Optimal Scheduling of Electrolyzer in Power Market with Dynamic Prices

TL;DR

This paper presents a deep learning-based forecasting method for hydrogen consumption in fuel cell vehicles, enabling optimal electrolyzer scheduling in power markets with dynamic prices to maximize profits.

Contribution

It introduces a novel deep learning approach for accurate hydrogen consumption forecasting to improve electrolyzer scheduling under dynamic electricity prices.

Findings

Forecasting accuracy improves hydrogen cost management.

Scheduling reduces hydrogen production during high-cost periods.

Enhanced profit margins for hydrogen producers.

Abstract

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.