics introduce non-linear risks that differ fundamentally from traditional, price-only exposures.What You’ll DoThe key scope of this degree project is to research derivative hedging strategies for next-generation flexible energy portfolios such as Flower’s Green Baseload. The study will examine how existing financial markets, within their liquidity and product constraints, can be used to extend conventional hedging to the broader and more dynamic risks introduced by storage-integrated, flexible portfolios.The final scope may be refined jointly with the student and supervisor to align with the most promising and relevant research directions that emerge during the project.Your backgroundM.Sc. student(s) with a degree project in Financial Mathematics or a closely related field (e.g., Applied Mathematics, Statistics, Quantitative Finance). Strong background in statistics, time-series modeling, and financial mathematics.Solid Python programming skills and experience with data analysis or quantitative modeling.Curious about energy markets and risk management, and able to communicate results clearly to both technical and non-technical audiences.📍 LocationOur beautiful office is located in the heart of Södermalm just a short walk from Slussen subway station. We prefer in-office collaboration for this matter.✉️ ApplyTo apply, please submit a CV and answer the questions thoroughly at the beginning of the application.We look forward to hearing from you!We kindly but firmly decline any engagement in recruitment assistance for our hiring processes. This includes partnership offers or the sale of recruitment tools.