Size Of Components Research Articles

Effects of energy sharing and electricity tariffs on optimal sizing of PV-battery systems for grid-connected houses

This paper investigates the optimal sizing of solar photovoltaic (PV) and battery energy storage (BES) for grid-connected houses based on mutually-agreed energy sharing prices by considering the flat and time-of-use (TOU) tariffs. The grid-tied house with PV-BES, referred to house 1 (H1) in the paper shares electricity with house 2 (H2) whenever needed with mutually-agreed electricity tariffs. The main objective function of the study is to minimize the cost of electricity (COE) for the H1 while decreasing COE for H2. Eight different schemes are investigated with the combination of flat and TOU tariffs for buying, selling, and mutually-agreed rates, respectively. For each scheme, optimal sizing of components and COE for both houses are evaluated. Realistic hourly-arranged annual data of temperature, solar irradiation, and load consumption of two houses are used as the input data. For each scheme, the results are compared with the situation when H1 does not have PV-BES and there is no electricity sharing. Sensitivity, operational, and uncertainty analyses are provided for the scheme with the lowest COE.

Development and clinical validation of a deep learning-based knee CT image segmentation method for robotic-assisted total knee arthroplasty.

This study aimed to develop a novel deep convolutional neural network called Dual-path Double Attention Transformer (DDA-Transformer) designed to achieve precise and fast knee joint CT image segmentation and to validate it in robotic-assisted total knee arthroplasty (TKA). The femoral, tibial, patellar, and fibular segmentation performance and speed were evaluated and the accuracy of component sizing, bone resection and alignment of the robotic-assisted TKA system constructed using this deep learning network was clinically validated. Overall, DDA-Transformer outperformed six other networks in terms of the Dice coefficient, intersection over union, average surface distance, and Hausdorff distance. DDA-Transformer exhibited significantly faster segmentation speeds than nnUnet, TransUnet and 3D-Unet (p<0.01). Furthermore, the robotic-assisted TKA system outperforms the manual group in surgical accuracy. DDA-Transformer exhibited significantly improved accuracy and robustness in knee joint segmentation, and this convenient and stable knee joint CT image segmentation network significantly improved the accuracy of the TKA procedure.

Triadic percolation induces dynamical topological patterns in higher-order networks.

Triadic interactions are higher-order interactions which occur when a set of nodes affects the interaction between two other nodes. Examples of triadic interactions are present in the brain when glia modulate the synaptic signals among neuron pairs or when interneuron axo-axonic synapses enable presynaptic inhibition and facilitation, and in ecosystems when one or more species can affect the interaction among two other species. On random graphs, triadic percolation has been recently shown to turn percolation into a fully fledged dynamical process in which the size of the giant component undergoes a route to chaos. However, in many real cases, triadic interactions are local and occur on spatially embedded networks. Here, we show that triadic interactions in spatial networks induce a very complex spatio-temporal modulation of the giant component which gives rise to triadic percolation patterns with significantly different topology. We classify the observed patterns (stripes, octopus, and small clusters) with topological data analysis and we assess their information content (entropy and complexity). Moreover, we illustrate the multistability of the dynamics of the triadic percolation patterns, and we provide a comprehensive phase diagram of the model. These results open new perspectives in percolation as they demonstrate that in presence of spatial triadic interactions, the giant component can acquire a time-varying topology. Hence, this work provides a theoretical framework that can be applied to model realistic scenarios in which the giant component is time dependent as in neuroscience.

Enhancing residential energy access with optimized stand-alone hybrid solar-diesel-battery systems in Buea, Cameroon

This study examined the optimal size of an autonomous hybrid renewable energy system (HRES) for a residential application in Buea, located in the southwest region of Cameroon. Two hybrid systems, PV-Battery and PV-Battery-Diesel, have been evaluated in order to determine which was the better option. The goal of this research was to propose a dependable, low-cost power source as an alternative to the unreliable and highly unstable electricity grid in Buea. The decision criterion for the proposed HRES was the cost of energy (COE), while the system’s dependability constraint was the loss of power supply probability (LPSP). The crayfish optimization algorithm (COA) was used to optimize the component sizes of the proposed HRES, and the results were contrasted to those obtained from the whale optimization algorithm (WOA), sine cosine algorithm (SCA), and grasshopper optimization algorithm (GOA). The MATLAB software was used to model the components, criteria, and constraints of this single-objective optimization problem. The results obtained after simulation for LPSP of less than 1% showed that the COA algorithm outperformed the other three techniques, regardless of the configuration. Indeed, the COE obtained using the COA algorithm was 0.06%, 0.12%, and 1% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively, for the PV-Battery configuration. Likewise, for the PV-Battery-Diesel configuration, the COE obtained using the COA algorithm was 0.065%, 0.13%, and 0.39% lower than the COE provided by the WOA, SCA, and GOA algorithms, respectively. A comparative analysis of the outcomes obtained for the two configurations indicated that the PV-Battery-Diesel configuration exhibited a COE that was 4.32% lower in comparison to the PV-Battery configuration. Finally, the impact of the LPSP reduction on the COE was assessed in the PV-Battery-Diesel configuration. The decrease in LPSP resulted in an increase in COE owing to the nominal capacity of the diesel generator.

Correlation between Component Size and Functional Knee Score in Patients Undergoing Bilateral Simultaneous Total Knee Arthroplasty: A Cross-Sectional Study

Abstract Background: Simultaneous bilateral total knee arthroplasty (simBTKA) has been a favored surgical solution to reduce costs and patient suffering. We aimed to evaluate the rate of asymmetry of component size in patients undergoing simBTKA and its impact on knee function as we believe that implant asymmetry may affect the functional outcomes in those patients. Methods: A cross-sectional study design was done on on 60 patients (120 knees) with simBTKA using patient-specific templating (PST). Patients were included if they had Kellgren–Lawrence Grade III-IV osteoarthritis. Revision surgeries, staged BTKA, or patients with bone defects, valgus deformity, severe varus deformity (defined as above 20°), and extra-articular deformities were excluded from the study. The outcome measures were interlimb component asymmetry, Knee Society Score (KSS), and range of motion (ROM). The comparison between pre- and postoperative findings was done using a dependent t-test. Results: A total of 29 (48.34%) patients had symmetrical femoral and tibial components, whereas the rest had asymmetry, of them, 11.7% had both femoral and tibial component size asymmetry. There were no statistically significant differences between the changes in KSS and ROM in the smaller implant and larger implant groups (P = 0.5 and P = 0.4, respectively). The total number of complications was eight and as follows: superficial infection, aseptic loosening, rupture of the patellar tendon after a bathroom fall, anemia requiring blood transfusion, residual varus deformity, deep venous thrombosis, periprosthetic fracture, and malalignment. Conclusion: There is no correlation between the interlimb component asymmetry and the knee function. However, there was statistically significant improvement from preoperative to postoperative KSS and ROM in small and large implants.

On the Size and Weight of Passive Components: Scaling Trends for High-Density Power Converter Designs

On the Size and Weight of Passive Components: Scaling Trends for High-Density Power Converter Designs

Preparation of CrB2 mixtures and formation of briquettes for drill bit composites

The research described in the article concerns advanced methods of creating composite materials for drill bits used in extreme conditions of deep wells. The main attention is paid to development of multicomponent mixtures that ensure uniform distribution of powders of tungsten carbide, cobalt, chromium diboride and diamond. This is achieved through careful selection of particle size and proportions, which is critically important to ensure high strength and wear resistance of final materials. The Spark Plasma Sintering (SPS) method was chosen for the production of samples of carbide matrices and composite diamond-containing materials because of its ability to provide high density and uniformity of the material. SPS allows temperature and pressure control during sintering process, which contributes to the formation of materials with optimal physical properties. The article also discusses in detail design parameters of the diamond bit, including the choice of grain size of diamond powder and other components. These meters have been optimized to max-imize drilling efficiency and extend tool life. The diamond powders used in study were obtained from De Beers, South Africa and are characterized by high quality and uniformity. The results presented in the article can have a significant impact on future production of drilling tools, offering new approaches to choice of materials and methods of their processing, which ultimately can lead lower drilling costs and increased process safety. These innovations can also contribute to more efficient development of new fields, which is of great importance for the energy industry and the mining industry.

Treatment effects in epilepsy: a mathematical framework for understanding response over time.

Epilepsy is a neurological disorder characterized by recurrent seizures, affecting over 65 million people worldwide. Treatment typically commences with the use of anti-seizure medications, including both mono- and poly-therapy. Should these fail, more invasive therapies such as surgery, electrical stimulation and focal drug delivery are often considered in an attempt to render the person seizure free. Although a significant portion ultimately benefit from these treatment options, treatment responses often fluctuate over time. The physiological mechanisms underlying these temporal variations are poorly understood, making prognosis a significant challenge when treating epilepsy. Here we use a dynamic network model of seizure transition to understand how seizure propensity may vary over time as a consequence of changes in excitability. Through computer simulations, we explore the relationship between the impact of treatment on dynamic network properties and their vulnerability over time that permit a return to states of high seizure propensity. For small networks we show vulnerability can be fully characterised by the size of the first transitive component (FTC). For larger networks, we find measures of network efficiency, incoherence and heterogeneity (degree variance) correlate with robustness of networks to increasing excitability. These results provide a set of potential prognostic markers for therapeutic interventions in epilepsy. Such markers could be used to support the development of personalized treatment strategies, ultimately contributing to understanding of long-term seizure freedom.

Analysis of Power-to-gas-to-X systems with metal hydride storage based on coupled electrochemical and thermodynamic simulation

Power-to-gas-to-X systems consisting of photovoltaic cells, proton-exchange membrane electrolysis, hydrogen storage based on metal hydrides, proton-exchange membrane fuel cells and buffer batteries could be used to meet heat and electricity demands of homes, businesses, or small districts. The actual size of the individual components and their interplay have to be optimized for the technical and economic feasibility of the overall system. A simulation-based optimization workflow would be a suitable way to accomplish this task, but there are hardly any tools that can simultaneously simulate power, fluid and heat flows of such systems and efficiently perform their optimization. In this paper, a multiphysical energy system simulation and optimization tool is introduced which models electrochemical and thermodynamic processes simultaneously, including modern equations of state and an own numerical solver for the arising differential–algebraic system of equations, and provides new methods for the calibration of parameters of the metal hydride storage, proton-exchange membrane electrolyzer and fuel cell as well as a metamodel-based approach for sizing optimization. As a demonstrator for the novel tool, a simulation model of a hydrogen lab is successfully set up based on experimental results. The novel tool is able to extract polarization and jump curves of the fuel cell, determine a first temperature and pressure dependency of the efficiency of the electrolysis coupled with the metal hydride storage and speed up sizing optimization through metamodeling by a factor 262.1 at 4.9% and 32.7 at 3.3% accuracy.

Performance evaluation of standalone new solar energy system of hybrid PV/electrolyzer/fuel cell/MED-MVC with hydrogen production and storage for power and freshwater building demand

To achieve zero carbon emissions, increasing the scale of renewable energy implementation is imperative. Hydrogen is considered the prospective solution for sustainable energy due to its dual nature as a carbon-neutral fuel and an efficient storage medium for renewable energy sources. So, in this work, a standalone new hybrid energy system powered by solar energy for yearly power and freshwater production for building demand in New Borg El-Arab, Egypt. The system comprises photovoltaic (PV) panels, a water electrolyzer, multi-effect mechanical vapor compression desalination (MED-MVC), and fuel cells with hydrogen storage. A complete mathematical system model is built and solved by MATLAB/Simulink to study the daily, monthly, and yearly performance and sizing of the different system components based on the location climate conditions. The results indicate that PV panels of an area of 1824 m2, in conjunction with an electrolyzer, 863 fuel cells, and a storage tank capacity of 3013.4 m3can satisfy the annual building requirements of 255.17 MWh electricity, 766.5 m3 hot water and 876 m3 cold water. The maximum hydrogen storage lies between September and October, with maximum consumption between February and March. The average annual efficiency of PV, electrolyzer, fuel cell, electrolyzer output, fuel cell output, and overall hybrid system is 20.7%, 68.2%, 34.6%, 13.4%, 4.4%, and 16.5%, respectively, and the performance ratio (PR) of MED-MVC is 2.61. The system proves its capability to supply the building with electrical and water requirements with a levelized cost of energy (LCOE) equal to 0.712 $/kWh. Additionally, This system shows the potential to include other activities in remote areas or islands besides its contribution to achieving SDGs 6, 7, and 13.

Performance modeling and cost optimization of a solar desalination system using forward osmosis with energy storage

This study presents the design and performance evaluation of a solar-driven water desalination system based on forward osmosis (FO) with thermally responsive ionic liquids (ILs). FO is a two-step process involving dilution of the ILs with water, followed by heating above a critical temperature to induce phase separation of liquid water from the IL. This regeneration step can be achieved by integrating FO with low-grade solar energy, which is abundant in regions that face severe water scarcity. A system design is presented that couples an FO membrane module with a compound parabolic concentrator solar collector and thermal energy storage to minimize solar intermittency and produce clean water at a distributed scale of 10 m3/day. To determine the optimal sizing of components in the system, a technoeconomic analysis using the TRNSYS software is performed with the goal of minimizing the levelized cost of water (LCOW). Notably, over 96 % of the energy required for regeneration comes from solar energy and/or the thermal energy storage (TES) in all cases, with auxiliary heat from electricity being used to maintain a continuous process. To evaluate the potential of solar-FO in three different locations within the United States, a case study is presented for Phoenix, AZ; San Diego, CA; and Atlanta, GA. The simulation results reveal that for a small-scale desalination system, LCOW values as low as $1.31/m3 can be attained, with the potential to approach $1/m3 by lowering the costs of the solar collector and FO module based on a sensitivity analysis.

Switched capacitor and coupled inductor based high power factor charger for E-2/E-3 wheelers

This paper introduces a modified Zeta converter topology designed specifically for charging low voltage electric vehicles (LVEVs), enhancing performance for electric two (E-2) and electric three (E-3) wheelers. Unlike traditional Zeta converter, which offers buck-boost gain, an AC-DC converter in this study, utilizes a switched capacitor configuration to efficiently step-down voltage in a single phase, bridging the gap between the AC grid and low potential battery assemblies. This design incorporates a coupled inductor structure and operates in discontinuous inductor current mode (DICM), reducing the size of magnetic components and overall dimensions of converter and power factor correction (PFC) rectifier. DICM operation provides intrinsic PFC and minimizes switching losses, ensuring exceptional grid-side performance with unity power factor and minimal input current harmonic distortions, while maintaining an optimal battery charging profile. The meticulously developed hardware prototype, rated at 450 W, features precise component selection and carefully designed modes of operation. Performance analysis reveals a converter peak efficiency of 94.7 %, total harmonics distortion (THD) in input current at 2.7 % under rated conditions, and near unity power factor with zero phase displacement between input voltage and current.

Investigation of remeshing parameters for deviation analysis in reverse engineering

The use of additive manufacturing technologies in industry is increasingly common, particularly with the emergence of Industry 4.0. These technologies can produce parts quickly and efficiently, but they also place higher demands on the quality of the manufactured products. The layer-by-layer processes create an anisotropic material model, which complicates component sizing. While the topic has been extensively researched, surface anisotropy has received less attention. The surface quality of a product may be affected by various factors, including the file conversion process or the staircase effect generated by the technology. Manufacturing parameters, such as layer thickness and orientation, can also have an impact. This paper focuses on the impact of reverse engineering step adjustment on surface quality.

Investigating the influence of erratic grid on stationary battery energy storage technologies in hybrid power systems: Techno-environ-economic perspectives

Several obstacles impede renewable energy penetration into the global energy sector. Amongst the apparent challenges which require credible research attention, is the selection of appropriate energy storage technologies in the context of intermittent renewable resources and frequent grid outages. Given these, the present study investigated the technical, economic, and environmental effects of an erratic national grid on four distinct battery technologies in hybrid wind, solar, and diesel energy systems. The study was conducted at Baze University Abuja, Nigeria using the Hybrid Optimization Model for Electric Renewables software. The battery types considered are vanadium redox flow battery (VRB), lead-acid battery, nickel-iron battery, and lithium-ion battery (LIB). The VRB-based hybrid energy systems demonstrated superior performances in meeting the electricity demands of the university at the lowest net present cost, levelized cost of energy, and carbon dioxide emissions of $6,328,003.00, $0.0722/kWh, and 21,754 kg/year respectively. This battery technology is characterized by storage depletion, throughput, and losses of 796 kWh/year, 406,570 kWh/year, and 182,757 kWh/year respectively. The sensitivity analysis showed that the frequency and duration of main grid outages affect the optimal systems’ economics, component sizes, battery energy losses, battery energy storage depletion, and renewable energy penetration.

Total Hip Arthroplasty Combined with Proximal Femoral Reconstruction Effectively Treats Severe Hip Deformities: A Novel Osteotomy Technique.

Total hip arthroplasty (THA) combined with proximal femoral reconstruction is a novel osteotomy technique developed to address severe hip deformities. There is a paucity of robust clinical and radiological evidence regarding the outcomes of this novel osteotomy technique. This study aimed to evaluate the clinical and radiological outcomes of THA combined with proximal femoral reconstruction during the early follow-up. This is a retrospective case series of 63 hips who underwent THA combined with proximal femoral reconstruction at a single institution between January 2020 and July 2023. The mean age of patients was 39.6 ± 12.6 years. The mean follow-up was 25.6 ± 3.8 months. Surgical characteristics and perioperative variables were evaluated to assess the efficacy of this technique. Harris hip score (HHS) was utilized to evaluate hip function. Leg length discrepancy (LLD) was evaluated in X-ray. The incidence of major adverse events including deep vein thrombosis (DVT), osteolysis, nonunion of the osteotomy, intraoperative femoral fracture, and infection was also evaluated. Paired-samples t-test was used to compare preoperative and postoperative HHS and LLD. The mean operative time was 125.1 min. The mean size of the acetabular components used was 45.2 mm, and the stem size was 7.5. The primary friction interface was ceramic-on-ceramic, accounting for 92.1% of cases. The average length of hospital stay was 8.5 days. The mean cost of treatment was 46,296.0 Yuan. There was a significant improvement in postoperative HHS (p < 0.001) and LLD (p < 0.001) compared to preoperative values. The incidence of deep venous thrombosis was 4.8%; osteolysis rates for the cup and stem were 4.8% and 6.4%, respectively. The non-union and dislocation rates were 1.6% and 3.2%, respectively. There was no incidence of postoperative infection. The novel osteotomy surgical procedure yields reliable and impressive clinical and radiological outcomes, with minimal complications. We advocate for its use in complex primary THA cases involving severe proximal femoral deformities.

REEP3 is a potential diagnostic and prognostic biomarker correlated with immune infiltration in pancreatic cancer

Receptor Expression-Enhancing Protein 3 (REEP3) serves as a pivotal enzyme crucial for endoplasmic reticulum (ER) clearance during mitosis and is implicated in the advancement of diverse malignancies. Nonetheless, the biological role and mechanisms of REEP3 in pancreatic cancer patients, along with its interplay with immune infiltration, remain inadequately elucidated. In this study, we initially analyzed the differential expression of REEP3 between pancreatic cancer tissues and normal pancreas tissues using the Cancer Genome Atlas (TCGA), GTEx and Gene Expression Omnibus (GEO) databases. Subsequently, we utilized Kaplan–Meier analysis, Cox regression and ROC curve to determine the predictive value of REEP3 for the clinical outcomes of pancreatic cancer patients. Functional enrichment analyses, including Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Gene Set Enrichment Analysis (GSEA), were conducted to explore the potential signaling pathways and biological functions associated with pancreatic cancer. Furthermore, we investigated the PPI network, miRNA, RBP and transcription factor interactions of REEP3 using databases such as GeneMania, STRING, StarBase, KnockTK, ENCODE, Jaspar and hTFtarget. Lastly, the “ssGSEA” algorithm and TIMER database were employed to investigate the correlation between REEP3 expression and immune infiltration as well as immune checkpoints. The expression of REEP3 in pancreatic cancer showed a significantly higher level compared to that in normal tissues. ROC curve analysis indicated that REEP3 holds substantial diagnostic potential for pancreatic cancer patients. Elevated REEP3 expression correlated with unfavorable outcomes in terms of both overall survival and relapse-free survival, establishing it as a notable adverse prognostic marker in pancreatic cancer. Moreover, both univariate and multivariate Cox regression analyses demonstrated that REEP3 maintained an independent association with overall survival. Functional enrichment analyses revealed pathways significantly linked to REEP3, including cytoplasmic translation, wound healing, viral processes, regulation of cellular component size and actin filament organization. Additionally, REEP3 expression displayed a significant positive correlation with CD8+ T cells, B cells, natural killer cells, dendritic cells and macrophages. REEP3 is a potential diagnostic, prognostic marker and immunotherapeutic target for pancreatic cancer.

Collapse modes and mechanisms of planar multi-story large-span steel frames under fire

This paper investigates the collapse modes and mechanisms for planar multi-story large-span steel frames exposed to fire. Based on a systematic parametric analysis conducted with a wide range of parameters, including structural topological forms, fire scenarios, component sizes, load cases, fire protection levels, section temperature gradients, and wind loads. Eight potential collapse modes are identified: general inward collapse, rebalancing collapse, partial lateral collapse, heated large-span beam-induced inward collapse, restraint failure-induced collapse, overall downward collapse, unheated large-span beam-induced inward collapse, and overall lateral collapse. Simultaneously, by combining typical fire cases, corresponding structural collapse mechanisms are explored to reveal the differences in the failure sequence of locally heated components and the final structural collapse extent for different collapse modes. Furthermore, this study also analyzes the evolution laws of key horizontal and vertical displacements and velocities within the fire area for various modes, highlighting distinctions resulting from diverse collapse mechanisms. Finally, a detailed summary of various collapse modes is provided, involving reasons for inducing structural local collapse, the final extent of progressive collapse, possible areas of occurrence, and potential mode transitions as parameters change.

An Analysis of Hybrid Renewable Energy-Based Hydrogen Production and Power Supply for Off-Grid Systems

Utilizing renewable energy sources to produce hydrogen is essential for promoting cleaner production and improving power utilization, especially considering the growing use of fossil fuels and their impact on the environment. Selecting the most efficient method for distributing power and capacity is a critical issue when developing hybrid systems from scratch. The main objective of this study is to determine how a backup system affects the performance of a microgrid system. The study focuses on power and hydrogen production using renewable energy resources, particularly solar and wind. Based on photovoltaics (PVs), wind turbines (WTs), and their combinations, including battery storage systems (BSSs) and hydrogen technologies, two renewable energy systems were examined. The proposed location for this study is the northwestern coast of Saudi Arabia (KSA). To simulate the optimal size of system components and determine their cost-effective configuration, the study utilized the Hybrid Optimization Model for Multiple Energy Resources (HOMER) software (Version 3.16.2). The results showed that, when considering the minimum cost of energy (COE), the integration of WTs, PVs, a battery bank, an electrolyzer, and a hydrogen tank brought the cost of energy to almost 0.60 USD/kWh in the system A. However, without a battery bank, the COE increased to 0.72 USD/kWh in the same location because of the capital cost of system components. In addition, the results showed that the operational life of the fuel cell decreased significantly in system B due to the high hours of operation, which will add additional costs. These results imply that long-term energy storage in off-grid energy systems can be economically benefited by using hydrogen with a backup system.

A sensitivity-based estimation method for investigating control co-design relevance

Abstract. Control co-design is a promising approach for wind turbine design due to the importance of the controller in power production, stability, load alleviation, and the resulting coupled effects on the sizing of the turbine components. However, the high computational effort required to solve optimization problems with added control design variables is a major obstacle to quantifying the benefit of this approach. In this work, we propose a methodology to identify if a design problem can benefit from control co-design. The estimation method, based on post-optimum sensitivity analysis, quantifies how the optimal objective value varies with a change in control tuning. The performance of the method is evaluated on a tower design optimization problem, where fatigue load constraints are a major driver, and using a linear quadratic regulator targeting fatigue load alleviation. We use the gradient-based multi-disciplinary optimization framework Cp-max. Fatigue damage is evaluated with time-domain simulations corresponding to the certification standards. The estimation method applied to the optimal tower mass and optimal cost of energy show good agreement with the results of the control co-design optimization while using only a fraction of the computational effort. Our results additionally show that there may be little benefit to using control co-design in the presence of an active frequency constraint. However, for a soft–soft tower configuration where the resonance can be avoided with active control, using control co-design results in a taller tower with reduced mass.

Design of hydrogen production systems powered by solar and wind energy: An insight into the optimal size ratios

Green hydrogen is expected to play a crucial role in the future energy landscape, particularly in the pursuit of deep decarbonisation strategies within hard-to-abate sectors, such as the chemical and steel industries and heavy-duty transport. However, competitive production costs are vital to unlock the full potential of green hydrogen. In the case of green hydrogen produced via water electrolysis powered by fluctuating renewable energy sources, the design of the plant plays a pivotal role in achieving market-competitive production costs. The present work investigates the optimal design of power-to-hydrogen systems powered by renewable sources (solar and wind energy). A detailed model of a power-to-hydrogen system is developed: an energy simulation framework, coupled with an economic assessment, provides the hydrogen production cost as a function of the component sizes. By spanning a wide range of size ratios, namely the ratio between the size of the renewable generator and the size of the electrolyser, the cost-optimal design point (minimum hydrogen production cost) is identified. This investigation is carried out for three plant configurations: solar-only, wind-only and hybrid. The objective is to extend beyond the analysis of a specific case study and provide broadly applicable considerations for the optimal design of green hydrogen production systems. In particular, the rationale behind the cost-optimal size ratio is unveiled and discussed through energy (utilisation factors) and economic (hydrogen production cost) indicators. A sensitivity analysis on investment costs for the power-to-hydrogen technologies is also conducted to explore various technological learning paths from today to 2050. The optimal size ratio is found to be a trade-off between the utilisation factors of the electrolyser and the renewable generator, which exhibit opposite trends. Moreover, the costs of the power-to-hydrogen technologies are a key factor in determining the optimal size ratio: depending on these costs, the optimal solution tends to improve one of the two utilization factors at the expense of the other. Finally, the optimal size ratio is foreseen to decrease in the upcoming years, primarily due to the reduction in the investment cost of the electrolyser.