China, People’s Republic

Fuel cell hybrid electric vehicles have the advantages of zero emission high efficiency and fast refuelling etc. and are one of the key directions for vehicle development. The energy management problem of fuel cell hybrid electric vehicles is the key technology for power distribution. The traditional power following strategy has the advantage of a real-time operation but the power correction is usually based only on the state of charge of a lithium battery which causes the operating point of the fuel cell to be in the region of a low efficiency. To solve this problem this paper proposes a hybrid power-following-fuzzy control strategy where a fuzzy logic control strategy is used to optimise the correction module based on the power following strategy which regulates the state of charge while correcting the output power of the fuel cell towards the efficient operating point. The results of the joint simulation with Matlab + Advisor under the Globally Harmonised Light Vehicle Test Cycle Conditions show that the proposed strategy still ensures the advantages of real-time energy management and for the hydrogen fuel cell the hydrogen consumption is reduced by 13.5% and 4.1% compared with the power following strategy and the fuzzy logic control strategy and the average output power variability is reduced by 14.6% and 5.1% respectively which is important for improving the economy of the whole vehicle and prolonging the lifetime of fuel cell.

This study addresses the optimization of urban integrated energy systems (UIESs) under uncertainty in peer-to-peer (P2P) electricity trading by introducing a two-stage robust optimization strategy. The strategy includes a UIES model with a photovoltaic (PV)–green roof hydrogen storage and cascading cold/heat energy subsystems. The first stage optimizes energy trading volume to maximize social welfare while the second stage maximizes operational profit considering uncertainties in PV generation and power prices. The Nested Column and Constraint Generation (NC&CG) algorithm enhances privacy and solution precision. Case studies with three UIESs show that the model improves economic performance energy efficiency and sustainability increasing profits by 1.5% over non-P2P scenarios. Adjusting the robustness and deviation factors significantly impacts P2P transaction volumes and profits allowing system operators to optimize profits and make risk-aligned decisions.

Underground coal gasification (UCG) is an emerging clean energy technology with significant potential for enhanced hydrogen production especially when coupled with water injection. Previous lab-scale studies have explored this potential but the mechanisms driving water-assisted hydrogen enhancement in large-scale deep UCG settings remain unclear. This study addresses this gap using numerical simulations of a large-scale deep coal model designed for hydrogen-oriented UCG. We investigated single-point and multipoint water injection stra tegies to optimize hydrogen production. Additionally we developed a retractable water injection technique to ensure sustained hydrogen output and effective cavity control. Our results indicate that the water–gas shift re action is crucial for increasing hydrogen production. Multipoint injection has been proven to be more effective than single-point injection increasing hydrogen production by 11% with an equal amount of steam. The introduction of retractable injection allows for continuous and efficient hydrogen generation with daily hydrogen production rates of approximately five times that of a conventional injection scheme and an increase in cumulative hydrogen production of approximately 105% over the same time period. Importantly the mul tipoint injection method also helped limit vertical cavity growth mitigating the risk of aquifer contamination. These findings support the potential of UCG as a low-carbon energy source in the transition to a hydrogen economy

In the context of “dual carbon” restrictions on carbon emissions have aĴracted widespread aĴention from researchers. In order to solve the issue of the insufficient exploration of the synergistic emission reduction effects of various low-carbon policies and technologies applied to multiple microgrids we propose a multi-microgrid electricity cooperation optimization scheduling strategy based on stepped carbon trading a hydrogen-doped natural gas system and P2G–CCS coupled operation. Firstly a multi-energy microgrid model is developed coupled with hydrogendoped natural gas system and P2G–CCS and then carbon trading and a carbon emission restriction mechanism are introduced. Based on this a model for multi-microgrid electricity cooperation is established. Secondly design optimization strategies for solving the model are divided into the dayahead stage and the intraday stage. In the day-ahead stage an improved alternating direction multiplier method is used to distribute the model to minimize the cooperative costs of multiple microgrids. In the intraday stage based on the day-ahead scheduling results an intraday scheduling model is established and a rolling optimization strategy to adjust the output of microgrid equipment and energy purchases is adopted which reduces the impact of uncertainties in new energy output and load forecasting and improves the economic and low-carbon operation of multiple microgrids. SeĴing up different scenarios for experimental validation demonstrates the effectiveness of the introduced low-carbon policies and technologies as well as the effectiveness of their synergistic interaction

Hydrogen globally recognized as the most efficient and clean energy carrier holds the potential to transform future energy systems through its use as a fuel and chemical resource. Although progress has been made in reversible hydrogen adsorption and release challenges in storage continue to impede widespread adoption. This review explores recent advancements in hydrogen storage materials and synthesis methods emphasizing the role of nanotechnology and innovative synthesis techniques in enhancing storage performance and addressing these challenges to drive progress in the field. The review provides a comprehensive overview of various material classes including metal hydrides complex hydrides carbon materials metal-organic frameworks (MOFs) and porous materials. Over 60 % of reviewed studies focused on metal hydrides and alloys for hydrogen storage. Additionally the impact of nanotechnology on storage performance and the importance of optimizing synthesis parameters to tailor material properties for specific applications are summarized. Various synthesis methods are evaluated with a special emphasis on the role of nanotechnology in improving storage performance. Mechanical milling emerges as a commonly used and cost-effective method for fabricating intermetallic hydrides capable of adjusting hydrogen storage properties. The review also explores hydrogen storage tank embrittlement mechanisms particularly subcritical crack growth and examines the advantages and limitations of different materials for various applications supported by case studies showcasing real-world implementations. The challenges underscore current limitations in hydrogen storage materials highlighting the need for improved storage capacity and kinetics. The review also explores prospects for developing materials with enhanced performance and safety providing a roadmap for ongoing advancements in the field. Key findings and directions for future research in hydrogen storage materials emphasize their critical role in shaping future energy systems.

Hydrogen fuel cell water-thermal management systems suffer from slow response time system vibration and large temperature fluctuations of load current changes. In this paper Logistic chaotic mapping adaptively adjusted inertia weight and asymmetric learning factors are integrated to enhance the particle swarm optimization (PSO) algorithm and combine it with fuzzy control to propose an innovative improved particle swarm optimization-Fuzzy control strategy. The use of chaotic mapping to initialize the particle population effectively enhances the variety within the population which subsequently improves the ability to search globally and prevents the algorithm from converging to a local optimum solution prematurely; by improving the parameters of learning coefficients and inertia weight the global and local search abilities are balanced at different stages of the algorithm so as to strengthen the algorithm’s convergence certainty while reducing the dependency on expert experience in fuzzy control. In this article a fuel cell experimental platform is constructed to confirm the validity and efficiency of the recommended strategy and the analysis reveals that the improved particle swarm optimization (IPSO) algorithm demonstrates better convergence performance than the standard PSO algorithm. The IPSO-Fuzzy-PID management approach is capable of providing a swift response and significantly diminishing the overshoot in the system’s performance to maintain the system’s safe and stable execution.

Hydrogen production modules (HPMs) play a crucial role in harnessing abundant photovoltaic power by producing and supplying hydrogen to factories resulting in significant operational cost reductions and efficient utilization of the photovoltaic panel output. However the output of photovoltaic power is stochastic which will affect the revenue of investing in an HPM. This paper presents a comprehensive analysis of HPMs starting with the modeling of their operational process and investigating their influence on distribution system operations. Building upon these discussions a deterministic optimization model is established to address the corresponding challenges. Furthermore a two-stage stochastic planning model is proposed to determine optimal locations and sizes of HPMs in distribution systems accounting for uncertainties. The objective of the twostage stochastic planning model is to minimize the distribution system’s operational costs plus the investment costs of the HPM subject to power flow constraints. To tackle the stochastic nature of photovoltaic power a data-driven algorithm is introduced to cluster historical data into representative scenarios effectively reducing the planning model’s scale. To ensure an efficient solution a Benders’ decomposition-based algorithm is proposed which is an iterative method with a fast convergence speed. The proposed model and algorithms are validated using a widely utilized IEEE 33-bus system through numerical experiments demonstrating the optimality of the HPM plan generated by the algorithm. The proposed model and algorithms offer an effective approach for decision-makers in managing uncertainties and optimizing HPM deployment paving the way for sustainable and efficient energy solutions in distribution systems. Sensitivity analysis verifies the optimality of the HPM’s siting and sizing obtained by the proposed algorithm which also reveals immense economic and environmental benefits.

Nowadays high-pressure hydrogen storage is the most commercially used technology owing to its high hydrogen purity rapid charging/discharging of hydrogen and low-cost manufacturing. Despite numerous reviews on hydrogen storage technologies there is a relative scarcity of comprehensive examinations specifically focused on high-pressure gaseous hydrogen storage and its associated materials. This article systematically presents the manufacturing processes and materials used for a variety of high-pressure hydrogen storage containers including metal cylinders carbon fiber composite cylinders and emerging glass material-based hydrogen storage containers. Furthermore it introduces the relevant principles and theoretical studies showcasing their advantages and disadvantages compared to conventional high-pressure hydrogen storage containers. Finally this article provides an outlook on the future development of high-pressure hydrogen storage containers.

Hydrogen as a clean energy carrier is of great potential to be an alternative fuel in the future. Proton exchange membrane (PEM) water electrolysis is hailed as the most desired technology for high purity hydrogen production and self-consistent with volatility of renewable energies has ignited much attention in the past decades based on the high current density greater energy efficiency small mass-volume characteristic easy handling and maintenance. To date substantial efforts have been devoted to the development of advanced electrocatalysts to improve electrolytic efficiency and reduce the cost of PEM electrolyser. In this review we firstly compare the alkaline water electrolysis (AWE) solid oxide electrolysis (SOE) and PEM water electrolysis and highlight the advantages of PEM water electrolysis. Furthermore we summarize the recent progress in PEM water electrolysis including hydrogen evolution reaction (HER) and oxygen evolution reaction (OER) electrocatalysts in the acidic electrolyte. We also introduce other PEM cell components (including membrane electrode assembly current collector and bipolar plate). Finally the current challenges and an outlook for the future development of PEM water electrolysis technology for application in future hydrogen production are provided.

To address the issues of energy wastage and uncertainty impacts associated with high levels of renewable energy integration a multi-objective distributed robust low-carbon optimization scheduling strategy for hydrogen-integrated Integrated Energy Systems (IES) is proposed. This strategy incorporates a green hydrogen trading mechanism and lowcarbon demand response. Firstly to leverage the low-carbon and clean characteristics of hydrogen energy an efficient hydrogen utilization model was constructed consisting of electricity-based hydrogen production waste heat recovery multi-stage hydrogen use hydrogen blending in gas and hydrogen storage. This significantly enhanced the system’s renewable energy consumption and carbon reduction. Secondly to improve the consumption of green hydrogen a novel reward–punishment green hydrogen certificate trading mechanism was proposed. The impact of green hydrogen trading prices on system operation was discussed promoting the synergistic operation of green hydrogen and green electricity. Based on the traditional demand-response model a novel low-carbon demand-response strategy is proposed with carbon emission factors serving as guiding signals. Finally considering the uncertainty of renewable energy an innovative optimal trade-off multi-objective distributed robust model was proposed which simultaneously considered low-carbon economic and robustness aspects. The model was solved using an improved adaptive particle swarm optimization algorithm. Case study results show that after introducing the reward–punishment green hydrogen trading mechanism and low-carbon demand response the system’s total cost was reduced by approximately 5.16% and 4.37% and carbon emissions were reduced by approximately 7.84% and 6.72% respectively. Moreover the proposed multi-objective distributed robust model not only considers the system’s economy low-carbon and robustness but also offers higher solving efficiency and optimization performance compared to multi-objective optimization methods.