Publications

Hydrogen is considered the primary energy source of the future. The best use of hydrogen is in microgrids that have renewable energy sources (RES). These sources have a small impact on the environment when it comes to carbon dioxide (CO2 ) emissions and a power generation cost close to that of conventional power plants. Therefore it is important to study the impact on the environment and the power cost. The proposed microgrid comprises loads RESs (micro-hydro and photovoltaic power plants) a hydrogen storage tank an electric battery and fuel cell vehicles. The power cost and CO2 emissions are calculated and compared for various scenarios including the four seasons of the year compared with the work of other researchers. The purpose of this paper is to continuously supply the loads and vehicles. The results show that the microgrid sources and hydrogen storage can supply consumers during the spring and summer. For winter and autumn the power grid and steam reforming of natural gas must be used to cover the demand. The highest power costs and CO2 emissions are for winter while the lowest are for spring. The power cost increases during winter between 20:00 and 21:00 by 336%. The CO2 emissions increase during winter by 8020%.

To reduce carbon emissions generation of electricity from combustion systems is being replaced by renewable resources. However the most abundant renewable sources – solar and wind – are not dispatchable vary diurnally and are subject to intermittency and produce electricity at times in excess of demand (excess production). To manage this variability and capture the excess renewable energy energy storage technologies are being developed and deployed such as battery energy storage (BES) hydrogen production with electrolyzers (ELY) paired with hydrogen energy storage (HES) and fuel cells (FCs) and renewable natural gas (RNG) production. While BES may be better suited for short duration storage hydrogen is suited for long duration storage and RNG can decarbonize the natural gas system. California Senate Bill 100 (SB100) sets a goal that all retail electricity sold in the State must be sourced from renewable and zero-carbon resources by 2045 raising the questions of which set of technologies and in what proportion are required to meet the 2045 target in the required timeframe as well as the role of the natural gas infrastructure if any. To address these questions this study combines electric grid dispatch modeling and optimization to identify the energy storage and dispatchable technologies in 5-year increments from 2030 to 2045 required to transition from a 60% renewable electric grid in 2035 to a 100% renewable electric grid in 2045. The results show that by utilizing the established natural gas system to store and transmit hydrogen and RNG the deployment of battery energy storage is dramatically reduced. The required capacity for BES in 2045 for example is 40 times lower by leveraging the natural gas infrastructure with a concomitant reduction in cost and associated challenges to transform the electric grid.

A substantial CO2-emmissions abatement from the steel sector seems to be a challenging task without support of so-called “breakthrough technologies” such as the hydrogen-based direct reduction process. The scope of this work is to evaluate both the potential for the implementation of green hydrogen generated via electrolysis in the direct reduction process as well as the constraints. The results for this process route are compared with both the well-established blast furnace route as well as the natural gas-based direct reduction which is considered as a bridge technology towards decarbonization as it already operates with H2 and CO as main reducing agents. The outcomes obtained from the operation of a 6-MW PEM electrolysis system installed as part of the H2FUTURE project provide a basis for this analysis. The CO2 reduction potential for the various routes together with an economic study are the main results of this analysis. Additionally the corresponding hydrogen- and electricity demands for large-scale adoption across Europe are presented in order to rate possible scenarios for the future of steelmaking towards a carbon-lean industry.

This study proposed a deep reinforcement learning-based energy management strategy (DRL-EMS) that can be applied to a hybrid electric ship propulsion system (HSPS) integrating liquid hydrogen (LH2 ) fuel gas supply system (FGSS) proton-exchange membrane fuel cell (PEMFC) and lithium-ion battery systems. This study analyzed the optimized performance of the DRL-EMS and the operational strategy of the LH2 -HSPS. To train the proposed DRL-EMS a reward function was defined based on fuel consumption and degradation of power sources during operation. Fuel consumption for ship propulsion was estimated with the power for balance of plant (BOP) of the LH2 FGSS and PEMFC system. DRL-EMS demonstrated superior global and real-time optimality compared to benchmark algorithms namely dynamic programming (DP) and sequential quadratic programming (SQP)-based EMS. For various operation cases not used in training DRL-EMS resulted in 0.7% to 9.2% higher operating expenditure compared to DP-EMS. Additionally DRL-EMS was trained to operate 60% of the total operation time in the maximum efficiency range of the PEMFC system. Different hydrogen fuel costs did not affect the optimized operational strategy although the operating expenditure (OPEX) was dependent on the hydrogen fuel cost. Different capacities of the battery system did not considerably change the OPEX.

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.

The Global Hydrogen Review is an annual publication by the International Energy Agency that tracks hydrogen production and demand worldwide as well as progress in critical areas such as infrastructure development trade policy regulation investments and innovation. The report is an output of the Clean Energy Ministerial Hydrogen Initiative and is intended to inform energy sector stakeholders on the status and future prospects of hydrogen while also informing discussions at the Hydrogen Energy Ministerial Meeting organised by Japan. Focusing on hydrogen’s potentially major role in meeting international energy and climate goals the Review aims to help decision makers fine-tune strategies to attract investment and facilitate deployment of hydrogen technologies at the same time as creating demand for hydrogen and hydrogen-based fuels. It compares real-world developments with the stated ambitions of government and industry. This year’s report includes a focus on demand creation for low-emission hydrogen. Global hydrogen use is increasing but demand remains so far concentrated in traditional uses in refining and the chemical industry and mostly met by hydrogen produced from unabated fossil fuels. To meet climate ambitions there is an urgent need to switch hydrogen use in existing applications to low-emission hydrogen and to expand use to new applications in heavy industry or long-distance transport.

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.

There is a pressing need to rapidly and massively scale up negative carbon strategies such as carbon capture and storage (CCS). At the same time large-scale CCS can enable ramp-up of large-scale hydrogen production a key component of decarbonized energy systems. We argue here that the safest and most practical strategy for dramatically increasing CO2 storage in the subsurface is to focus on regions where there are multiple partially depleted oil and gas reservoirs. Many of these reservoirs have adequate storage capacity are geologically and hydrodynamically well understood and are less prone to injection-induced seismicity than saline aquifers. Once a CO2 storage facility is up and running it can be used to store CO2 from multiple sources. Integration of CCS with hydrogen production appears to be an economically viable strategy for dramatically reducing greenhouse gas emissions over the next decade particularly in oil- and gas-producing countries where there are numerous depleted reservoirs that are potentially suitable for large-scale carbon storage.

Proton exchange membrane fuel cells (PEMFCs) are attracting attention for their green energy-saving and high-efficiency advantages becoming one of the future development trends of renewable energy utilization. However there are still deficiencies in the gas supply system control strategy that plays a crucial role in PEMFCs which limits the rapid development and application of PEMFCs. This paper provides a comprehensive and in-depth review of the PEMFC air delivery system (ADS) and hydrogen delivery system (HDS) operations. For the ADS the advantages and disadvantages of the oxygen excess ratio (OER) oxygen pressure and their decoupling control strategies are systematically described by the following three aspects: single control hybrid control and intelligent algorithm control. Additionally the optimization strategies of the flow field or flow channel for oxygen supply speeds and distribution uniformity are compared and analyzed. For the HDS a systematic review of hydrogen recirculation control strategies purge strategies and hydrogen flow control strategies is conducted. These strategies contribute a lot to improving hydrogen utilization rates. Furthermore hydrogen supply pressure is summarized from the aspects of hybrid control and intelligent algorithm control. It is hoped to provide guidance or a reference for research on the HDS as well as the ADS control strategy and optimization strategy

Clean energy technological innovations are widely acknowledged as a prerequisite to achieving ambitious longterm energy and climate targets. However the optimal speed of their adoption has been parsimoniously studied in the literature. This study seeks to identify the optimal intensity of moving to a green hydrogen electricity sector in Greece using the OSeMOSYS energy modeling framework. Green hydrogen policies are evaluated first on the basis of their robustness against uncertainty and afterwards against conflicting performance criteria and for different decision-making profiles towards risk by applying the VIKOR and TOPSIS multi-criteria decision aid methods. Although our analysis focuses exclusively on the power sector and compares different rates of hydrogen penetration compared to a business-as-usual case without considering other game-changing innovations (such as other types of storage or carbon capture and storage) we find that a national transition to a green hydrogen economy can support Greece in potentially cutting at least 16 MtCO2 while stimulating investments of EUR 10–13 bn. over 2030–2050.