(169c) Integrated Design of a Green Hydrogen Production System Under Climate and Demand Uncertainty

Climate change poses a long-term threat to human society, necessitating urgent action to mitigate global temperature rise through substantial and rapid reductions in greenhouse gas emissions, especially CO₂. As the energy sector contributes the largest share of global CO₂ emissions [1], transitioning toward low-carbon and zero-carbon alternatives is essential. In response, green hydrogen, a clean energy carrier, offers a promising pathway for decarbonizing some sectors of the energy landscape [2].

This study proposes a framework to design an integrated green hydrogen production system for regions with a hot, arid desert climate [3] considering climate change and hydrogen demand uncertainties. These regions are characterized by very high solar irradiation and little or no rain, and therefore, solar conversion technologies and water desalination should be considered for hydrogen production–for example, regions including the Middle East, North Africa, Southwest Asia, some regions of the North of Mexico, the US, and Australia. Recognizing the impact of climate change on renewable energy conversion [4], its uncertainty, and the uncertainty of future hydrogen demand, we consider multiple climate scenarios and hydrogen demand profiles to design green hydrogen production systems.

The proposed framework involves i) an initial superstructure combining a concentrated solar power plant (CSP) with thermal storage, a photovoltaic plant, a wind turbine plant, a battery electrical storage system, a seawater reverse osmosis desalination plant with water storage tanks, a proton exchange membrane (PEM) electrolyzer system, and hydrogen storage tanks; ii) an optimization model based on a stochastic program; and iii) climate and hydrogen demand projections.

We develop a two-stage stochastic program that optimizes first-stage investment decisions on renewable conversion technologies, storage, fresh-water production, and electrolyzers and subsequently adjusts recourse operational strategies to minimize the expected system life cycle cost and levelized cost of hydrogen (LCOH). The original model is a mixed-integer nonlinear program (MINLP) that we reformulate into a mixed-integer linear program (MILP) using the method described in [5] and [6], which renders a more computational efficient model. Ultimately, this reformulation enables us to address larger planning horizons with hourly resolution to capture hourly and yearly weather variability.

We consider future climate projection scenarios obtained from the Coupled Model Intercomparison Project (CMIP) Phase 6 to capture future renewable energy conversion uncertainty, variability, and evolution over the coming decades. Alternative scenarios for hydrogen demand are also postulated, covering local continuous demand and weekly or monthly demand representing international demand to be supplied by shipping vessels. Therefore, this approach accounts for a range of long-term predicted climate profiles and demand patterns to ensure resilience against multiple weather events and assess the impact of alternative hydrogen demand profiles on the LCOH.

We consider a case study in Saudi Arabia to assess the impact of climate change projections and demand uncertainty on i) the technologies selection to produce green hydrogen; ii) system life cycle cost and iii) the LCOH. Our results demonstrate enhanced system reliability and a competitive levelized cost of hydrogen (LCOH), ensuring operational feasibility under adverse conditions.

This work underscores the necessity of incorporating uncertainty into green hydrogen infrastructure planning and offers a scalable, technically feasible model that can be adapted to other regions with similar renewable resource profiles.

References:

[1] “CO₂Emissions–Global Energy Review 2025–Analysis.”IEA, March 2025. https://www.iea.org/reports/global-energy-review-2025/co2-emissions.

[2] IRENA (2024), Green hydrogen strategy: A guide to design, International Renewable Energy Agency, Abu Dhabi. https://www.irena.org/Publications/2024/Jul/Green-hydrogen-strategy-A-g….

[3] H.E. Beck, N.E. Zimmermann, T.R. McVicar, N. Vergopolan, A. Berg, E.F. Wood, Present and future Köppen-Geiger climate classification maps at 1-km resolution, Scientific Data 5 (1) (2018) 1–12.

[4] Feron S., Cordero R.R., Damiani A., Jackson R.B., 2021. Climate change extremes and photovoltaic power output. Nature Sustainability 4 (3), 270–276.

[5] Zionts, Stanley. "Programming with linear fractional functionals." Naval Research Logistics Quarterly 15, no. 3 (1968): 449-451.

[6] Yue, Dajun, Gonzalo Guillén‐Gosálbez, and Fengqi You. "Global optimization of large‐scale mixed‐integer linear fractional programming problems: a reformulation‐linearization method and process scheduling applications." AIChE Journal 59, no. 11 (2013): 4255-4272.