Hi everyone! I've been exploring the battery arbitrage space for the past few months and wanted to share my most recent project: I built a battery arbitrage backtester that finds the optimal charging schedule using LP, and tests it against three forecasting approaches(two rolling averages, one ML) and measures how much of the theoretically available market value was captured. I pooled data from different EU market zones to train a LightGBM model and trained a separate one for Britain, which is why the rolling average model overtook the ML model in Germany's results, for example, which was an interesting result. Article and Streamlit dashboard to play around with here if anyone's curious: https://kilowatthours.substack.com/p/building-a-battery-arbitrage-backtester https://bessarbitragedemo.streamlit.app/ I'm currently working on multi-battery coordination and whether batteries "knowing" more about the grid's topology can improve dispatch results, and using a neural network for the congestion signal. Curious to hear your feedback, and if anyone's working on battery optimization or grid ML, would love to connect.

I am currently struggling a bit with my future career path. Because job opportunities in the energy sector are limited in my country, I am trying to identify alternative routes. However, I do not want to stop working on energy markets and the sector itself. For me, it seems somewhat easier to enter the finance industry. Which department within finance would best support this long-term goal? I have been thinking that starting in a treasury department might be helpful, since it is directly related to monetary conditions and financial management. What do you think about this? If there is no direct path into energy markets, how should I build an alternative route that would still move me toward that objective?

Welcome to The Energy Data Scientist. This community helps you build quantitative skills for the energy sector through industry-based courses and 1-on-1 guidance. If you are new here, send me a short message with your background and the role or technical area you want to move toward, and I'll point you to the most relevant starting content. As a subscriber, you get the complete experience: - Full access to 120 courses and 220+ hours of video, with at least 1 new course added every month. - Personalised curriculum: share your CV and target roles, and I design a phased learning path based on the courses that match your profile. Each month, we review your progress and decide the next step. - Downloadable models: Python, Excel, GAMS, and more. - Direct support: you can ask questions at any time and receive a reply within 24 hours. - Career support: weekly job opportunities and interview preparation if you are actively applying for jobs, plus CV and cover letter feedback. About the courses: My courses are based on energy projects I've worked on as a consultant for private companies, governments, and European institutions. For each course, I rebuild the model from scratch using public or synthetic data, so the commercial logic and implementation patterns match what's used in practice, without any client confidentiality concerns. The attached PDF describes each of the 120 courses in the library. About me: I am Dr. Spyros Giannelos, PhD in Energy from Imperial College London. I have published extensively across energy systems, power-grid planning, and optimisation (1300+ citations, h-index 25). I have also led private, government, and European energy consultancy projects across the UK and European Union. [Upgrade here]