Transmission, Distribution & Storage

Fluid dispersion directly influences the transport mixing and efficiency of hydrogen storage in depleted gas reservoirs. Pore structure parameters such as pore size throat geometry and connectivity influence the complexity of flow pathways and the interplay between advective and diffusive transport mechanisms. Hence these factors are critical for predicting and controlling flow behavior in the reservoirs. Despite its importance the relationship between pore structure and dispersion remains poorly quantified particularly under elevated flow conditions. To address this gap this study employs pore network modeling (PNM) to investigate the influence of sandstone and carbonate structures on fluid flow properties at the micro-scale. Eleven rock samples comprising seven sandstone and four carbonate were analyzed. Pore network extraction from CT images was used to obtain detailed pore structure parameters and their statistical measures. Pore-scale simulations were conducted across 60 scenarios with varying average interstitial velocities and water as the injected fluid. Effluent hydrogen concentrations were measured to generate elution curves as a function of injected pore volumes (PV). This approach enables the assessment of the relationship between the dispersion coefficient and pore structure parameters across all rock samples at consistent average interstitial velocities. Additionally dispersivity and n-exponent values were calculated and correlated with pore structure parameters.

Subsurface porous media hydrogen storage could be a viable option to mitigate shortages in energy supply from renewable sources. In this work a scenario for such a storage is developed and the operation is simulated using a numerical model. A hypothetical storage site is developed based on an actual geological structure. The results of the simulations show that the storage can supply about 20 % of the average demand in electrical energy of the state of Schleswig-Holstein Germany for a week-long period.

This paper presents a mathematical model for a multi-period hydrogen supply chain design problem considering several design features not addressed in other studies. The model is formulated as a mixed-integer program allowing the production and storage facilities to be extended over time. Pipeline and tube trailer transport modes are considered for carrying hydrogen. The model also allows finding the optimal pipeline routes and the number of transport units. The objective is to obtain an efficient supply chain design within a given time frame in a way that the demand and carbon dioxide emissions constraints are satisfied and the total cost is minimized. A computer program is developed to ease the problem-solving process. The computer program extracts the geographical information from Google Maps and solves the problem using an optimization solver. Finally the applicability of the proposed model is demonstrated in a case study from Oman.

It is economical and efficient to use existing natural gas pipelines to transport hydrogen. The fast and accurate prediction of mixing uniformity of hydrogen injection in natural gas pipelines is important for the safety of pipeline transportation and downstream end users. In this study the computational fluid dynamics (CFD) method was used to investigate the hydrogen injection process in a T-junction natural gas pipeline. The coefficient of variation (COV) of a hydrogen concentration on a pipeline cross section was used to quantitatively characterize the mixing uniformity of hydrogen and natural gas. To quickly and accurately predict the COV a deep neural network (DNN) model was constructed based on CFD simulation data and the main influencing factors of the COV including flow velocity hydrogen blending ratio gas temperature flow distance and pipeline diameter ratio were taken as input nodes of the DNN model. In the model training process the effects of various parameters on the prediction accuracy of the DNN model were studied and an accurate DNN architecture was constructed with an average error of 4.53% for predicting the COV. The computational efficiency of the established DNN model was also at least two orders of magnitude faster than that of the CFD simulations for predicting the COV.

Underground hydrogen storage in depleted hydrocarbon reservoirs and aquifers has been proposed as a potential long-term solution to storing intermittently produced renewable electricity as the subsurface formations provide secure and large storage space. Various phenomena can lead to hydrogen loss in subsurface systems with the key cause being the trapping especially during the withdrawal cycle. Capillary trapping in particular is strongly related to the hysteresis phenomena observed in the capillary pressure/saturation and relative-permeability/saturation curves. This paper address two key points: (1) the sole impact of hysteresis in capillary pressure on hydrogen trapping during withdrawal cycles and (2) the dependency of optimal operational parameters (injection/withdrawal flow rate) and the reservoir characteristics such as permeability thickness and wettability of the porous medium on the remaining hydrogen saturation.<br/>Model<br/>To study the capillary hysteresis during underground hydrogen storage Killough [1] model was implemented in the MRST toolbox [2]. A comparative study was performed to quantify the impact of changes in capillary pressure behaviour by including and excluding the hysteresis and scanning curves. Additionally this study investigates the impact of injection/withdrawal rates and the aquifer permeability for various capillary and Bond numbers in a homogeneous system.<br/>Findings<br/>It was found that although the hydrogen storage efficiency is not considerably impacted by the inclusion of the capillary-pressure scanning curves the impact of capillary pressure on the well properties (withdrawal rate and pressure) can become significant. Higher injection and withdrawal rates does not necessarily lead to a better performance in terms of productivity. The productivity enhancement depends on the competition between gravitational capillary and viscous forces. The observed water upconing at relatively high capillary numbers resulted in low hydrogen productivity. highlighting the importance of well design and placement.

Smart energy networks provide an effective means to accommodate high penetrations of variable renewable energy sources like solar and wind which are key for the deep decarbonisation of energy production. However given the variability of the renewables as well as the energy demand it is imperative to develop effective control and energy storage schemes to manage the variable energy generation and achieve desired system economics and environmental goals. In this paper we introduce a hybrid energy storage system composed of battery and hydrogen energy storage to handle the uncertainties related to electricity prices renewable energy production and consumption. We aim to improve renewable energy utilisation and minimise energy costs and carbon emissions while ensuring energy reliability and stability within the network. To achieve this we propose a multi-agent deep deterministic policy gradient approach which is a deep reinforcement learning-based control strategy to optimise the scheduling of the hybrid energy storage system and energy demand in real time. The proposed approach is model-free and does not require explicit knowledge and rigorous mathematical models of the smart energy network environment. Simulation results based on real-world data show that (i) integration and optimised operation of the hybrid energy storage system and energy demand reduce carbon emissions by 78.69% improve cost savings by 23.5% and improve renewable energy utilisation by over 13.2% compared to other baseline models; and (ii) the proposed algorithm outperforms the state-of-the-art self-learning algorithms like the deep-Q network.

Hydrogen and electricity derived from renewable sources present feasible alternative energy options for the decarbonisation of the transportation and power sectors. This study presents the utilisation of hydrogen generated from solar and wind energy resources as a clean fuel for mobility and backup storage for stationary applications under economic and environmental uncertainties. This is achieved by developing a detailed technoeconomic model of an integrated system consisting of a hydrogen refuelling station and an electric power generation system using Mixed Integer Quadratic Constrained Programming (MIQCP) which is further relaxed to Mixed Integer Linear Programming (MILP). The model is implemented in the Advanced Interactive Multidi mensional Modelling Software (AIMMS) and considering the inherent uncertainties in the wind resource solar resource costs and discount rate the total cost of the three configurations (Hybrid PV-Wind Standalone PV and Standalone wind energy system) was minimised using robust optimisation technique and the corresponding optimal sizes of the components levelised cost of energy (LCOE) excess energy greenhouse emission avoided and carbon tax were evaluated. The levelised cost of the deterministic optimisation solution for all the config uration ranges between 0.0702 $/kWh to 0.0786 $/kWh while the levelised cost of the robust optimisation solution ranges between 0.07188 $/kWh to 0.1125 $/kWh. The proposed integration has the advantages of affordable hydrogen and electricity prices minimisation of carbon emissions and grid export of excess energy.

The most challenging aspect of developing a green hydrogen economy is long-distance oceanic transportation. Hydrogen liquefaction is a transportation alternative. However the cost and energy consumption for liquefaction is currently prohibitively high creating a major barrier to hydrogen supply chains. This paper proposes using solid nitrogen or oxygen as a medium for recycling cold energy across the hydrogen liquefaction supply chain. When a liquid hydrogen (LH2) carrier reaches its destination the regasification process of the hydrogen produces solid nitrogen or oxygen. The solid nitrogen or oxygen is then transported in the LH2 carrier back to the hydrogen liquefaction facility and used to reduce the energy consumption cooling gaseous hydrogen. As a result the energy required to liquefy hydrogen can be reduced by 25.4% using N2 and 27.3% using O2. Solid air hydrogen liquefaction (SAHL) can be the missing link for implementing a global hydrogen economy.

Underground hydrogen storage (UHS) in salt caverns is a sustainable energy solution to reduce global warming. Salt rocks provide an exceptional insulator to store natural hydrogen as they have low porosity and permeability. Nevertheless the salt creeping nature and hydrogeninduced impact on the operational infrastructure threaten the integrity of the injection/production wells. Furthermore the scarcity of global UHS initiatives indicates that investigations on well integrity remain insufficient. This study strives to profoundly detect the research gap and imperative considerations for well integrity preservation in UHS projects. The research integrates the salt critical characteristics the geomechanical and geochemical risks and the necessary measurements to maintain well integrity. The casing mechanical failure was found as the most challenging threat. Furthermore the corrosive and erosive effects of hydrogen atoms on cement and casing may critically put the well integrity at risk. The research also indicated that the simultaneous impact of temperature on the salt creep behavior and hydrogen-induced corrosion is an unexplored area that has scope for further research. This inclusive research is an up-to-date source for analysis of the previous advancements current shortcomings and future requirements to preserve well integrity in UHS initiatives implemented within salt caverns.

Hydrogen is a key energy carrier playing a vital role in sustainable energy systems. This review provides a comparative analysis of physical chemical and innovative hydrogen storage methods from technical environmental and economic perspectives. It has been identified that compressed and liquefied hydrogen are predominantly utilized in transportation applications while chemical transport is mainly supported by liquid organic hydrogen carriers (LOHC) and ammonia-based systems. Although metal hydrides and nanomaterials offer high hydrogen storage capacities they face limitations related to cost and thermal management. Furthermore artificial intelligence (AI)- and machine learning (ML)-based optimization techniques are highlighted for their potential to enhance energy efficiency and improve system performance. In conclusion for hydrogen storage systems to achieve broader applicability it is recommended that integrated approaches be adopted—focusing on innovative material development economic feasibility and environmental sustainability