Hydrogen Demand Research Articles

Estimating Hydrogen Price Based on Combined Machine Learning Models by 2060: Especially Comparing Regional Variations in China

Hydrogen energy’s economic efficiency is the key for China to obtain the goal of “carbon neutrality” by 2060. Different from the bottom-up methods and learning rate methods, this study estimates the hydrogen prices in China and typical regions by 2060 from the perspectives of economics and machine learning. The main factors influencing hydrogen price are determined from the perspectives of economics: hydrogen production, demand, and cost. A novel model is established based on combined machine learning models to predict hydrogen price. The hydrogen production is predicted based on the trained BP neural network model optimized by particle swarm optimization considering the uses of hydrogen. The hydrogen prices prediction model is built by applying a least squares support vector machine optimized by Bayesian optimization considering the hydrogen production, hydrogen demand, natural gas price, coal price, electricity price, and green hydrogen share. Moreover, the hydrogen prices in typical regions in China are compared with the average prices. The results show that the hydrogen price is estimated to decrease below CNY 12/kg and the hydrogen price in Northwest China will be lower than CNY 7.5/kg due to low electricity cost by 2060.

Integrated Planning Model for Optimizing Investments in Hydrogen Infrastructure Deployment: A Case Study of Yangtze River Delta Region, China

ABSTRACTThe hydrogen industry is of great significance for global energy system transition and decarbonization; thus, the holistic planning of hydrogen infrastructure from a supply chain level is necessary. To this end, first, the framework and methods in the hydrogen infrastructure deployment research were investigated systematically. Second, a hydrogen supply chain design (HSCD) model and a hydrogen refueling station location (HRSL) model were constructed. In view of the limitations of the two models, this paper reformulated the HRSL model through alternating the objective function and constraint, then proposed an integrated planning model using the ‐constraint method, which relaxed the assumption condition and expanded the decision‐making boundary of the original model, realizing the reasonable equilibrium of multiple segments. Finally, a case study was conducted using the Yangtze River Delta region as an example, and the main findings were as follows: (1) To meet the hydrogen demand of 25494t/d, the Yangtze River Delta region needs to build 94 coal gasification hydrogen plants, storage facilities with capacity of 12747t, transmission modes with capacity of 2333t/d, and 150 hydrogen refueling stations. (2) Railways have more advantages in large capacity and long‐distance hydrogen transmission. (3) Road segments with higher traffic flow often have a higher capture proportion. (4) The investment cost accounts for the majority of hydrogen production plant construction, which is 43.80%.

Exploring Economic Expansion of Green Hydrogen Production in South Africa

Hydrogen is a crucial energy carrier for the Clean Energy Sustainable Development Goals and the just transition to low/zero-carbon energy. As a top CO2-emitting country, hydrogen (especially green hydrogen) production in South Africa has gained momentum due to the availability of resources, such as solar energy, land, wind energy, platinum group metals (as catalysts for electrolysers), and water. However, the demand for green hydrogen in South Africa is insignificant, which implies that the majority of the production must be exported. Despite the positive developments, there are unclear matters, such as dependence on the national electricity grid for green hydrogen production and the cost of transporting it to Asian and European markets. Hence, this study aims to explore opportunities for economic expansion for sustainable production, transportation, storage, and utilisation of green hydrogen produced in South Africa. This paper uses a thematic literature review methodology. The key findings are that the available renewable energy sources, incentivizing the green economy, carbon taxation, and increasing the demand for green hydrogen in South Africa and Africa could decrease the cost of hydrogen from 3.54 to 1.40 €/kgH2 and thus stimulate its production, usage, and export. The appeal of green hydrogen lies in diversifying products to green hydrogen as an energy carrier, clean electricity, synthetic fuels, green ammonia and methanol, green fertilizers, and green steel production with the principal purpose of significant energy decarbonisation and economic and foreign earnings. These findings are expected to drive the African hydrogen revolution in agreement with the AU 2063 agenda.

Stochastic multi-layer optimization for cooperative multi-microgrid systems with hydrogen storage and demand response

Stochastic multi-layer optimization for cooperative multi-microgrid systems with hydrogen storage and demand response

A transactive energy cooperation scheduling for hydrogen-based community microgrid with refueling preferences of hydrogen vehicles

Hydrogen, as a high specific energy green carrier, is promising for power generation and transportation. This paper proposes a transactive energy cooperation framework for an end-user-oriented hydrogen-based community microgrid considering refueling preferences of hydrogen vehicles (HVs) and industrial hydrogen demand to minimize social cost. In this framework, an aggregator obtains stacking profits in both the electricity and hydrogen markets through flexible interconversion, storage, and interaction of electricity and hydrogen. For resident users, the refueling preferences of HVs are analyzed to reduce cost through demand response (DR). A novel energy density-weighted asymmetric Nash bargaining (DANB) method is designed to fairly assess the combined contribution, considering both electricity and hydrogen bargaining metrics for end-users. An alternating optimization procedure with the adaptive alternating direction method of multipliers (ADMM) algorithm is designed to solve the mixed-integer linear programming (MILP) problem in the social operation cost minimization process. Comparison results show the algorithm convergence advantage, the cost-effectiveness and scalability of the cooperation framework, as well as the rationality of the DANB and the flexibility of HVs in DR. Besides, the optimal configuration of the battery, the hydrogen tank, and the electrolyzer in the framework is explored, alongside the economic feasibility of electricity-hydrogen-electricity technology.

Forecasting regional green hydrogen demand in Mexico by 2035

Forecasting regional green hydrogen demand in Mexico by 2035

The hydrogen-water collision: Assessing water and cooling demands for large-scale green hydrogen production in a warming climate

Hydrogen is expected to play a critical role in future energy systems, projected to have an annual demand of 401-660 Mt by 2050. With large-scale green hydrogen projects advancing in water-scarce regions like Australia, Chile, and the Middle East and North Africa, understanding water requirements for large-scale green hydrogen production is crucial. Meeting this future hydrogen demand will necessitate 4,010 to 6,600 GL of demineralised water annually for electrolyser feedwater if dry cooling is employed, or an additional 6,015 to 19,800 GL for cooling water per year if evaporative cooling is employed. Using International Panel of Climate Change 2050 climate projections, this work evaluated the techno-economic implications of dry vs. evaporative cooling for large-scale electrolyser facilities under anticipated higher ambient temperatures. The study quantifies water demands, costs, and potential operational constraints, showing that evaporative cooling is up to 8 times cheaper to implement than dry cooling, meaning that evaporative cooling can be oversized to accommodate increased cooling demand of high temperature events at a lower cost. Furthermore, of the nations analysed herein, Chile emerged as having the lowest cost of hydrogen, owing to the lower projected ambient temperatures and frequency of high temperature events.

Techno-economic pathway for green hydrogen adoption in thermal applications across US small and medium manufacturing sectors

This study presents an optimization framework for designing a renewable hydrogen production system to meet the annual thermal energy demand of US small and medium-sized manufacturers (SMMs). The proposed system integrates solar photovoltaics, battery storage, and an electrolyser and the use of a mixed-integer linear programming model to determine the optimal sizing of components to minimize system cost while satisfying hydrogen demand under varying natural gas replacement scenarios. The optimization results indicate that increased PV and battery capacities are required for higher shares of hydrogen, while the size of the electrolyser remains constant within each industry. The economic analysis reveals significant cost escalation at higher hydrogen shares, with photovoltaics contributing more than half of the net present cost. Although a carbon pricing mechanism could help make the proposed system economically feasible, a cost analysis indicates that very high emission prices would be required to render hydrogen economically competitive with natural gas. The study thus recommends the use of targeted incentives and renewable grid integration to meet the challenges of transitioning SMMs to hydrogen and accelerate decarbonization.

A framework for high resolution coupled global electricity & hydrogen models based on integrated assessment model scenarios

Technology-rich Integrated Assessment Models (IAMs) offer the possibility to endogenously resolve hydrogen demand as a function of competition between technologies to meet energy service demands while considering wider interactions with climate and the economy. Such models are typically configured with relatively low spatial and temporal resolution thereby limiting their utility in studying future global hydrogen trade scenarios. This work presents a framework for the soft-linking of IAM scenarios to a higher spatial and temporal resolution global model. This framework provides an “engine” with which to generate future-looking coupled hydrogen & electricity models with the co-optimization of production capacity, storage, and transmission infrastructure that allow the user to vary key technoeconomic input parameters. The modelling framework is applied to an IAM scenario and validated against the parent IAM. The utility of the model when examining electricity & hydrogen commodity trade in future scenarios is then demonstrated.

Simulation of a decentralized floating offshore wind hydrogen production system with hydro-pneumatic energy storage and subsea isobaric hydrogen storage

Abstract Hydrogen production using offshore wind power is a promising solution for producing clean fuels in remote areas. However, the intermittency of offshore wind power poses significant challenges to Proton exchange membrane (PEM) water electrolysis systems. Besides, there is generally an imbalance between hydrogen production and hydrogen demand. In this study, hydro-pneumatic electricity energy storage and subsea isobaric hydrogen storage are integrated into the decentralized offshore wind hydrogen production system. The hydro-pneumatic energy storage unit (HPES) is used for mitigating the intermittency and fluctuation of wind power, thereby prolonging the lifespan of PEM electrolyzers. Subsea isobaric hydrogen storage unit is used for replacing conventional isochoric hydrogen storage. In this study, simulation models of a decentralized offshore wind hydrogen production system with various configurations are established with the software Simcenter Amesim 2021.1. The results show that an 83% reduction in the on/off operation can be achieved with the help of hydro-pneumatic electricity energy storage. The isobaric and isothermal storage of compressed hydrogen can be achieved with subsea isobaric hydrogen storage, saving compression energy and facilitating thermal management. In terms of the investigated decentralized offshore wind hydrogen production system, the amount of produced hydrogen is increased by less than 1% by integrating hydro-pneumatic energy storage and subsea isobaric hydrogen storage.

Low-emission hydrogen supply chain for oil refining: Assessment of large-scale production via electrolysis and gasification

The refining industry is a major hydrogen consumer, mainly relying on fossil fuel-derived hydrogen. While recent literature has focused on the production of hydrogen from water electrolysis, (waste) biomass gasification is another effective method for low-emission hydrogen production, and the refining industry is well positioned for its rapid adoption. This study conducts a techno-economic assessment of the whole supply chain of hydrogen for oil refining and analyses the LCA-climate change for residual biomass gasification and water electrolysis pathways. The potential economic and environmental benefits of combining both production methods are also analyzed. A real case study featuring four different scenarios, using data from a refinery, coupled with local data and future projections for potential curtailment and electricity prices, is included. The hourly hydrogen generation and demand profiles of the refinery were analyzed to accurately assess storage requirements. Results indicate that combining both technologies does not result in clear environmental or cost benefits, with residual biomass gasification emerging as the most advantageous configuration for the base case. Sensitivity analysis reveals that hydrogen produced via electrolysis may become more cost-effective if residual biomass prices are high enough. The importance of underground storage (salt cavern) is highlighted due to its low investment, while other storage methods can significantly increase the Levelized Cost of Hydrogen (LCOH). This study demonstrates that hydrogen production through gasification can be less carbon-intensive and more cost-competitive than electrolysis. Achieving low LCOH values and greenhouse gas emissions is feasible in all scenarios, indicating that both water electrolysis and residual biomass gasification are economically viable options for contributing to a low-emission global energy system.

Safe deep reinforcement learning-assisted two-stage energy management for active power distribution networks with hydrogen fueling stations

In a power-hydrogen coupled integrated energy system (PHCIES), hydrogen fueling stations (HFSs) with solar photovoltaic (PV) systems are crucial devices to support hydrogen demand and maintain a stable and near-zero pollutant emission-based PHCIES operation by producing a pollutant-free hydrogen. However, optimal operation management of HFSs is challenging because the electrolyzers, compressor, hydrogen storage system (HSS), and fuel cell in HFSs are interconnected and change dynamically under various operational environments. To resolve this issue, this paper presents a safe deep reinforcement learning (DRL)-assisted two-stage framework that ensures a reliable and economical PHCIES operation. In the first stage, day-ahead operational schedules of stand-alone PV systems, PV-enabled HFSs, on-load tap changers, and capacitor banks with hourly resolution are coordinated to minimize the system operation cost, electricity arbitrage cost, PV curtailment cost, and real power loss by solving a Volt-VAR control (VVC) optimization problem. In the second stage, a safe DRL algorithm with a 15-min resolution is employed to minimize the real power loss and maximize the HFS profit by rescheduling the reactive power of stand-alone PV systems and real/reactive power and hydrogen of HFSs. The proposed self-tuning adaptive safety module integrated in the DRL method ensures no violations of HSS’ state-of-hydrogen (SOH) and voltage magnitude in the PHCIES during the training process. Numerical examples conducted on a PHCIES (IEEE 33-bus system coupled with two PV-enabled HFSs) demonstrate the effectiveness of the proposed framework in terms of training convergence, SOH/voltage violation, real power loss, and profit of HFSs.

The impact of hydrogen on decarbonisation and resilience in integrated energy systems

The lack of clarity and uncertainty about hydrogen's role, demand, applications, and economics has been a barrier to the development of the hydrogen economy. In this paper, an optimisation model for the integrated planning and operation of hydrogen and electricity systems is presented to identify the role of hydrogen technologies and linepack in decarbonising energy systems, improving system flexibility, and enhancing energy system security and resilience against extreme weather events. The studies are conducted on Great Britain's (GB) 2050 net-zero electricity and gas transmission systems to analyse the hydrogen transport and capacity requirements within the existing infrastructure under different scenarios. This includes sensitivities on the level of flexibility, high gas prices, hydrogen production mixes, enabled reversibility of electrolysers, electricity generation cost, and hydrogen storage facilities. In all sensitivity scenarios, efficient hydrogen transport within the existing infrastructure is enabled by the optimal allocation of green and blue hydrogen sources, distributed storage facilities, and the intra-day flexibility provided by linepack. The findings highlight that increased renewable deployment transfers intermittency to the hydrogen network, requiring greater linepack flexibility compared to the current paradigm (up to 83%). Furthermore, the necessity of synergy between different gas and electricity systems components in providing flexibility, security, and resilience is quantified.

An up-to-date perspective of levelized cost of hydrogen for PV-based grid-connected power-to-hydrogen plants across all Italy

Green hydrogen holds potential for decarbonizing the energy sector, but high production costs are a major barrier. This study provides a comprehensive techno-economic-financial-environmental analysis of PV-based grid-connected hydrogen production plants, targeting hard-to-abate industries having constant hydrogen demand across all Italy. Using real hourly data, the Multi Energy System Simulator (MESS), an in-house developed rule-based tool, was employed and integrated with Genetic Algorithm for optimal plant sizing. The aim is to minimize the Levelized Cost of Hydrogen (LCOH) while complying with regulatory frameworks for green hydrogen incentives access. Key findings show that hydrogen storage is more advantageous than battery storage for supply-side flexibility, and the optimal PV-to-electrolyzer size ratio ranges from 1.8 in Southern Italy to 2.1 in Northern Italy, with hydrogen tank designed for daily storage. Considering photovoltaic, electrolyzer and battery aging models grid dependence increases by 60 % when comparing the first and worst year of operation and leads to a 7 % increase in LCOH. Transitioning from the strictest (hourly) to the least stringent (annual) temporal correlation increases certified green hydrogen by 22 %, while LCOH decreases by only 3 %, suggesting that the environmental benefits of stringent temporal requirements outweigh their moderate economic drawbacks. These findings underscore the need for additional national-level incentives to allow the deployment of this technology and achieving cost parity with grey hydrogen.

Techno-economic analysis of green hydrogen production from wind and solar along CPEC special economic zones in Pakistan

Hydrogen-based systems are garnering attention as part of efforts to achieve low-carbon emissions and net-zero targets. In developing countries, natural gas and coal serve as a primary source for hydrogen production, given their accessibility and cost-effectiveness. Green hydrogen presents a promising clean energy solution with significant potential to decarbonize the industrial sector. In this article, a techno-economic analysis has been performed for green hydrogen production using wind and solar as primary energy sources. The analysis is conducted at nine special economic zones (SEZs) and a free zone at Gwadar Sea Port using the Hybrid Optimization of Multiple Energy Resources (HOMER) Pro software. A hybrid energy system has been designed to meet the industrial electrical and hydrogen demand of 600 MWh/day and 60 tonnes H2 per day, respectively. A comparative analysis of on-grid and off-grid systems across all SEZs is performed for the optimal system. A sensitivity analysis is conducted on different parameters that could impact the levelized cost of hydrogen (LCOH). The results indicate that LCOH varies from 4.19 $/kg to 8.66 $/kg for off-grid and 2.12 $/kg to 4.65 $/kg for on-grid systems which is a competitive cost to other countries. The most feasible economic zones for green hydrogen production are found to be Dhabeji and Port Qasim with the lowest LCOH of 4.19 $/kg and 4.22 $/kg for off-grid, 2.12 $/kg and 2.36 $/kg for the grid-connected system, respectively. Dhabeji also exhibits the lowest CO2 emissions per year making itself the most feasible location for green hydrogen production. Grid-connected systems are a great opportunity for Pakistan to produce low-cost green hydrogen for industrial decarbonization and the country’s economic growth.

Technoeconomic analysis of supercritical water gasification of canola straw for hydrogen production

Production of hydrogen from renewable sources is gaining popularity to reduce our dependency on non-renewable fossil fuels to meet growing hydrogen demand. However, despite the great prospect of production of hydrogen from sustainable sources such as lignocellulosic biomass via supercritical water gasification (SCWG), it has not been commercialized at a large industrial scale. This is due to the lack of detailed economic analysis of SCWG of lignocellulosic biomass, owing to the complexity of the SCWG process and the heterogeneous nature of biomass. Therefore, to address this knowledge gap, in this study, a detailed technoeconomic analysis (TEA) of a conceptual SCWG pilot having the capacity to process 200 tons/day of canola straw for the production of green hydrogen was conducted. Mass and energy balance of conceptual pilot was performed using Aspen Plus ® simulation by utilizing experimental data and hydrogen yield of 41.62 mmol/g was obtained at optimized reaction conditions of 500 °C, 23 MPa, and 10 wt%. Economic analysis based on calculated mass and energy balance was performed using SuperPro software. Cash flow analysis for capital expenses (CAPEX) of 81 Million USD showed a high internal rate of return (IRR) of 38.9% and an undiscounted net present value (NPV) of 548 million USD. A minimum selling price (MSP) of 3.38 USD/kg H2 for produced hydrogen was estimated, which is lower than other renewable hydrogen production processes and comparable to non-renewable hydrogen production technologies. A high positive IRR and NPV, while a lower MSP showed that despite having a low technological readiness level (TRL) of 4, SCWG of lignocellulosic biomass is a technically feasible and economically viable process for the production of hydrogen. Furthermore, sensitivity analysis also revealed that capital expenses (CAPEX) and canola straw price had the highest influence on net present value (NPV) and MSP. However, overall NPV and MSP were highly stable to changes in parameters highlighting the robustness of the economic analysis.

Techno-Economic Analysis of Territorial Case Studies for the Integration of Biorefineries and Green Hydrogen

To achieve sustainable development, the transition from a fossil-based economy to a circular economy is essential. The use of renewable energy sources to make the overall carbon foot print more favorable is an important pre-requisite. In this context, it is crucial to valorize all renewable resources through an optimized local integration. One opportunity arises through the synergy between bioresources and green hydrogen. Through techno-economic assessments, this work analyzes four local case studies that integrate bio-based processes with green hydrogen produced via electrolysis using renewable energy sources. An analysis of the use of webGIS tools (i.e., Atlas of Biorefineries of IEA Bioenergy) to identify existing biorefineries that require hydrogen in relation to territories with a potential availability of green hydrogen, has never been conducted before. This paper provides an evaluation of the production costs of the target products as a function of the local green hydrogen supply costs. The results revealed that the impact of green hydrogen costs could vary widely, ranging from 1% to 95% of the total production costs, depending on the bio-based target product evaluated. Additionally, hydrogen demand in the target area could require an installed variable renewable energy capacity of 20 MW and 500 MW. On the whole, the local integration of biorefineries and green hydrogen could represent an optimal opportunity to make hydrogenated bio-based products 100% renewable.

Pd nanoislands-modified ZnO nanowire-network for sensitive and linear hydrogen sensing

To facilitate the safety use of hydrogen fuel in aerospace applications and in hydrogen fuel cell vehicles, there is a high demand for hydrogen sensing technology with both merits of high sensitivity and high linearity. However, it is hard to integrate these two advantages together in just one device. Here, we designed and developed a hydrogen-sensitive Pd nanoislands-modified ZnO nanowire-network structure (Pd-ZnO nanoisland structure) for sensitive and linear hydrogen sensing. By optimizing the size-to-gap ratio of Pd nanoislands, we enhanced the response of the Pd-ZnO nanoisland structure sensor (Pd-ZnO sensor) more than 12 times larger than that of the pure ZnO nanowire-network sensor (without Pd nanoislands), and decreased its limit of detection down to 100 ppb at 150 °C low temperature. On the premise of high sensitivity, the sensor also has a remarkable sensing linearity (R2 >0.9969) in the whole detecting range and the full operating temperature range as designed. The high sensitivity together with high linearity can maximize the signal-to-noise ratio and facilitate the calibration of sensors. For example, only the single calibration at a specific concentration can meet the sensing of Pd-ZnO sensors in the whole concentration range. This study of utilizing noble metal nanoislands to modify metal oxide semiconductors provides an opportunity for hydrogen sensors with high sensitivity and high linearity to be integrated into one device.

The Possibility of Using Hydrogen as a Green Alternative to Traditional Marine Fuels on an Offshore Vessel Serving Wind Farms

Achieving the required decarbonisation targets by the shipping industry requires a transition to technologies with zero or near-zero greenhouse gas (GHG) emissions. One promising shipping fuel with zero emission of exhaust gases (including CO2) is green hydrogen. This type of fuel, recognised as a 100% clean solution, is being investigated for feasible use on a service offshore vessel (SOV) working for offshore wind farms. This study aims to examine whether hydrogen may be used on an SOV in terms of the technical and economic challenges associated with the design process and other factors. In the analyses, a reference has been made to the current International Maritime Organization (IMO) guidelines and regulations. In this study, it was assumed that hydrogen would be directly combusted in a reciprocating internal combustion engine. This engine type was reviewed. In further research, hydrogen fuel cell propulsion systems will also be considered. The hydrogen demand was calculated for the assumed data of the SOV, and then the volume and number of high-pressure tanks were estimated. The analyses revealed that the SOV cannot undertake 14-day missions using hydrogen fuel stored in cylinders on board. These cylinders occupy 66% of the ship’s current volume, and their weight, including the modular system, accounts for 62% of its deadweight. The costs are over 100% higher compared to MDO and LNG fuels and 30% higher than methanol. The actual autonomy of the SOV with hydrogen fuel is 3 days.

Study on two-stage robust optimal configuration of integrated energy system considering CCUS and electric hydrogen production

Under the background of complementary and cascade utilization of multiple types of energy, how to carry out the capacity planning of economic, efficient, and low-carbon integrated energy system to meet the diversified needs of cooling, heating, electricity, and hydrogen loads is a challenging problem. This study conducted a two-stage robust optimization (RO) configuration of integrated energy system considering Carbon Capture Utilization and Storage (CCUS) and electric hydrogen production. In order to adapt the configuration scheme to the potential extreme operational scenarios, the uncertainty sets of new energy and load are constructed based on Latin hypercubic sampling. The efficacy is validated through the design of several scenarios and a comparison with alternative methods. The results indicate: 1) Strengthening of carbon emission constraints facilitates the cleaner and electrified transformation of energy. In the shift from “low carbon” to “zero carbon” scenario, the proportion of new energy capacity in power generation equipment increases from 45.1% to 79.8%; the proportion of electric chillers in cooling equipment increases from 1.3% to 7.2%, and reaches 60% under “zero carbon” scenario. 2) With a 36% reduction in the investment cost of new energy, the spatiotemporal transfer value of energy is realized, which increases the investment capacity of energy storage by about 7 times. 3) The configuration of hydrogen production and storage equipment to meet hydrogen demand reduces costs by 19% compared to direct purchasing in “low carbon” scenario. 4) Compared to other methods, the proposed method in this study is able to reduce the solution time and configuration cost.