Search Results (10,050)

Enhancing the real-world performance of sustainably designed and certified green buildings remains a significant challenge, particularly in hot climates where efforts to improve thermal comfort often conflict with energy efficiency goals. In the United Arab Emirates (UAE), even newly constructed facilities with green building certifications present opportunities for retrofitting and performance optimization. This study investigates the energy and thermal comfort performance of a LEED Gold-certified, mixed-use university campus in Dubai through a calibrated digital twin developed using IES thermal modelling software. The analysis evaluated existing sustainable design strategies alongside three retrofit energy conservation measures (ECMs): (1) improved building envelope U-values, (2) installation of additional daylight sensors, and (3) optimization of fan coil unit efficiency. Simulation results demonstrated that the three ECMs collectively achieved a total reduction of 15% in annual energy consumption. Thermal comfort was assessed using operative temperature distributions, Predicted Mean Vote (PMV), and Predicted Percentage of Dissatisfaction (PPD) metrics. While fan coil optimization yielded the highest energy savings, it led to less favorable comfort outcomes. In contrast, enhancing envelope U-values maintained indoor conditions consistently within ASHRAE-recommended comfort zones. To further support energy reduction and progress toward Net Zero targets, the study also evaluated the integration of a 228.87 kW rooftop solar photovoltaic (PV) system, which offset 8.09% of the campus’s annual energy demand. By applying data-driven thermal modelling to assess retrofit impacts on both energy performance and occupant comfort in a certified green building, this study addresses a critical gap in the literature and offers a replicable framework for advancing building performance in hot climate regions. Full article

This study investigates the impact of Time-of-Use (TOU) scheduling and battery energy storage systems (BESS) on voltage stability in a typical Malaysian medium-voltage distribution network with high photovoltaic (PV) system penetration. The analyzed network comprises 110 nodes connected via eight feeders to a pair of 132/11 kV, 15 MVA transformers, supplying a total load of 20.006 MVA. Each node is integrated with a 100 kW PV system, enabling up to 100% PV penetration scenarios. A hybrid mitigation strategy combining TOU-based load shifting and BESS was implemented to address voltage violations occurring, particularly during low-load night hours. Dynamic simulations using DIgSILENT PowerFactory were conducted under worst-case (no load and peak load) conditions. The novelty of this research is the use of real rural network data to validate a hybrid BESS–TOU strategy, supported by detailed sensitivity analysis across PV penetration levels. This provides practical voltage stabilization insights not shown in earlier studies. Results show that at 100% PV penetration, TOU or BESS alone are insufficient to fully mitigate voltage drops. However, a hybrid application of 0.4 MWh BESS with 20% TOU load shifting eliminates voltage violations across all nodes, raising the minimum voltage from 0.924 p.u. to 0.951 p.u. while reducing active power losses and grid dependency. A sensitivity analysis further reveals that a 60% PV penetration can be supported reliably using only 0.4 MWh of BESS and 10% TOU. Beyond this, hybrid mitigation becomes essential to maintain stability. The proposed solution demonstrates a scalable approach to enable large-scale PV integration in dense rural grids and addresses the specific operational characteristics of Malaysian networks, which differ from commonly studied IEEE test systems. This work fills a critical research gap by using real local data to propose and validate practical voltage mitigation strategies. Full article

The Union of the Comoros, located in the Indian Ocean, faces persistent energy challenges due to its geographic isolation, heavy dependence on imported fossil fuels, and underdeveloped electricity infrastructure. This study investigates the techno-economic optimization of a hybrid microgrid designed to supply electricity to a rural village in Grande Comore. The proposed system integrates photovoltaic (PV) panels, wind turbines, a diesel generator, and battery storage. Detailed modeling and simulation were conducted using HOMER Energy, accompanied by a sensitivity analysis on solar irradiance, wind speed, and diesel price. The results indicate that the optimal configuration consists solely of PV and battery storage, meeting 100% of the annual electricity demand with a competitive levelized cost of energy (LCOE) of 0.563 USD/kWh and zero greenhouse gas emissions. Solar PV contributes over 99% of the total energy production, while wind and diesel components remain unused under optimal conditions. Furthermore, the system generates a substantial energy surplus of 63.7%, which could be leveraged for community applications such as water pumping, public lighting, or future system expansion. This study highlights the technical viability, economic competitiveness, and environmental sustainability of 100% solar microgrids for non-interconnected island territories. The approach provides a practical and replicable decision-support framework for decentralized energy planning in remote and vulnerable regions. Full article

To sustainably power future urban communities, cities require advanced solar energy planning tools that overcome the limitations of traditional approaches, such as data fragmentation and siloed decision-making. SDTs present a transformative opportunity by enabling precision urban modelling, integrated simulations, and iterative decision support. However, their application in solar energy planning remains underexplored. This study introduces SDT4Solar, a novel SDT-based framework designed to integrate city-scale rooftop solar planning through 3D building semantisation, solar modelling, and a unified geospatial database. By leveraging advanced spatial modelling and Internet of Things (IoT) technologies, SDT4Solar facilitates high-resolution 3D solar potential simulations, improving the accuracy and equity of solar infrastructure deployment. We demonstrate the framework through a proof-of-concept implementation in Ballarat East, Victoria, Australia, structured in four key stages: (a) spatial representation of the urban built environment, (b) integration of multi-source datasets into a unified geospatial database, (c) rooftop solar potential modelling using 3D simulation tools, and (d) dynamic visualization and analysis in a testbed environment. Results highlight SDT4Solar’s effectiveness in enabling data-driven, spatially explicit decision-making for rooftop PV deployment. This work advances the role of SDTs in urban energy transitions, demonstrating their potential to optimise efficiency in solar infrastructure planning. Full article

In this paper, we present a distributed low-carbon demand response method in distribution networks incorporating day-ahead and intraday flexibilities on the demand side. This two-stage demand dispatch scheme, including day-ahead schedule and intraday adjustment, is proposed to facilitate the coordination between power demand and local photovoltaic (PV) generation. We employ the alternating direction method of multipliers (ADMM) to solve the dispatch problem in a distributed manner. Demand response in a 141-bus test system serves as our case study, demonstrating the effectiveness of our approach in shifting power loads to periods of high PV generation. Our results indicate remarkable reductions in the total carbon emission by utilizing more distributed PV generation. Full article

Meta-heuristic algorithms are the dominant techniques for parameter estimating for solar photovoltaic (PV) models. Current algorithms are primarily designed with a focus on search performance and convergence speed, but they fail to account for the significant difference in the lengths of the feasible regions for each decision variable in the solar parameter estimation problem. The consideration of variable length difference in algorithm design may be beneficial to the efficiency for solving this problem. A gray predictive evolutionary algorithm with adaptive threshold adjustment strategy (GPEat) is proposed in this paper to estimate the parameters of several solar photovoltaic models. Unlike original GPEs and their existing variants with fixed thresholds, GPEat designs an adaptive threshold adjustment strategy (ATS), which adaptively adjusts the threshold parameter of GPE to be proportional to the length of each dimensional variable of the PV problem. The adaptive change of the threshold helps GPEat to select suitable operators for different dimensions of the PV problem. Several sets of experiments are conducted based on single-, double-, and triple-diode models and PV panel models. The experimental results indicate the highly competitive in parameter estimation for solar PV models of the proposed algorithm. Full article

Evaluating the renewable energy potential, such as that of solar photovoltaics (PV), is important for developing renewable energy policies. This study investigated rooftop solar PV potential in Thailand based on open-source geographic information system (GIS) building footprints, solar PV power output, and the most recent land use data (2022). GIS-based overlay analysis, buffering, fishnet modeling, and spatial join operations were applied to assess rooftop availability across various building types, taking into account PV module installation parameters and optimal panel orientation. Economic feasibility and sensitivity analyses were conducted using standard economic metrics, including net present value (NPV), internal rate of return (IRR), payback period, and benefit–cost ratio (BCR). The findings showed a total rooftop solar PV power generation potential of 50.32 TWh/year, equivalent to 25.5% of Thailand’s total electricity demand in 2022. The Central region contributed the highest potential (19.59 TWh/year, 38.94%), followed by the Northeastern (10.49 TWh/year, 20.84%), Eastern (8.16 TWh/year, 16.22%), Northern (8.09 TWh/year, 16.09%), and Southern regions (3.99 TWh/year, 7.92%). Both commercial and industrial sectors reflect the financial viability of rooftop PV installations and significantly contribute to the overall energy output. These results demonstrate the importance of incorporating rooftop solar PV in renewable energy policy development in regions with similar data infrastructure, particularly the availability of detailed and standardized land use data for building type classification. Full article

Among the new strategies for managing diseases in agricultural crops is the application of metallic nanoparticles due to their ability to inhibit the development of phytopathogenic microorganisms and to induce plant defense responses. Therefore, this research evaluated the effects of silver (AgNPs), zinc oxide (ZnONPs), and silicon dioxide (SiO₂NPs) nanoparticles on symptom progression and physiological parameters in two pathosystems: Pseudomonas syringae pv. tomato (Psto) in tomato (pathosystem one, culturable pathogen) and Candidatus Liberibacter solanacearum (CaLso) in pepper plants (pathosystem two, non-culturable pathogen). For in vitro pathosystem one assays, SiO₂NPs did not inhibit Psto growth. The minimum inhibitory concentration (MIC) was 31.67 ppm for AgNPs and 194.3 ppm for ZnONPs. Furthermore, the minimum lethal concentration (MLC) for AgNPs was 100 ppm, while for ZnONPs, it was 1000 ppm. For in planta assays, ZnONPs, AgNPs, and SiO₂NPs reduced the number of lesions per leaf, but only ZnONPs significantly decreased the severity. Regarding pathosystem two, AgNPs, ZnONPs, and SiO₂NPs application delayed symptom progression. However, only AgNPs significantly reduced severity percentage. Moreover, treatments with AgNPs and SiO₂NPs increased the plant height and dry weight compared to the results for the control. Full article

The increasing penetration of photovoltaic solar energy has intensified the need for accurate production forecasting to ensure efficient grid operation. This study presents a critical comparison of traditional statistical methods and machine learning approaches for forecasting solar irradiance using the benchmark Folsom PLC dataset. Two primary research questions are addressed: whether machine learning models outperform traditional techniques, and whether time series modelling improves prediction accuracy. The analysis includes an evaluation of a range of models, including statistical regressions (OLS, LASSO, ridge), regression trees, neural networks, LSTM, and random forests, which are applied to physical modelling and time series approaches. The results reveal that although machine learning methods can outperform statistical models, particularly with the inclusion of exogenous weather features, they are not universally superior across all forecasting horizons. Furthermore, pure time series approach models yield lower performance. However, a hybrid approach in which physical models are integrated with machine learning demonstrates significantly improved accuracy. These findings highlight the value of hybrid models for photovoltaic forecasting and suggest strategic directions for operational implementation. Full article

Using light-emitting diodes (LEDs), we examined how different light wavelengths influence the hypersensitive response (HR) in tobacco plants infected with Pseudomonas syringae pv. tomato (Pst). Pst-infiltrated plants exhibited greater resistance to Pst infection under green and blue light compared to white and red light, as indicated by reduced HR-associated programmed cell death, lower H₂O₂ production, and up to 64% reduction in membrane damage. During the late stage of HR, catalase and ascorbate peroxidase activities peaked under green and blue LEDs, with 5- and 10-fold increases, respectively, while superoxide dismutase activity was higher under white and red LEDs. Defense-related genes CHS1, PALa, PR1, and PR2 were more strongly induced by white and red light. The plants treated with green or blue LEDs during Pst infection prompted faster degradation of phototoxic Mg-porphyrins and exhibited smaller declines in F_v/F_m, electron transport rate, chlorophyll content, and LHCB expression compared to those treated with white or red LEDs. By contrast, the induction of the chlorophyll catabolic gene SGR was 54% and 77% lower in green and blue LEDs, respectively, compared to white LEDs. This study demonstrates that light quality differentially affects Pst-mediated HR, with green and blue light more effectively suppressing HR progression, mainly by reducing oxidative stress through enhanced antioxidative capacity and mitigation of photosynthetic impairments. Full article

Developing a more efficient, cleaner, and energy-saving pretreatment process is the primary goal for lignocellulosic biofuels production. This study demonstrated the feasibility of circulating high-concentration acetone–butanol–ethanol (ABE) obtained via in situ product recovery (ISPR) as a pretreatment liquor. Taking ABE solvent separated from pervaporation (PV) and gas stripping (GS) as examples, results indicated that under dilute alkaline (1% NaOH) catalysis, the highly recalcitrant lignocellulosic matrices can be efficiently depolymerized, thereby improving fermentable sugars recovery in saccharification stage and ABE yield in subsequent fermentation stage. Results also revealed delignification of 91.5% (stream from PV) and 94.3% (stream from GS), with total monosaccharides recovery rates of 56.5% and 57.1%, respectively, can be realized when using corn stover as feedstock. Coupled with ABE fermentation, mass balance indicated a maximal 106.6 g of ABE (65.8 g butanol) can be produced from 1 kg of dry corn stover by circulating the GS condensate in pretreatment (the optimized pretreatment conditions were 1% w/v alkali and 160 °C for 1 h). Additionally, technical lignin with low molecular weight and narrow distribution was isolated, which enabled further side-stream valorisation. Therefore, integrating ISPR product circulation with lignocellulosic biobutanol shows strong potential for application under the concept of biorefinery. Full article

The penetration rate of variable energy sources in the current power grid is increasing, with the aim being to expand the use of these energy sources and to replace the traditional black start power supply. This study investigates the black start of a photovoltaic storage joint system based on the system’s short-circuit ratio threshold. Firstly, the principles and control modes of the photovoltaic (PV) system, energy storage system (ESS), and high-voltage direct current (DC) transmission system are studied separately to build an overall model; secondly, computational determinations of the short-circuit ratio under different scenarios are introduced to analyze the strength of the system, and the virtual inertia and virtual damping of the PV system are configured based on this; finally, the change trend of the storage system’s state of charge (SOC) is computed and observed, and the limits of what the system can support in each stage are determined. An electromagnetic transient simulation model of a black start system is constructed in PSCAD/EMTDC, and according to the proposed recovery strategy, the system frequency is maintained in the range of 49.4~50.6 Hz during the entire black start process; the fluctuation in maximum frequency after the recovery of the DC transmission system is no more than 0.1%; and the fluctuation in photovoltaic power at each stage is less than 3%. In addition, all the key indexes meet the requirements for black start technology, which verifies the validity of the strategy and provides theoretical support and a practical reference for the black start of a grid with variable energy sources. Full article

Papillomaviruses (PVs) are double-stranded DNA viruses known to induce a variety of epithelial lesions in dogs, ranging from benign hyperplasia to malignancies. In regions of rich biodiversity such as the Western Amazon, data on the circulation and genetic composition of canine papillomaviruses (CPVs) remain scarce. This study investigated CPV types present in oral and cutaneous papillomatous lesions in domiciled dogs from Acre and Rondônia States, Brazil. Sixty-one dogs with macroscopically consistent lesions were clinically evaluated, and tissue samples were collected for histopathological examination and PCR targeting the L1 gene. Among these, 37% were histologically diagnosed as squamous papillomas or fibropapillomas, and 49.2% (30/61) tested positive for papillomavirus DNA. Sequencing of the L1 gene revealed that most positive samples belonged to CPV1 (Lambdapapillomavirus 2), while one case was identified as CPV8 (Chipapillomavirus 3). Complete genomes of three CPV1 strains were obtained via high-throughput sequencing and showed high identity with CPV1 strains from other Brazilian regions. Phylogenetic analysis confirmed close genetic relationships among isolates across distinct geographic areas. These findings demonstrate the circulation of genetically conserved CPVs in the Amazon and reinforce the value of molecular and histopathological approaches for the accurate diagnosis and surveillance of viral diseases in domestic dogs, especially in ecologically complex regions. Full article

Background: The influence of the initial ablation modality on pulmonary vein (PV) reconnection and substrate characteristics in redo procedures for recurrent atrial fibrillation (AF) remains unclear. We assessed how different thermal strategies—ablation index (AI)-guided radiofrequency (RF) versus cryoballoon (CB) ablation—affect remapping findings during redo pulmonary vein isolation (PVI). Methods: We included patients undergoing redo ablation between 2015 and 2024 with high-density electroanatomic mapping. Initial PVI modalities were retrospectively classified as low-power, long-duration (LPLD) RF; high-power, short-duration (HPSD) RF; or second-/third-generation CB. Reconnection sites were mapped using multielectrode catheters. Redo PVI was performed using AI-guided RF. Segments showing PV reconnection were reisolated; if all PVs remained isolated and AF persisted, posterior wall isolation was performed. Results: Among 195 patients (LPLD: 63; HPSD: 30; CB: 102), complete PVI at redo was observed in 0% (LPLD), 23.3% (HPSD), and 10.1% (CB) (p < 0.01 for LPLD vs. HPSD). Reconnection patterns varied by technique; LPLD primarily affected the right carina, while HPSD and CB showed reconnections at the LSPV ridge. Organized atrial tachycardia was least frequent after CB (12.7%, p < 0.002). Conclusion: Initial ablation strategy significantly influences PV reconnection and post-PVI arrhythmia patterns, with implications for redo procedure planning. Full article

This paper presents an annual, real-world evaluation of the performance and economics of a residential photovoltaic (PV) system coupled with a battery energy storage system (BESS) in southern Poland. The system, monitored with 5 min resolution, operated under time-of-use (TOU) electricity tariffs. Seasonal variation was significant; self-sufficiency exceeded 90% in summer, while winter conditions increased grid dependency. The hybrid system reduced electricity costs by over EUR 1400 annually, with battery operation optimized for high-tariff periods. Comparative analysis of three configurations—grid-only, PV-only, and PV + BESS—demonstrated the economic advantage of the integrated solution, with the shortest payback period (9.0 years) achieved with financial support. However, grid voltage instability during high PV production led to inverter shutdowns, highlighting limitations in the infrastructure. This study emphasizes the importance of tariff strategies, environmental conditions, and voltage control when designing residential PV-BESS systems. Full article