Modeling alkaline water electrolysis for power-to-x applications: A scheduling approach. (24th February 2021)

• Record Type: Journal Article
• Authors: Varela, Christopher; Mostafa, Mahmoud; Zondervan, Edwin
• Is Part Of: International journal of hydrogen energy. Volume 46:Number 14(2021)
• Page Start: 9303
• Page End: 9313
• Publication Date: 2021-02-24
• Subjects: Hydrogen -- Alkaline water electrolysis -- Power-to-x -- Mathematical programming -- Renewable energy sources; Hydrogen as fuel -- Periodicals; Hydrogène (Combustible) -- Périodiques; Hydrogen as fuel; Periodicals; 665.81
• DOI: 10.1016/j.ijhydene.2020.12.111
• Languages: English
• ISSNs: 0360-3199

Abstract

Abstract: The flexible operation of alkaline water electrolyzers enables power-to-x plants to react efficiently to different energy scenarios. In this work, a novel scheduling model for alkaline water electrolysis is formulated as a mixed-integer linear program. The model is constructed by implementing operational states (production, standby, idle) and transitions (cold/full startup, shutdown) as integer variables, while the power loading and hydrogen flowrate are set as continuous variables. The operational characteristics (load range, startup time, ramp rates) are included as model constraints. The proposed model allows finding optimal number of electrolyzers and production schedules when dealing with large data sets of intermittent energy and electricity price. The optimal solution of the case study shows a balance between hydrogen production, energy absorption, and operation and investment costs. The optimal number of electrolyzers to be installed corresponds to 54% of the ones required to absorb the highest energy peak, being capable of loading 89.7% of the available energy during the year of operation, with an overall plant utilization of 93.7% and 764 startup/shutdown cycles evenly distributed among the units. Highlights: A novel scheduling model for alkaline water electrolysis (AEL) has been developed. The model introduces AEL states, transitions, and operational characteristics. The model allows to find optimal number of electrolyzers and production schedules. The model is suitable to handle large data sets of fluctuating energy and prices. The MILP solution provides a balance between production, energy absorption and costs.