Malaysia

Fuel cell (FC) technologies are often regarded as a sustainable alternative to conventional combustion-based energy systems due to their low environmental impact and high efficiency. Thorough environmental assessments using Life Cycle Assessment (LCA) methodologies are needed to understand and mitigate their impacts. However there has been a lack of comprehensive reviews on LCA studies across all major types of FCs. This study reviews and synthesizes results from 44 peer-reviewed LCA studies from 2015 to 2024 covering six major FC types: alkaline (AFC) direct methanol (DMFC) molten carbonate (MCFC) proton- exchange membrane (PEMFC) solid oxide (SOFC) and phosphoric acid (PAFC). The review provides an updated overview of LCA practices and results over the past decade while identifying methodological inconsistencies and gaps. PEMFCs are the most frequently assessed FC typology covering 49 % of the studies followed by SOFCs at 38 % with no studies on DMFCs. Only 11 % of comparative studies carry out inter-comparison between FC types. Discrepancies in system boundary definitions across studies are identified highlighting the need for standardization to enhance comparability between studies. Global Warming Potential (GWP) evaluated in 100 % of the studies is the most assessed impact category. Fuel supply in the use phase a major contributor to greenhouse gas (GHG) emissions is under-assessed as it is usually aggregated with Operation and Maintenance (O&M) phase instead of discussed separately. GWP of energy production by all FC typologies spans from 0.026 to 1.76 kg CO₂-equivalent per kWh. Insufficient quantitative data for a meta-analysis and limited inter-comparability across FC types are noted as critical gaps. The study highlights the need for future research and policies focusing on green hydrogen supply and circular economy practices to improve FC sustainability.

Hydrogen energy characterized by its abundant resources green and lowcarbon attributes and wide-ranging applications is a critical energy source for achieving carbon peaking and carbon neutrality goals. The operational efficiency of the hydrogen energy industrial chain is pivotal in determining the security of its supply chain and its contribution to China’s energy transition. This study investigates the efficiency of China’s hydrogen energy industrial chain by selecting 30 listed companies primarily engaged in hydrogen energy as the research sample. A three-stage data envelopment analysis (DEA) model is applied to assess the industry’s comprehensive technical efficiency pure technical efficiency and scale efficiency. Additionally kernel density estimation is utilized to analyze efficiency trends over time. Key factors influencing efficiency are identified and targeted recommendations are provided to enhance the performance and sustainability of the hydrogen energy industrial chain. These findings offer valuable insights to support the development and resilience of China’s hydrogen energy industry

Metal organic frameworks (MOFs) exhibit exceptional efficacy in hydrogen storage owing to their distinctive characteristics including elevated gravimetric densities rapid kinetics and reversibility. An in-depth look at existing literature indicates that while there are many studies using machine learning (ML) algorithms to develop predictive models for estimating hydrogen uptake by MOFs a great number of these models are not explainable. The novelty of this work lies in the integration of explainability approaches and ML models providing both accuracy and interpretability which is rarely addressed in existing studies. To fill this gap this paper attempts to develop explainable ML models for forecasting the hydrogen storage capacity of MOFs using three ML techniques including Bayesian regularized neural networks (BRANN) least squares support vector machines (LSSVM) and the extra tree algorithm (ET). An MOF databank comprising 1729 data points was assembled from literature. Surface area temperature pore volume and pressure were employed as input variables in this database. The findings demonstrate that of the three algorithms the ET intelligent model attained exceptional performance yielding precise estimates with a root mean square error (RMSE) of 0.1445 mean absolute error (MAE) of 0.0762 and a correlation coefficient (R2 ) of 0.995. In addition a novel contribution of this study is the generation of an explicit formula derived from BRANN enabling straightforward implementation of hydrogen storage predictions without requiring retraining of complex models. The sensitivity analysis employing Shapley Additive Explanation technique revealed that pressure and surface area were the most significant features influencing hydrogen storage with relevance values of 0.84 and 0.59 respectively. Furthermore the outlier detection evaluation using the leverage method showed that approximately 98 % of the utilized MOFs data are trustworthy and fell within the acceptable range. Altogether this work establishes a distinctive framework that combines accuracy interpretability and practical usability advancing the state of predictive modelling for hydrogen storage in MOFs.

The shift to renewable energy sources requires systems that are not only environmentally sustainable but also cost-effective and reliable. Mitigating the inherent intermittency of renewable energy optimally managing the hybrid energy storage efficiently integrating the microgrid with the power grid and maximizing the lifespan of system components are the significant challenges that need to be addressed. With this aim the paper proposes an economic viability assessment framework with an optimized dynamic operation approach to determine the most stable cost-effective and environmentally sound system for a specific location and demand. The green integrated hybrid microgrid combines photovoltaic (PV) generation battery storage an electrolyzer a hydrogen tank and a fuel cell tailored for deployment in remote areas with limited access to conventional infrastructure. The study’s control strategy focuses on managing energy flows between the renewable energy resources battery and hydrogen storage systems to maximize autonomy considering real-time changes in weather conditions load variations and the state of charge of both the battery and hydrogen storage units. The core system’s components include the interlinking converter which transfers power between AC and DC grids and the decentralized droop control approach which adjusts the converter’s output to ensure balanced and efficient power sharing particularly during overload conditions. A cloud-based Internet of Things (IoT) platform has been employed allowing continuous monitoring and data analysis of the green integrated microgrid to provide insights into the system's health and performance during the dynamic operation. The results presented in this paper confirmed that the proposed framework enabled the strategic use of energy storage particularly hydrogen systems. The optimal operational control of green hydrogen-integrated microgrid can indeed mitigate voltage and frequency fluctuations caused by variable solar input ensuring stable power delivery without reliance on the main grid or fossil fuel backups.

Fuel cell electric vehicles (FCEVs) provide a viable answer to transportation issues caused by fossil fuel limitations and environmental concerns. This review presents a thorough evaluation of the most recent advances in FCEV durability research. It addresses 4 major topics: component upgrades technical control techniques test optimization and durability prediction. Upgrades to components include improved catalysts bipolar plates gas diffusion layers proton exchange membranes and plant balancing. Technical control solutions include power energy temperature ventilation and control management. Stress acceleration and cold start tests are examples of test optimization whereas durability prediction requires parameter selection real-time monitoring dynamic modeling and lifespan prediction. This review also makes some novel recommendations targeted at improving the endurance of FCEVs. These include measures for raising public awareness lowering prices while increasing performance improving subsystems for greater durability updating health diagnostics to prevent performance deterioration and implementing supporting regulations to encourage industry upgrading. These findings are expected to accelerate the adoption of FCEVs and the transition to a more sustainable transportation system.

Climate change necessitates urgent action to decarbonize the transport sector. Sustainable vehicles represent crucial alternatives to traditional combustion engines. This study comprehensively compares four prominent sustainable vehicle technologies: biofuel-powered vehicles (BPVs) fuel cell vehicles (FCVs) electric vehicles (EVs) and solar vehicles. We examine each technology’s history development classification key components and operational principles. Furthermore we assess their sustainability through technical factors environmental impacts cost considerations and policy dimensions. Moreover the discussion section addresses the challenges and opportunities associated with each technology and assesses their social impact including public perception and adoption. Each technology offers promise for sustainable transportation but faces unique challenges. Policymakers industry stakeholders and researchers must collaborate to address these challenges and accelerate the transition toward a decarbonized transport future. Potential future research areas are identified to guide advancements in sustainable vehicle technologies.

Underground hydrogen (H2) storage (UHS) and carbon dioxide (CO2) geo-storage (CGS) are prominent methods of meeting global energy needs and enabling a low-carbon global economy. The pore-scale distribution reservoir-scale storage capacity and containment security of H2 and CO2 are significantly influenced by interfacial properties including the equilibrium contact angle (θE) and solid-liquid and solid-gas interfacial tensions (γSL and γSG). However due to the technical constraints of experimentally determining these parameters they are often calculated based on advancing and receding contact angle values. There is a scarcity of θE γSL and γSG data particularly related to the hydrogen structural sealing potential of caprock which is unavailable in the literature. Young's equation and Neumann's equation of state were combined in this study to theoretically compute these three parameters (θE γSL and γSG) at reservoir conditions for the H2 and CO2 geo-storage potential. Pure mica organic-aged mica and alumina nano-aged mica substrates were investigated to explore the conditions for rock wetting phenomena and the sealing potential of caprock. The results reveal that θE increases while γSG decreases with increasing pressure organic acid concentration and alkyl chain length. However γSG decreases with increasing temperatures for H2 gas and vice versa for CO2. In addition θE and γSL decrease whereas γSG increases with increasing alumina nanofluid concentration from 0.05 to 0.25 wt%. Conversely θE and γSL increase whereas γSG decreases with increasing alumina nanofluid concentration from 0.25 to 0.75 wt%. The hydrogen wettability of mica (a proxy of caprock) was generally less than the CO2 wettability of mica at similar physio-thermal conditions. The interfacial data reported in this study are crucial for predicting caprock wettability alterations and the resulting structural sealing capacity for UHS and CGS.

More than 150 Hydrogen refueling stations were built around the world in the past 10 years. Much of the technical issues with passenger fuel cell car were discussed and studied. However fuel cell passenger cars are still far from mass production stage. The problem mainly lies with the high cost of fuel cell car production and insufficient hydrogen refueling infrastructure. While the future of fuel cell passenger cars are not clear fuel cell for personal mobility devices like bicycles get more and more attractive. This is mainly due to the simplicity in system design and reducing cost of small size hydrogen fuel cells. But for this technology to be commercialized affordable hydrogen refueling stations is crucial. This study discusses solutions for small sized hydrogen refueling stations based on pressure equalization and simulates the Hydrogen utilization ratio based on different equipment setup. The study is also supported with the experimental data from prototype fuel cell vehicles developed by eMobility in Singapore.

Introduction: Several cities in Malaysia have established plans to reduce their CO2 emissions in addition to Malaysia submitting a Nationally Determined Contribution to reduce its carbon intensity (against GDP) by 45% in 2030 compared to 2005. Meeting these emissions reduction goals will require ajoint effort between governments industries and corporations at different scales and across sectors.<br/>Methods: In collaboration with national and sub-national stakeholders we developed and used a global integrated assessment model to explore emissions mitigation pathways in Malaysia and Kuala Lumpur. Guided by current climate action plans we created a suite of scenarios to reflect uncertainties in policy ambition level of adoption and implementation for reaching carbon neutrality. Through iterative engagement with all parties we refined the scenarios and focus of the analysis to best meet the stakeholders’ needs.<br/>Results: We found that Malaysia can reduce its carbon intensity and reach carbon neutrality by 2050 and that action in Kuala Lumpur can play a significant role. Decarbonization of the power sector paired with extensive electrification energy efficiency improvements in buildings transportation and industry and the use of advanced technologies such as hydrogen and carbon capture and storage will be Major drivers to mitigate emissions with carbon dioxide removal strategies being key to eliminate residual emissions.<br/>Discussion: Our results suggest a hopeful future for Malaysia’s ability to meet its climate goals recognizing that there may be technological social and financial challenges along the way. This study highlights the participatory process in which stakeholders contributed to the development of the model and guided the analysis as well as insights into Malaysia’s decarbonization potential and the role of multilevel governance.

With the rapid growth of photovoltaic installed capacity photovoltaic hydrogen production can effectively solve the problem of electricity mismatch between new energy output and load demand. Photovoltaic electrolysis systems pose unique challenges due to their nonlinear multivariable and complex nature. This paper presents a thorough investigation into the control methodologies for such systems focusing on both Maximum Power Point Tracking (MPPT) and electrolysis cell control strategies. Beginning with a comprehensive review of MPPT techniques including classical intelligent optimization and hybrid approaches the study delves into the intricate dynamics of Proton Exchange Membrane Electrolysis Cells (PEMEL). Considering the nonlinear and time-varying characteristics of PEMEL various control strategies such as Proportional-Integral-Derivative (PID) robust Model Predictive Control (MPC) and Fault Tolerant Control (FTC) are analyzed. Evaluation metrics encompass stability accuracy computational complexity and response speed. This paper provides a comparative analysis encapsulating the strengths and limitations of each MPPT and PEM control technique.

Considering rising environmental concerns and the energy transition towards sustainable energy Singapore’s power sector stands at a crucial juncture. This study explores the integration of grid infrastructure with both generated and imported renewable energy (RE) sources as a strategic pathway for the city-state’s energy transition to reach net-zero carbon emissions by 2050. Employing a combination of simulation modeling and data analysis for energy trading and advanced energy management technologies we examine the current and new grid infrastructure’s capacity to assimilate RE sources particularly solar photovoltaic and energy storage systems. The findings reveal that with strategic upgrades and smart grid technologies; Singapore’s grid can efficiently manage the variability and intermittency of RE sources. This integration is pivotal in achieving a higher penetration of renewables as well as contributing significantly to Singapore’s commitment to the Paris Agreement and sustainable development goals. While the Singapore’s power system has links to the Malay Peninsula the planned ASEAN regional interconnection might alter the grid operation in Singapore and possibly make Singapore a new green energy hub. The study also highlights the key challenges and opportunities associated with cross-border energy trade with ASEAN countries including the need for harmonized regulatory frameworks and incentives to foster public–private partnerships. The insights from this study could guide policymakers industry stakeholders and researchers offering a roadmap for a sustainable energy transition in Singapore towards meeting its 2050 carbon emission goals.

This study develops and optimizes a solar-powered system for hydrogen generation with oxygen and power coproducts addressing the need for efficient scalable carbon-free energy solutions. The system combines a linear parabolic collector a Steam Rankine cycle and a Proton Exchange Membrane Electrolyzer (PEME) to produce electricity for electrolysis. Thermodynamic modeling was accomplished in Engineering Equation Solver while a hybrid Artificial Intelligence (AI) framework combining Artificial Neural Networks and Genetic Algorithms in Statistica coupled with Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) decision support optimized technical and economic performance. Optimization considered seven key decision variables covering collector design thermodynamic inputs and component efficiencies. The optimization achieved energy and exergy efficiencies of 30.83 % and 26.32 % costing 47.02 USD/h and avoiding CO2 emissions equivalent to 190 USD/ton. Economic and exergy analyses showed the solar and hydrogen units had the highest costs (38.17 USD/h and 9.61 USD/h) with 4503 kWh of exergy destruction to generate 575 kWh of electricity. A case study across six cities suggested that Perth Bunbury and Adelaide with higher solar irradiance delivered the highest annual power and hydrogen outputs consistent with irradiance–electrolyzer correlation. Unlike conventional single-site studies this work delivers a climate-responsive multi-city analysis integrating solar thermal and PEME within an AI-driven framework. By linking techno-economic performance with quantified environmental value and co-production synergies of hydrogen oxygen and electricity the study highlights a novel pathway for scalable clean hydrogen measurable CO2 reductions and global decarbonization with future work focused on digital twins and dynamic uncertainty-aware optimization.

This study discusses energy management in thermal and electrical microgrids while taking heat pumps renewable sources thermal and hydrogen storages into account. The weighted total of the operating cost grid emissions level voltage and temperature deviation function and other factors makes up the objective function of the suggested method. The restrictions include the operationflexibility model of resources and storages micro-grid flexibility limits and optimum power flow equations. Point Estimation Method is used in this work to simulate load energy price and renewable phenomenon uncertainty. A fuzzy decision-making methodology is used to arrive at a compromise solution that satisfies network operators’ operational environmental and financial goals. The innovations of this paper include energy management of various smart microgrids simultaneous modeling of several indicators especially flexibility investigation of optimal performance of resources and storage devices and modeling of uncertainty considering low computational time and an accurate flexibility model. Numerical findings indicate that the fuzzy decision-making approach has the capability to reach a compromise point in which the objective functions approach their minimum values. The integration of the proposed uncertainty modeling with precise flexibility modeling results in a reduction in computational time when compared to stochastic optimization based on scenarios. For the compromise point and uncertainty modeling with PEM by efficiently managing resources and thermal and hydrogen storages scheme is capable of attaining high flexibility conditions. Compared to load flow studies the approach can enhance the operational environmental and economic conditions of smart microgrids by approximately 33–57% 68% and 33–68% respectively under these circumstances.