Optimising Electrolyser Operation for E-Methanol Production Using Renewable Electricity and Price Forecasting
Translated title
Optimering af Elektrolyserdrift Til E-Methanol Produktion Ved Hjælp af Vedvarende El- og Prisprognose
Author
Term
4. term
Education
Publication year
2025
Submitted on
2025-05-29
Pages
96
Abstract
This thesis explores the development of a modelling framework for optimising electrolyser operation in e-methanol production, using wind and solar electricity under fluctuating market conditions. A key focus lies in forecasting electricity prices with machine learning methods, including XGBoost and LSTM, to enable predictive control of electrolyser operation. By integrating these forecasts into a MILP-based scheduling model, the electrolyser is dynamically managed across three operational states(running, standby, and shutdown) to minimise total production cost while ensuring a fixed methanol output. The model incorporates transition constraints, variable electricity pricing, and CO2 sourcing costs to reflect realistic system behaviour. Scenario analysis demonstrates that electricity prices and electrolyser efficiency are the most critical cost drivers, while CO2 cost variations have a limited effect. The results highlight the importance of combining accurate short-term forecasting with flexible operation to enhance economic viability. Future work should prioritise advancements in electrolyser technology, electricity cost reduction strategies, and real-time model integration.
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