Search Results (1,458)

This study assessed photovoltaic (PV) soiling losses at Alamogordo, New Mexico, USA, located within the Chihuahuan Desert and near the White Sands gypsum dune field, a region with frequent dust events. Soiling material collected from PV module surfaces showed seasonal variations in mineral composition, with quartz being the main component during the fall season and calcite predominating during the spring. All samples collected during the following spring season contained large amounts of gypsum, indicating transport from White Sands, supported by HYSPLIT back-trajectories and surface wind data. Soiling materials collected from PV module surfaces generally had a mineral composition similar to that of the surrounding local soils. The mean particle size of collected soiling material samples ranged from 8 to 21 µm, with ~90% of particles being dust (<50 µm) and ~10% of the soiling particles being sand (>50 µm). Despite Alamogordo experiencing 22 dust events during this study, soiling-related power losses were relatively low, about 2% to 3%, much lower than reported for Global Dust Belt locations. The prevailing south-to-southwest winds and their gusts acted as a passive cleaning mechanism, as they were aligned with the front of the PV modules and likely resuspended particles off panel surfaces. Additionally, relatively low rainfall (about 2.2 mm per hour) was effective in restoring PV performance. These findings suggest that, due to the relatively low soiling losses observed, frequent cleaning may not be necessary at this location, resulting in potential savings in maintenance costs over the long-term operation of the PV system. Full article

Photovoltaic (PV) power stations are pivotal for the renewable energy transition, yet their operational efficiency is often compromised by defects such as surface dust accumulation and cracks. Traditional manual inspections are labor-intensive and subjective, while conventional monitoring methods struggle with environmental interference. This study proposes YOLO-MSG, a lightweight framework specifically designed for the automated detection of PV module defects during system operation, including normal panels as well as defective conditions such as dusty and cracked panels. The methodology integrates a Multi-Scale Grouped Convolution (MSGC) module for enhanced feature extraction and a Group-Stem Decoupled Head (GSD-Head) to reduce parameter redundancy. Furthermore, a joint optimization strategy involving LAMP and logits-based knowledge distillation is employed to facilitate edge deployment. Experimental results on a specialized PV defect dataset demonstrate that YOLO-MSG achieves a superior balance between detection accuracy and computational cost. Compared to state-of-the-art models like YOLO11 and YOLOv12, YOLO-MSG significantly reduces GFLOPs and parameter count while maintaining highly competitive mean Average Precision (mAP), with improvements of 1.35% in mAP and 2.37% in mAP50-95 over the baseline models. Specifically, the model achieves an average inference speed of 90.30 FPS on the NVIDIA Jetson AGX platform. These findings confirm the algorithm’s industrial viability, providing a robust and efficient solution for the real-time automated maintenance of photovoltaic infrastructures. Full article

Surface anomalies such as dust accumulation and bird droppings on photovoltaic (PV) panels can significantly reduce their energy production and lead to inefficient maintenance decisions. This paper proposes a vision-based deep learning framework for the automatic detection of PV panel surface conditions and validates the detected anomalies using real inverter-level energy production data. Unlike conventional studies focusing solely on detection performance, the proposed approach introduces a unified and physically interpretable framework that directly links image-based anomaly detection with inverter-level energy performance and decision-oriented PV maintenance. An EfficientNetB3-based model is trained using a two-stage transfer learning strategy on a publicly available Kaggle dataset and evaluated using standard classification metrics. The trained model is then deployed and validated at a 1 MW solar power plant located at Karaman, Türkiye. Classification results obtained from field images are systematically linked with inverter-associated hourly energy production measurements. Following panel cleaning and natural rainfall, an approximately 12.5% increase in inverter-level hourly energy production is observed for the analyzed PV group (120 panels, ~270 Wp), corresponding to an increase from 23.2 to 26.1 kWh. In addition, the study introduces an energy–water–sustainability-aware cleaning decision framework tailored for arid and semi-arid regions where water scarcity and deep groundwater extraction present critical constraints. The framework defines a quantitative decision rule in which panel cleaning is performed only when the expected recoverable energy exceeds the energy cost of water extraction and cleaning. Overall, the proposed approach enables accurate surface anomaly detection while supporting sustainability-aware, resource-efficient and data-driven maintenance decisions for PV power plant operation. Full article

Kazakhstan, the largest landlocked country and the ninth-largest country by land area in the world, played a central role in the Soviet Virgin Lands campaign. However, decades of cultivation left the soil degraded and vulnerable to erosion. This legacy, along with worldwide water scarcity, drives the search for alternative farming methods such as hydroponics. This study investigates the feasibility of powering an indoor hydroponic system with photovoltaic (PV) technology in different regions of Kazakhstan. Three PV configurations, 16, 20, and 24 panels, were simulated in PVsyst (8.0.12) to meet the monthly energy demand of the system. The goal was to determine the minimum PV size and storage capacity for continuous year-round operation. Results showed that 16 panels were sufficient only from April to July, whereas 20- and 24-panel systems provided better reliability throughout the year. Optimal designs varied by region. For instance, those in the south, such as Turkistan, required smaller setups (6.8 kWp, 26 panels, 7 batteries), whereas those in the north, such as Akmola, needed larger ones (10.9 kWp, 42 panels, 10 batteries). Performance ratios ranged from 41% to 66% depending on the region. These results indicate that PV-powered hydroponic systems are feasible in Kazakhstan, although system configurations must be adapted to specific regional solar conditions. Full article

Photovoltaic (PV) systems are important for sustainable energy infrastructure, but their rapid deployment introduces complex fire dynamics that current regulations fail to address adequately. While existing standards focus on the electrical safety of individual components, they often neglect the risks arising from the interaction between the PV array and the building envelope. This review synthesizes current research on ignition mechanisms, thermal behavior, and the aerodynamic propagation of smoke to evaluate these overlooked hazards. A primary finding is that the interstitial space between the panel and the roof functions as a “heat trap,” significantly altering airflow patterns and accelerating flame spread even across fire-rated materials. The analysis further highlights that standard testing protocols do not sufficiently account for the urban dispersion of toxic combustion byproducts, such as hydrogen fluoride and volatile organic compounds. By evaluating recent advancements in Computational Fluid Dynamics (CFD) and helium-based surrogate testing, this paper demonstrates that accurate prediction of pollutant transport requires coupled modeling of wind effects and thermal buoyancy. The study concludes that ensuring urban fire resilience demands an evolution from component certification to integrated system assessments that include installation geometry, ventilation strategies, and environmental impact. Full article

Building-integrated photovoltaic systems are essential components of smart buildings and sustainable urban infrastructure, contributing to energy efficiency and carbon footprint reduction in smart cities. Mismatch loss, particularly under partial shading, is one of the concerns in photovoltaic (PV) systems, especially in urban environments where buildings, trees, and other structures create complex shading patterns. It leads to significant power loss and poor efficiency. Several methods, such as string converters, multi-string converters, central converters, and micro-inverters/power optimizers, have been widely employed to address this issue. These methods suffer from hardware complexity and are good in certain shading patterns only; they remain ineffective otherwise. Power optimizers lead in efficiency under all the shading patterns, whereas string converters lead in hardware simplicity. We propose a novel parallel-series converter to mitigate mismatch losses in smart building applications that is as efficient as power optimizers and as simple as converters. In the proposed parallel-series converter design, multiple PV modules are connected in parallel to a very simple converter, and many such converters are then connected in series to get the final output. The proposed converter is rigorously evaluated for various shading patterns using MATLAB/SIMULINK. A prototype system of $3\times 2$ PV panels is also developed for hardware evaluation. The simulation and hardware results show that the proposed parallel-series converter dominantly competes with power optimizers with much simpler hardware and outperforms the other converters, making it particularly suitable for smart building energy systems where cost-effectiveness and reliability are critical. Full article

This work uses battery-coupled power electronic converter systems and distributed backstepping controllers to improve the reliability of electrical distribution networks. The motivation is to prevent blackouts such as the 28 April 2025 outage in Spain, Portugal, and the south of France. It is now accepted that a rapid rise in solar power injections caused AC overvoltage above grid code limits, triggering photovoltaic (PV) park disconnections as overvoltage self-protection. This case study considers near-Zero-Energy Buildings (nZEBs) connected to the Madeira Island isolated microgrid, where PV power installation is increasing excessively. The main university facility will be upgraded as an nZEB, using roughly 3000 m² of unshaded rooftops plus coverable parking areas to install PV panels. Optimizing the profits/energy cost ratio, a PV power system of around 560 kW can be planned, and the Battery Storage System (BSS) energy capacity can be estimated. The BSS is connected to the university nZEB via backstepping-controlled multilevel converters to manage PV and BSS, enabling the building to contribute to voltage and frequency regulation. Distributed multilevel converters inject renewable energy into the medium-voltage network, regulating active and reactive power to prevent overvoltages shutting down the PV inverters. This removes sustained overvoltage and maximizes PV penetration while augmenting AC grid reliability and resilience. When there is excess solar power and reactive power is insufficient to reduce voltage, controllers slightly curtail PV active power to eliminate overvoltage, maintaining operation with minimal revenue loss while preventing long interruptions, thereby improving grid reliability and power quality. Full article

Due to the high energy demand of buildings, especially educational buildings, it is crucial to improve total building energy consumption. The proposed methodology is the integration of a photovoltaic (PV) system with a smart control plan for educational buildings. The main aim is to improve energy consumption in an educational building (Electrical Engineering Department, Assiut University, Egypt) using photovoltaic integration and a smart control plan to regulate energy and boost indoor comfort without requiring a significant change in the building architecture. This study was conducted in two main phases: field measurements for annual energy consumption in Assiut University over a five-year period from 2009 to 2014, and an analysis of energy consumption for the Electrical Engineering Department. Then, integration of PV panels on the roof to generate electricity was considered, with the calculation of the shading factor and tilt angle to ensure a realistic estimation of energy yield and to improve energy efficiency using smart control plans. The findings indicate that the average annual peak consumption reached about 30 GWh in Assiut University during the academic years 2009 to 2014. The maximum energy consumption for a typical occupied day in the educational building is 47 kWh. An improvement in building energy consumption was achieved using PV, producing 33–35 MWh annually with an effective smart control plan and without installing sensor-based systems. The results of this study will help improve energy consumption for educational buildings in hot arid climates without building modifications. This study highlights that unoccupied periods—when human activity is absent in classrooms and other rooms—account for up to 40% of the scheduled energy consumption. Using PV panels will result in a shading factor of 0.562 from the total roof area. Full article

This study evaluates the rooftop solar photovoltaic (PV) potential at the building level in the tourism-rich districts of Northern Khyber Pakhtunkhwa (KPK), Pakistan, using advanced geospatial analysis to support renewable energy planning. By combining the Area Solar Radiation tool with detailed building footprint data, the study identified solar energy potential and prioritized areas for PV system installations. Results show that approximately 35% of the 1.29 million buildings analyzed are suitable for solar panels, with energy generation capacity varying by building size and district. Spatial analysis further highlighted Union Councils (UCs) where over 50% of buildings are solar-suitable, enabling precise targeting of renewable energy initiatives. The study underscores the importance of integrating local geographical and socio-economic data to enhance the feasibility and scalability of solar energy solutions in rural and urban settings and can be used to guide policy prioritization and funding decisions. This research demonstrates how geospatial analysis and open data can drive localized clean energy adoption, directly contributing to Sustainable Development Goal 7 by advancing affordable and sustainable energy solutions. Full article

This study analyzes the feasibility of increasing the energy and economic efficiency of a residential heating and domestic hot water (DHW) preparation system with a solar-assisted air-to-water heat pump (AWHP), implemented in southeast Romania. The following options are evaluated from the sustainability point of view (energy, economic and CO₂ emissions): renovation of the building and modernization of the system by integrating an electric accumulator, increasing the capacity of photovoltaic panels (PV) and solar thermal collectors (STCs), and the option of replacing the AWHP with a ground-source heat pump (GSHP) with a vertical loop (GSHP-VL) and a GSHP with a horizontal loop (GSHP-HL). The energy performance of heating systems was simulated using GeoT*SOL software. The results show that by renovating a home, the energy requirement for heating decreased by about 58%; therefore, following the current financial rules applied to prosumers, the GSHP-VL system has the best energy performance (electricity consumption and solar coverage rate of this consumption), economic performance (investment recovery period and annual operating cost) and environmental performance (lowest CO₂ emissions) and that through a government program that promotes energy efficiency and the use of renewable energy sources in homes, capital costs can be reduced by (43–57)% in the case of systems with HP, PV and electric storage. This study shows that a 5 kW PV system combined with 5 kWh battery cannot cover the full heat demand of a medium-to-large house during the winter, and for full energy independence, a larger PV array paired with a higher-capacity battery is necessary. Generous government subsidies amounting to 50% can reduce the payback period for such investments from (11.26–14.68) years to (5.86–7.26) years. Full article

As zero-energy buildings (ZEBs) become increasingly mandatory, photovoltaic (PV) systems play a key role in increasing on-site energy generation. For staged ZEB certification based on the energy self-sufficiency ratio (ESSR), it is essential to determine the required power generation and to design PV systems with appropriate installation area and location. This study proposes a systematic design decision-support process for PV system planning that links required energy generation to panel installation strategies. The process enables the determination of a feasible installation area and location of PV panels and was implemented as a design-support program. The proposed process was applied to an apartment building under construction with a ZEB certification grade 5. Compared to the existing design, the optimal design reduced the required PV system capacity by 1.7% while increasing the predicted power generation by approximately 2.8%. The reported improvement in energy generation represents a relative comparison between design alternatives evaluated under identical modeling assumptions and therefore remains valid for comparative design decision-making. Field measurements conducted at a residential building with installed PV systems showed that the predicted power generation is consistent with measured trends, supporting comparative design evaluation and feasibility screening in early-stage PV planning. The developed design process provides a practical framework for early-stage PV system planning, supporting informed design decisions to meet target energy self-sufficiency requirements in ZEBs. Full article

In the context of increasing energy demand and the global transition toward sustainable solutions, the integration of renewable energy sources into power systems is becoming a necessity. Data centers, as major energy consumers, are particularly impacted by this shift. Photovoltaic (PV) panels represent a promising alternative to conventional electricity sources due to their low environmental impact. However, their intermittent nature leads to instability in power supply, requiring efficient energy storage solutions to ensure reliability and self-sufficiency. Among the various storage technologies available, hydrogen stands out as a viable energy carrier due to its high energy density, long-term storage capability, and minimal environmental footprint. To address these challenges, a hybrid energy storage system combining hydrogen production, battery storage, and grid connection is designed in this study to enhance energy autonomy while maintaining cost efficiency. The system relies on a combination of an electrolyzer, hydrogen storage tanks, a fuel cell, and a battery to ensure a continuous and stable energy supply. A simulation-based optimization approach is conducted using Python to determine the optimal configuration of these components. The results show that a self-sufficiency rate of 95% is achieved, with a levelized cost of electricity (LCOE) of 0.47 US$/kWh, demonstrating the feasibility of the proposed system. The environmental impact is also assessed, revealing a significant reduction in carbon emissions, with 8.97 tons of CO₂ saved over the system’s 15-year lifespan, compared to the 10 tons emitted by a conventional grid-powered system over the same period. Furthermore, a detailed analysis of energy flow within the system highlights the role of each storage component in balancing supply and demand. The hybrid design leverages the advantages of both hydrogen and battery storage, where the battery is primarily used to compensate for short-term fluctuations, while hydrogen ensures long-term energy storage. The impact of different electrolyzer and fuel cell sizes on system performance is also evaluated, leading to an optimal configuration with an electrolyzer of 5 kW, a hydrogen storage capacity of 200 L at 350 bars, a fuel cell of 2 kW, and a battery of 50 kWh. Full article

Remote off-grid microgrids are often locked into diesel-backed operation because renewable variability creates multi-day and seasonal energy gaps that short-duration batteries cannot economically bridge. This work examines how renewable hydrogen can complement batteries and dispatchable biomass to push an existing hybrid microgrid toward near-autonomous, low-carbon operation, while remaining robust under future electrification demands. The analysis is based on real operational load insights from a remote off-grid system, combined with techno-economic optimization in HOMER Pro. The examined architecture includes PV panels, battery energy storage, a biomass CHP unit, and a diesel generator as backup; the hydrogen pathway additionally incorporates an electrolysis, storage and a PEMFC. Three scenarios are considered: a baseline PV/BAT configuration, an intermediate PV/BAT/BIO configuration that strengthens dispatchable renewable supply and short-term flexibility, and a PV/BAT/BIO/H₂ configuration targeting an increase in renewable energy penetration (REP). Results show that hydrogen integration shifts the system from curtailment-limited, diesel-supported operation to storage-enabled operation: surplus renewable production that would otherwise be curtailed is converted into hydrogen and later dispatched during prolonged deficits, enabling deep diesel displacement without compromising reliability. Hydrogen-enabled configurations achieve 90–99% REP, reduced diesel consumption, and lower CO₂ emissions, primarily by converting curtailed surplus into storable hydrogen. A rule-based EMS highlights technology complementarity across timescales, with batteries providing diurnal balancing and hydrogen covering longer deficits, which also reduces battery cycling stress. Overall, the study clarifies key design trade-offs, especially the need for coordinated PV expansion and storage sizing, and illustrates how a multi-storage portfolio can support high renewable penetration in such systems. Full article

Building-Integrated and Building-Attached Photovoltaic (BIPV/BAPV) systems are increasingly being adopted in metropolitan areas worldwide, driven by international commitments to reduce greenhouse gas emissions and the declining cost of PV technology. A promising application involves the vertical integration of PV panels into building facades, which offers architectural and energy benefits, particularly in urban environments with limited roof space. This study experimentally evaluates the energy behavior of 12 vertically mounted 5 W PV panels (model SP005P) installed on university buildings in Ibarra, Ecuador, across four azimuth orientations (−135° SE, −45° NE, 45° NW, 135° SW). A continuous 8-month monitoring campaign was conducted using a custom-designed Arduino-based data logger, validated with multimeter measurements (error < 5%). The dataset was used to develop MATLAB version 2025b forecasting models based on Sum-of-Sine functions, achieving R² values between 0.83 and 0.98 and RMSE values between 0.024 and 0.082 W. The 45° (NW) orientation achieved the highest annual energy yield of 48% STC, reaching up to ≈440 kWh/kWp in the best-performing facade, while 135° (SW) also exhibited favorable performance compared with the northeast and southeast orientations. These findings provide significant evidence for facade-integrated PV design in equatorial latitudes, offering performance benchmarks and validated forecasting tools that can support architectural planning, BIPV feasibility analysis, and urban solar-energy strategies in regions with similar conditions. Full article

Residential photovoltaic/thermal (PV/T) systems can reduce electricity consumption by supplying both electricity and heat; however, their performance depends on household composition and lifestyle-driven demand profiles. This study simulates a PV/T system for a detached house in Tokyo while accounting for occupant-behavior variability using Japanese time-use statistics from 2015 and 2020, which capture the pandemic-related increase in time spent at home in 2020. Both a PV/T system and a conventional PV system were evaluated for four representative household scenarios, reflecting changes in domestic hot water (DHW), space conditioning, and appliance electricity demand. In the 2020 dataset, the large-household case (Case C) showed the largest improvement in net electricity balance relative to the PV system, with an improvement of 1.8 GJ, while the elderly-couple case (Case D) achieved the highest overall thermal efficiency, with a DHW COP of 6.26 and a space-heating COP of 5.75. In the young-couple case (Case A), the CO₂ reduction increased from 169 kg in the 2015 dataset to 239 kg in the 2020 dataset, showing that lifestyle changes affected the energy-saving benefit. These findings indicate that lifestyle-dependent behavioral changes should be considered in PV/T performance assessment and system sizing. Full article