Search Results (3,619)

Photovoltaic (PV) modules undergo comprehensive testing to validate their electrical and thermal properties prior to market entry. These evaluations consist of durability and efficiency tests performed under realistic outdoor conditions with natural climatic influences, as well as in controlled laboratory settings. The overall performance of PV cells is affected by several factors, including solar irradiance, operating temperature, installation site parameters, prevailing weather, and shading effects. In the presented study, three distinct PV modules were analyzed using a sophisticated large-scale steady-state solar simulator. The current–voltage (I-V) characteristics of each module were precisely measured and subsequently scrutinized. To augment the analysis, a three-layer artificial neural network, specifically the multilayer perceptron (MLP), was developed. The experimental measurements, along with the outputs derived from the MLP model, served as the foundation for a comprehensive global sensitivity analysis (GSA). The experimental results revealed variances between the manufacturer’s declared values and those recorded during testing. The first module achieved a maximum power point that exceeded the manufacturer’s specification. Conversely, the second and third modules delivered power values corresponding to only 85–87% and 95–98% of their stated capacities, respectively. The global sensitivity analysis further indicated that while certain parameters, such as efficiency and the ratio of V_oc/V, played a dominant role in influencing the power-voltage relationship, another parameter, U, exhibited a comparatively minor effect. These results highlight the significant potential of integrating machine learning techniques into the performance evaluation and predictive analysis of photovoltaic modules. Full article

Water management is a critical issue for improving both the performance and durability of proton exchange membrane fuel cells (PEMFCs). A gas diffusion layer (GDL), as a porous medium, plays a key role in liquid water removal, reactant supply, and ensuring uniform distribution within the cell. Local perforations in the GDL are known to enhance water management capability. To further improve mass transfer, the effects of the perforation location in the GDL on PEMFC performance were investigated under different flow rates. The performance was compared and analyzed for three cases with GDL on the cathode side: a conventional GDL, a GDL perforated only under the channel, and a GDL with the perforations offset toward the rib by half the channel width. As a result, the offset of the perforations led to improved performance and enhanced uniformity, and the effect of the offset became more significant at higher flow rates. The under-channel and offset cases showed slight performance increases of 3.02% and 3.11% under the cathode stoichiometric ratio (SR_c) of 1.2, but more significant improvements of 4.72% and 5.29% were observed under the SR_c of 3.0. These results suggest the necessity of considering the flow field when designing a perforated GDL. Full article

Borehole trajectory planning under complex geological conditions poses significant challenges for intelligent drilling systems. To tackle this issue, a novel optimization framework is developed, leveraging the Soft Actor-Critic (SAC) algorithm enhanced by a self-attention mechanism. A three-dimensional heterogeneous geological model is constructed via generative adversarial networks (GANs), incorporating key formation features such as lithology, pressure, and fault zones. A tailored multi-objective reward function is introduced, balancing directional convergence, trajectory smoothness, obstacle avoidance, and formation adaptability. The self-attention mechanism is embedded into both the actor and critic networks to strengthen the agent’s capacity for spatial perception and decision stability. The proposed approach enables the agent to adaptively generate control sequences for efficient trajectory planning in highly variable formations. Experimental results demonstrate that the model exhibits superior convergence stability, improved curvature control, and enhanced obstacle avoidance, highlighting its potential for intelligent trajectory planning in challenging drilling environments. Full article

The output of vehicle-integrated photovoltaics (VIPVs) varies due to complex surface interactions, shading, weather conditions, module temperature, and module configuration, making accurate predictions of power generation challenging. This study examines the characteristics of incident light on VIPVs, focusing on installations on automobile roofs and hoods. Surface element data were collected from areas near the target locations (hood and roof), with shading effects taken into account. The calculations evaluated how the angle of incoming light impacts the intensity on specific parts of the vehicle, identifying which surfaces are most likely to receive maximum illumination. For example, the hood exhibited the highest incident light intensity when sunlight approached directly from the front at a solar altitude of 71°, reaching approximately 98% of the light intensity. These calculations enable the assessment of incident light intensity characteristics for various vehicle parts, including the hood and roof. Additionally, by utilizing database information, it is possible to calculate the incident light on vehicle surfaces at any given time and location. Full article

The wind energy sector is experiencing substantial growth, with global wind turbine capacity increasing and projected to expand further in the coming years. However, rain erosion on the leading edges of turbine blades remains a significant challenge, affecting both aerodynamic efficiency and structural longevity. The associated degradation reduces annual energy production and leads to high maintenance costs due to frequent inspections and repairs. To address this issue, researchers have developed numerical models to predict blade erosion caused by water droplet impacts. This study presents a finite element analysis model in Abaqus to simulate the interaction between a single water droplet and wind turbine blade material. The novelty of this model lies in evaluating the influence of several parameters on von Mises and S33 peak stresses in the leading-edge protection, such as friction coefficient, type of contact, impact velocity, and droplet diameter. The findings provide insights into optimising LEP numerical models to simulate rain erosion as closely as possible to real-world scenarios. Full article

Modernizing irrigation systems (ISs) from traditional gravity methods to sprinkler and drip technologies has significantly improved water use efficiency. However, it has simultaneously increased electricity demand and operational costs. Integrating photovoltaic generators into ISs represents a promising solution, as solar energy availability typically aligns with peak irrigation periods. Despite this potential, photovoltaic pumping systems (PVPSs) often face reliability issues due to fluctuations in solar irradiance, resulting in frequent start/stop cycles and premature equipment wear. The IEC 62253 standard establishes procedures for evaluating PVPS performance but primarily addresses steady-state conditions, neglecting transient regimes. As the main contribution, the current paper proposes a non-intrusive, high-resolution monitoring system and a methodology to assess the performance of an isolated, high-power PVPS, considering also transient regimes. The system records critical electrical, hydraulic and environmental parameters every second, enabling in-depth analysis under various weather conditions. Two performance indicators, pumped volume efficiency and equivalent operating time, were used to evaluate the system’s performance. The results indicate that near-optimal performance is only achievable under clear sky conditions. Under the appearance of clouds, control strategies designed to protect the system reduce overall efficiency. The proposed methodology enables detailed performance diagnostics and supports the development of more robust PVPSs. Full article

In the context of urban decarbonization, multi-energy microgrids (MEMGs) are gaining increasing relevance due to their ability to enhance synergies across multiple energy vectors. This study presents a block-based MILP framework developed to optimize the operations of a real MEMG, with a particular focus on accurately modeling the structure of electricity and natural gas bills. The objective is to assess the added economic value of integrating a battery energy storage system (BESS) under the assumption it is employed to provide implicit flexibility—namely, bill management, energy arbitrage, and peak shaving. Results show that under assumed market conditions, tariff schemes, and BESS costs, none of the analyzed BESS configurations achieve a positive net present value. However, a 2 MW/4 MWh BESS yields a 3.8% reduction in annual operating costs compared to the base case without storage, driven by increased self-consumption (+2.8%), reduced thermal energy waste (–6.4%), and a substantial decrease in power-based electricity charges (–77.9%). The performed sensitivity analyses indicate that even with a significantly higher day-ahead market price spread, the BESS is not sufficiently incentivized to perform pure energy arbitrage and that the effectiveness of a time-of-use power-based tariff depends not only on the level of price differentiation but also on the BESS size. Overall, this study provides insights into the role of BESS in MEMGs and highlights the need for electricity bill designs that better reward the provision of implicit flexibility by storage systems. Full article

The reliable and economical operation of modern power systems is increasingly complex due to the integration of diverse energy sources and dynamic load patterns. A critical challenge is maintaining the balance between electricity supply and demand within various operational constraints. This study addresses the economic scheduling of generation units using a Mixed Integer Programming (MIP) optimization model. Key constraints considered include reserve requirements, ramp rate limits, and minimum up/down time. Simulations are performed across multiple scenarios, including systems with spinning reserves, responsive demand, renewable energy integration, and energy storage systems. For each scenario, the optimal mix of generation resources is determined to meet a 24 h load forecast while minimizing operating costs. The results show that incorporating demand responsiveness and renewable resources enhances the economic efficiency, reliability, and flexibility of the power system. Full article

This study aims to determine the model parameters of proton exchange membrane fuel cells (PEMFC) by employing the Sea Horse Optimization (SHO) algorithm, a novel metaheuristic approach inspired by natural behaviors. Although conventional algorithms in the literature have achieved considerable success in parametric modeling accuracy, many of them suffer from inherent drawbacks, such as premature convergence and entrapment in local minima. The SHO algorithm, with its adaptive and dynamic nature, is designed to overcome these limitations. To further evaluate its performance, a detailed parametric sensitivity analysis is conducted on SHO-specific control parameters. In this work, experimental polarization data from a Ballard Mark V PEMFC is used as a reference to estimate the equivalent circuit model parameters ${ϵ}_1$, ${ϵ}_2$, ${ϵ}_3$, ${ϵ}_4$, $\beta$, $\lambda$, $R_c$. The SHO algorithm achieved a mean absolute error (MAE) of 0.001079 and a coefficient of determination (R²) of 0.999791, with a model-to-experiment fit ratio of 99.92%. Compared to similar studies reported in the literature, the results indicate that the SHO algorithm offers competitive performance. Moreover, the average convergence time is recorded as 1.74 s for 5000 iteration, highlighting the algorithm’s rapid convergence and low computational cost. Overall, the SHO algorithm is demonstrated to be an efficient, robust, and promising alternative to conventional methods for parameter identification in PEMFC modeling. Full article

Hydrogen produced from renewable sources has the potential to tackle various energy challenges, from allowing cost-effective transportation of renewable energy from production to consumption regions to decarbonizing intensive energy consumption industries. Due to its application versatility and non-greenhouse gaseous emissions characteristics, it is expected that hydrogen will play an important role in the decarbonization strategies set out for 2050. Currently, there are some barriers and challenges that need to be addressed to fully take advantage of the opportunities associated with hydrogen. The present work aims to characterize the state of the art of different hydrogen production, storage, transport, and distribution technologies, which compose the hydrogen value chain. Based on the information collected it was possible to conclude the following: (i) Electrolysis is the frontrunner to produce green hydrogen at a large scale (efficiency up to 80%) since some of the production technologies under this category have already achieved a commercially available state; (ii) in the storage phase, various technologies may be suitable based on specific conditions and purposes. Technologies of the physical-based type are the ones mostly used in real applications; (iii) transportation and distribution options should be viewed as complementary rather than competitive, as the most suitable option varies based on transportation distance and hydrogen quantity; and (iv) a single value chain configuration cannot be universally applied. Therefore, each case requires a comprehensive analysis of the entire value chain. Methodologies, like life cycle assessment, should be utilized to support the decision-making process. Full article

The growing number of daylighting metrics—often overlapping in scope or terminology—combined with the need for prior familiarization to interpret and apply them effectively, has created a barrier to their adoption beyond academic settings. Consequently, this study analyzes a representative set of established and emerging daylighting metrics to evaluate applicability, synergies, and limitations. Particular attention is given to their implications for occupant health, well-being, performance, and energy use, especially within the context of sensorless smart control systems. A virtual room model was simulated using DaySim 3.1 in two contrasting climates—Seville and London—with varying window-to-wall ratios, orientations, and occupancy schedules. The results show that no single metric provides a comprehensive daylighting assessment, highlighting the need for combined approaches. Daylighting Autonomy (DA) proved useful for task illumination, while Useful Daylight Illuminance (UDI) helped identify areas prone to excessive solar exposure. Spatial metrics such as Spatial Daylight Autonomy (sDA) and Annual Sunlight Exposure (ASE) offer an overview but lack necessary granularity. Circadian Stimulus Autonomy (CSA) appears promising for evaluating circadian entrainment, though its underlying models remain under refinement. Continuous Overcast Daylight Autonomy (DAo.con) shows the potential for sensorless lighting control when adjusted for orientation. A nuanced, multi-metric approach is therefore recommended. Full article

This paper describes the design and implementation of a battery equalizer circuit for small satellites, developed under the New Space philosophy exclusively using commercial off-the-shelf (COTS) components. The primary objective is to ensure high reliability for mission-critical power systems while adhering to strict cost constraints. In order to achieve this objective, the design incorporates a robust analog control circuit, thereby avoiding the complexities and potential single-point failures associated with digital controllers. A comprehensive study of various cell-balancing topologies was conducted, leading to the selection, hardware implementation, and comparative analysis of the two most suitable candidates. The results of this study provide a validated, cost-effective, and reliable battery equalizer solution for developers of small satellites. Full article

Many kinds of panel structures are proposed in aircraft design. This study presents a topology optimization method to improve the buckling resistance of panel structures. It should be noted that a popular configuration of the present panel structure is that with ribs and frames. Stiffening characteristics (i.e., effects of increasing structural stiffness of a panel structure with ribs and frames) are thus included during analysis of panel structures. After studying the coupling relationship between the dynamic characteristics and buckling behavior of the panel, a developed MMC (moving morphable component) method is proposed for topology optimization to improve the buckling resistance of the panel. It is seen that the coupling relationship between the dynamic characteristics and buckling behavior of the panel is mainly reflected when the compression force acts on the panel, corresponding that dynamic characteristics will vary with the load. If the load acts on the structure, the first-order natural frequency of the panel with ribs and frames in this study decreases with the increase in the load, with the optimization objective of maximizing the first-order natural frequency. Based on the coupling relationship between dynamic characteristics and buckling behavior, the critical buckling load of the panel increases as the first-order natural frequency increases. The present optimization method can reduce computational complexity without changing the accuracy of the calculation. At the same time, the coupling relationship between dynamic characteristics and buckling behavior is applied in topology optimization, which is of great significance to improve the comprehensive performance of panel structures in the engineering design process. This paper improves the dynamic characteristics and buckling resistance of panels with ribs and frames based on the improved MMC method. The proposed method effectively meets the design requirements of flight vehicle design in complex environments. Full article

This paper presents a high-fidelity multi-physics dynamic model for electric vehicles, serving as a fundamental building block for intelligent vehicle-to-grid (V2G) integration systems. The model accurately captures complex vehicle dynamics of the powertrain, battery, and regenerative braking, enabling precise energy consumption evaluation, including in AI-driven V2G scenarios. Validated using real-world data from a Citroën Ami operating on urban routes in Naples, Italy, it achieved exceptional accuracy with a root mean square error (RMSE) of 1.28% for dynamic state of charge prediction. This robust framework provides an essential foundation for AI-driven digital twin technologies in V2G applications, significantly advancing sustainable transportation and smart grid integration through predictive simulation. Its versatility supports diverse fleet applications, from residential energy management and coordinated charging optimization to commercial car sharing operations, leveraging backup power during peak demand or grid outages, so to maximize distributed battery storage utilization. Full article

The growing imperative to mitigate climate change and accelerate the shift toward energy sustainability has called for a critical evaluation of heat and electricity generation methods. This article presents a comparative life cycle assessment (LCA) of solar and biogas energy systems on a common basis of 1 kWh of useful energy using SimaPro, the ReCiPe 2016 methodology (both midpoint and endpoint indicators), and cumulative energy demand (CED) analysis. This study is the first to evaluate co-located solar PV, solar thermal compound parabolic concentrator (CPC) and biogas combined heat and power (CHP) systems with in situ data collected under identical climatic and operational conditions. The project costs yield levelized costs of electricity (LCOE) of INR 2.4/kWh for PV, 3.3/kWh for the solar thermal dish and 4.1/kWh for biogas. However, the collaborated findings indicate that neither solar-based systems nor biogas technology uniformly outperform the others; rather, their effectiveness hinges on contextual factors, including resource availability and local policy incentives. These insights will prove critical for policymakers, industry stakeholders, and local communities seeking to develop effective, context-sensitive strategies for sustainable energy deployment, emissions reduction, and robust resource management. Full article