Day-ahead energy management in green microgrids: Impact of long-term scheduling of hydrogen storage systems

Abstract

Day-ahead energy management systems focus on optimizing resource scheduling on a daily basis, which may not adequately address seasonal load or price fluctuations. Targeting these long-term fluctuations in day-ahead scheduling, this paper introduces a two-stage optimization methodology specifically designed for day-ahead scheduling with long-duration hydrogen storage systems (HSS) that effectively eliminates the need for scenario-reduction techniques by dividing the long-term scales into short-term ones. As the amount of stored hydrogen in the storage tank affects operational scheduling on consecutive days, the first stage introduces a new variable to represent variations in the stored hydrogen amount, effectively decoupling consecutive days. Subsequently, the second stage employs a developed active set algorithm. This algorithm adds hydrogen storage tank constraints to the objective function to ensure that the stored hydrogen amount does not exceed the tank’s capacity limits on any day. Using real-world data from South Australia State, simulation results validate the proposed algorithm’s effectiveness and demonstrate that employing large storage tanks within an HSS is viable for long-duration applications.