Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices
This work addresses cost optimization for hydrogen producers in the energy sector, but it is incremental as it applies existing forecasting methods to a specific domain.
The paper tackles the problem of maximizing hydrogen producer profit in dynamic power markets by developing a deep learning approach to forecast hydrogen consumption for fuel cell vehicles in the taxi industry, resulting in minimized production costs through strategic scheduling based on price fluctuations.
Optimal scheduling of hydrogen production in dynamic pricing power market can maximize the profit of hydrogen producer; however, it highly depends on the accurate forecast of hydrogen consumption. In this paper, we propose a deep leaning based forecasting approach for predicting hydrogen consumption of fuel cell vehicles in future taxi industry. The cost of hydrogen production is minimized by utilizing the proposed forecasting tool to reduce the hydrogen produced during high cost on-peak hours and guide hydrogen producer to store sufficient hydrogen during low cost off-peak hours.