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1. Optimal Planning of Co-Located Wind Energy and Hydrogen Plants: A Techno-Economic Analysis

   https://doi.org/10.1088/1742-6596/2265/4/042063  

   Li, Honglin; Rahman, Jubeyer; Zhang, Jie (May 2022, Journal of Physics: Conference Series)

   Abstract Green hydrogen produced using renewable electricity could play an important role in a clean energy future. This paper seeks to analyze the techno-economic performance of integrated wind and hydrogen systems under different conditions. A co-located wind and hydrogen hybrid system is optimized to reduce the total system cost. We have adopted and improved a state-of-the-art techno-economic tool REopt, developed by the National Renewable Energy Laboratory (NREL), for optimal planning of the integrate energy system (IES). In addition to wind and electrolyzer components, we have also considered battery energy storage, hydrogen tank, and hydrogen fuel cell in the IES. The results show that (i) adding electrolyzers to the grid-connected wind energy system could reduce the total system cost by approximately 8.9%, and (ii) adding electrolyzers, hydrogen tank, and hydrogen fuel cells could reduce the total system cost by approximately 30%. 

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2. Multivariate CNN-Bi-LSTM temporal production forecasting for distributed wind-to-hydrogen integrated systems with efficient data feature extraction

   https://doi.org/10.1016/j.ijhydene.2025.152853  

   Bounaim, Doha; Mouafik, Sara; Li, Gang (January 2026, International Journal of Hydrogen Energy)

   This paper presents a practical framework that integrates wind speed forecasting with proton exchange membrane (PEM) electrolyzer design to optimize hydrogen production. Due to wind speed fluctuations, excess electrical energy is sometimes produced and left unused. A wind-to-hydrogen system addresses this challenge by converting surplus energy into storable hydrogen using a PEM electrolyzer. The proposed approach employs a multivariate supervisory control and data acquisition (SCADA) dataset and applies a convolutional neural network with bi-directional long short-term memory (CNN-Bi-LSTM) for multivariate wind speed temporal forecasting, enabling more efficient PEM operations. Compared to standard deep learning models, the CNN-Bi-LSTM architecture reduces the root mean square error by 52.5% and the mean absolute error by 56%, thereby enhancing hydrogen production forecasting. Simulation results show that a membrane thickness of 0.0252 mm and an operating temperature of 70% achieve the highest overall PEM efficiency of 63.611%. This study demonstrates the integration of deep learning-based forecasting with electrochemical modeling and SCADA datasets as a novel approach for wind-to-hydrogen production systems. 

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3. Time-Series AI Forecasting of Wind-to-Hydrogen Production

   https://doi.org/10.1115/ES2025-156886  

   Bounaim, Doha; Li, Gang (July 2025, American Society of Mechanical Engineers)

   Abstract The transition to carbon-neutral energy has increased the reliance upon renewable sources of energy, e.g., wind power, placing added demands on resilience and stability of the power grid. Wind-to-hydrogen production systems can be a solution for addressing these demands. By converting excess wind energy into hydrogen via electrolysis, these systems can effectively store the intermittent energy generated by wind turbines. This study discusses the application of two time-series prediction models, i.e., long short-term memory (LSTM) and bidirectional long short-term memory (Bi-LSTM), in forecasting the energy of wind farms, subsequently used for an assessment of hydrogen production means of a proton exchange membrane (PEM) electrolyzer. Using a dataset comprising wind speed, active power, and wind direction, inputs were normalized, and wind direction was transformed into sine and cosine components to retain circular characteristics. Bi-LSTM demonstrated superior accuracy with lower testing RMSE than LSTM. Integrating wind forecasts with a PEM electrolyzer model, incorporating critical electro-chemical parameters, revealed an optimal efficiency of 63.611% at a membrane thickness of 0.00254 cm and a temperature of 70°C. Bi-LSTM forecasts boosted hydrogen production by 5% to 8% compared to LSTM. 

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4. Towards Sustainable Integration: Techno-Economic Analysis and Future Perspectives of Co-Located Wind and Hydrogen Energy Systems

   https://doi.org/10.1115/1.4063971  

   Li, Honglin; Zhang, Jie (February 2024, Journal of Mechanical Design)

   Abstract This article presents a comprehensive study that focuses on the techno-economic analysis of co-located wind and hydrogen energy integration within an integrated energy system (IES). The research investigates four distinct cases, each exploring various configurations of wind farms, electrolyzers, batteries, hydrogen storage tanks, and fuel cells. To obtain optimal results, the study employs a sophisticated mathematical optimization model formulated as a mixed-integer linear program. This model helps determine the most suitable component sizes and hourly energy scheduling patterns. The research utilizes historical meteorological data and wholesale market prices from diverse regions as inputs, enhancing the study’s applicability and relevance across different geographical locations. Moreover, sensitivity analyses are conducted to assess the impact of hydrogen prices, regional wind profiles, and potential future fluctuations in component prices. These analyses provide valuable insights into the robustness and flexibility of the proposed IES configurations under varying market conditions and uncertainties. The findings reveal cost-effective system configurations, strategic component selections, and implications of future energy scenarios. Specifically comparing to configurations that only have wind and battery combinations, we find that incorporating an electrolyzer results in a 7% reduction in the total cost of the IES, and utilizing hydrogen as the storage medium for fuel cells leads to a 26% cost reduction. Additionally, the IES with hybrid hydrogen and battery energy storage achieves even higher and stable power output. This research facilitates decision-making, risk mitigation, and optimized investment strategies, fostering sustainable planning for a resilient and environmentally friendly energy future. 

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5. Numerical Modeling and Simulation of Ocean Current Turbines for Green Hydrogen Production

   https://doi.org/10.1109/PESGM52009.2025.11225177  

   Fung, Sasha; Nichols, Carter; Tang, Yufei; Sadoughipour, Mahsan; VanZwieten, James; Muljadi, Eduard; Hong, Tianqi; Alsenas, Gabriel (July 2025, IEEE)

   This paper presents a numerical modeling and simulation approach for hydrogen production using ocean current turbines (OCT) integrated with a proton exchange membrane (PEM) electrolyzer in a Simulink environment. As the demand for renewable energy grows, marine energy technologies, such as OCTs, are increasingly considered reliable sources for continuous green hydrogen production. This study examines two primary cases: (1) the performance of a PEM electrolyzer powered by an OCT under constant ocean current flow speed, evaluating system efficiency and hydrogen production dynamics, and (2) the hydrogen production potential within the Gulf Stream, a high-energy region for ocean current capture. This simulation framework models the OCT–PEM electrolyzer interaction, offering foundational insights into the feasibility and scalability of OCT-based hydrogen production, ultimately revealing a hydrogen production rate in the Gulf Stream of 20 mt/year for an OCT with a 10 m diameter rotor. This foundational study is the first step toward future real-time hardware-in-the-loop (HIL) simulations, advancing the integration of marine renewable energy into green hydrogen initiatives. 

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