6d • 💻 Applied Modeling
Battery Arbitrage Project
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:
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.
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Battery Arbitrage Project
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