Search Results (2,010)

Under the growing pressures of climate change and low-carbon transition, economic policy instruments play an important role in shaping technological innovation in renewable energy industries. This study examines the association between research and development (R&D) tax deduction intensity and patent-based low-carbon innovation among Chinese photovoltaic firms. Using a panel of 71 Chinese A-share listed photovoltaic firms during 2019–2024 and two-way fixed effects models, we find no stable association between R&D tax deduction intensity and firms’ overall innovation output. However, after distinguishing innovation types, R&D tax deduction intensity is positively associated with radical innovation output and negatively associated with incremental innovation output, indicating that R&D tax deduction intensity leads to a structural reallocation of innovation resources rather than a simple expansion of innovation quantity. Further results suggest that financing constraints and the external government subsidy intensity shape the marginal effect of R&D tax deduction intensity. Financing constraints exhibit a relatively stable moderating role, while the effect of the government subsidy intensity becomes more evident when firm financing conditions are jointly considered. These findings highlight the need to evaluate energy-transition policies not only by aggregate innovation output, but also by innovation structure, firm heterogeneity, and complementary policy environments. Full article

This study presents a mathematics-based simulation model for designing, analyzing, and optimizing offshore green hydrogen stations powered by solar photovoltaic systems, applicable to any location worldwide. Developed in Python, the model integrates environmental, physical, and technological parameters to simulate and forecast hydrogen production via water electrolysis using alkaline (ALK) or proton exchange membrane (PEM) electrolyzers, combined with an adiabatic compressor that enhances energy storage and facilitates integration into smart grids. The five-phase modular methodology includes timeframe definition; estimation of solar electricity generation based on solar trajectory and the geographic orientation of photovoltaic panels; performance modeling of electrolyzers and compressors; and the integration of all components into a cohesive system. A case study demonstrates the model’s real-world applicability. Results from the Gulf of Cadiz case study show a substantial increase in solar energy capture in offshore environments due to reduced atmospheric pollution and sea-surface reflection. The reflected component is modeled as a function of sea-surface flatness. This reflection increases the daily average solar irradiance received by the photovoltaic panels by 8.44%. Under the modeled 2026 conditions and equivalent irradiance levels, the ALK electrolyzer produces 3.347% more hydrogen than the PEM electrolyzer. In addition, a 20% increase in electrolyzer efficiency raises hydrogen production by 32.35%, whereas the same increase in compressor efficiency improves production by 0.758%. These impacts directly correlate with proportional reductions in the photovoltaic panel surface area, driven by increased electricity generation capacity, which translates into smaller infrastructure needs. The model enables quantitative evaluation of trade-offs among solar irradiance, component performance, and system design. It supports cost reduction through optimized sizing and improved integration. This approach contributes to lowering the Levelized Cost of Electricity (LCOE) and promoting the viability of marine-based green hydrogen deployment. Full article

The energy management (EM) of smart multi-carrier energy hub (SMCEH) systems for cost and emission reduction remains a challenging problem due to the diversity of renewable energy resources (RERs), varying load demands, and the stochastic nature of these resources. This paper addresses the EM problem of SMCEHs to minimize operational costs and greenhouse gas (GHG) emissions using the particle swarm optimization (PSO) algorithm. The studied SMCEHs are designed to simultaneously supply electrical, cooling, and thermal demands. The hub system comprises wind turbines (WTs), photovoltaic (PV) panels, gas turbines (GT), electric chillers (EC), gas boilers (GBs), absorption chillers (AC), battery storage systems, and thermal storage units. To assess system performance and the impact of key technologies, three case studies are investigated: (i) EM of SMCEHs without RERs, (ii) EM of SMCEHs with RERs, and (iii) EM of SMCEHs with RERs and an integrated carbon capture unit (CCU). These scenarios enable a systematic evaluation of the role of renewable integration and carbon capture in enhancing system performance. The results demonstrate that incorporating RERs into SMCEHs leads to a substantial reduction in both operational costs and GHG emissions. Furthermore, the integration of a CCU provides additional emission reductions, underscoring its effectiveness in supporting the low-carbon operation of SMCEHs. The obtained results show that integrating RERs into SMCEH decreases the total cost and emissions by 64.12% and 7.95%, respectively, compared to the scenario without RERs. Furthermore, the integration of the CCU into SMCEHs provides a 39.36% reduction in total costs and a 72.57% decrease in CO₂ emissions. The suggested energy management solution promotes a sustainable and low-carbon emission system by maximum utilization of the RERs and CCU. Full article

The goal of agrivoltaic engineers is to use growing space for the synergistic production of both food and energy, typically via photovoltaic (PV) capture. Most research in this area has been carried out in arid, high-light environments, but subtropical and temperate regions are also critical production zones, and installation designs vary considerably. In this study, tomato and lettuce production using an agrivoltaic high tunnel (HT) design specific for a subtropical environment (NE Georgia, USA, USDA Zone 8A) was tested using organic production standards. The design utilized typical HTs (approx. 11 m × 5 m) with solar panel arrays hung internally. The design aimed to (1) meet off-grid power needs, (2) mitigate excessive temperature and humidity, (3) balance shade and plant productivity, and (4) simplify installation and maintenance. Treatments were replicated at the HT level, and cultivar differences were assessed to identify genotypes that might serve in future work to optimize yield under partial shade. In 2023 and 2024, we employed novel organic photovoltaic (OPV) panels, which are partially opaque. The OPV panels provided sufficient energy needs to maintain beneficial conditions without external power sources. In 2024, tomato plants in the OPV HTs experienced an area-weighted daily light integral (DLI, mol photons m⁻² d⁻¹) of approximately 31.8 (95% CI [28.9, 34.7]), compared to 34.7 (95% CI [31.8, 37.6]) in non-OPV HTs, an approximate reduction of 8%. Average maximum temperatures in the OPV HTs were 33.5 °C (95% CI [30.6, 36.4], compared to 35.1 °C (95% CI [30.9, 39.2]) in the non-OPV HTs, an approximate reduction of 1.6 °C. In 2023, tomato marketable yield was reduced by approximately 0.9 kg per plant in OPV HTs compared to non-OPV HTs (p = 0.023). In 2024, yields were statistically equivalent across all treatments (p > 0.1), while marketable fraction was improved relative to 2023 and was greatest in the HTs. Lettuce yield for both years was unaffected by the presence of HTs or OPV panels (p > 0.1). In 2025, we conducted an additional experiment using a shade-equivalent array of conventional 100% opaque photovoltaic (PV) panels and observed a similar reduction in DLI and no significant impact on tomato yield parameters (p > 0.1 Both designs were effective at equilibrating conditions inside the HTs to ambient temperature levels outside the tunnels. Using results from the study, an app for agrivoltaic value estimation was developed. Based on that software, the presented agrivoltaic design under currently available silicon–PV technology achieves an 18% annual return, assuming system depreciation is minimal and surplus energy could be applied to other on-farm needs. Full article

This proof-of-concept study proposes a photovoltaic transparent vacuum insulation panel (PV-TVIP) and evaluates its heat transfer and power generation characteristics with increased temperatures, and light transmission characteristics for visible light and ultraviolet wavelengths. The study was conducted with a climate-controlled chamber mimicking the common temperature range of Sapporo, Japan. The average TVIP heat flux was measured to be 65–75 W/m² with a U-value of 1.95–2.3 W/(m²∙K). Compared to earlier measurements to see the effect of seasonal atmospheric conditions to the quality of the TVIP, it was determined that the TVIP manufactured during winter conducted less heat, assumed to be caused by decreased humidity. Placing the PV between the TVIP and a glass pane increased the operating temperature by 26.06 °C and decreased power generation by 13%. Afterwards, the transparency of the TVIP and PV-TVIP were measured under a bright light therapy lamp, showing that TVIP reduced the amount of most visible light wavelengths by 50% and the PV-TVIP by 90%. UV radiation was respectively reduced by approximately 78% and 100%. The results show that while PV-TVIP shows potential as a BAPV window retrofit solution, its manufacturing requires optimized, low-humidity conditions during all phases of the manufacturing process. Full article

Active ripple suppression branches (ARSBs) are widely employed in switching power converters interfacing ripple-sensitive devices such as batteries, supercapacitors, hydrogen electrolyzers, fuel cells, and photovoltaic panels. Conventional ARSBs share the main converter DC-link voltage and require inductance comparable to that of the primary power stage, resulting in high semiconductor voltage stress and bulky magnetic components. Recent studies have proposed supplying the ARSB from a lower auxiliary voltage source, significantly reducing both inductance value and semiconductor voltage ratings. This paper shows, however, that lowering the ARSB rating while keeping the series capacitance value unaltered inherently increases residual current ripple, degrading ripple-cancellation performance. It is then demonstrated that this limitation should be overcome by increasing the ARSB capacitance in inverse proportion to the rating reduction, thereby restoring ripple suppression performance. Furthermore, it is revealed that for converters operating at a fixed duty cycle, a unique operating point exists where the ARSB capacitor can be eliminated without sacrificing the ripple attenuation ability of the circuit. The resulting capacitorless implementation reduces component count, size, complexity, and cost while improving ripple suppression. Simulation and experimental results validate the theoretical analysis and confirm the feasibility and effectiveness of the proposed capacitorless open-loop operating ARSB. Full article

This scientific article presents a comprehensive case study detailing the design of a fully off-grid household in Poland, utilizing an energy solution that combines high-pressure hydrogen energy storage and photovoltaic (PV) technology. In response to the growing demand for sustainable and self-sufficient energy sources, the current study investigates the efficiency and yearly energy balance of this innovative system. The off-grid household is powered by a hybrid system that seamlessly integrates PV panels to harness solar energy and a high-pressure hydrogen energy storage system for long-term energy management. The presented case study examines the design and performance of a system integrating solar energy production with hydrogen storage. Through an analysis of real-world data and operational parameters, this research contributes valuable insights into the viability of such an off-grid solution in Polish environmental conditions. These findings provided an interesting approach to off-grid residential systems, offering a glimpse into the possible future of residential energetic autonomy in the pursuit of a greener and more resilient energy landscape. Full article

Photovoltaic systems have recently attracted significant attention for the free, clean, and sustainable energy they generate. In this work, thermal image processing techniques were developed and utilized to classify hot spots on solar photovoltaic panels. Thermal images were classified into three categories: (a) ideal images, where images do not contain hot spots; (b) images affected by shadow; and (c) images affected by bird drops. The proposed classification was developed using image processing techniques, including histogram analysis, contrast enhancement, and filtering tools. The attained classes are then matched to the decrease in electrical power output. The proposed method was applied to thermal images to detect and classify the target hot spot. Experimental results showed that the estimated error was approximately 6.3% of the total number of images used in the research, with error rates of 6.57% for the shadow hot spot type and 6.67% for the bird drops (mud-like class). Moreover, the accuracy of the proposed method was around 93.7%. Full article

In this study, an experimental investigation was conducted to evaluate the thermal and electrical behavior of three photovoltaic panel configurations under controlled indoor solar irradiation of 600, 800, and 1000 W/m², considering both natural and forced convection to the surrounding air. The tested configurations included an uncooled reference panel (PV-1), a PCM-cooled panel incorporating a flat rear container (PV-2), and a proposed PCM-cooled panel equipped with an extended-surface rear container (PV-3). A PCM characterized by a phase change temperature range of 41–48 °C was employed. The results showed that the extended-surface PCM configuration associated with PV-3 provides a more effective passive cooling solution compared to the flat container design. Under natural convection, this thermal advantage of PV-3 became more pronounced, with a maximum temperature reduction of 15 °C at 1000 W/m² after 170 min of operation, compared to PV-2. Consequently, PV-3 achieved the highest electrical performance, delivering peak efficiency enhancements of 12.05% and 7.38% relative to PV-1 and PV-2, respectively, and average efficiency gains of 7.06% and 5.35% over the entire test period. Under forced convection, however, performance differences among the configurations were minimal because forced convection dominated the heat removal process, reducing the influence of the PCM. Full article

In the context of the global transition toward sustainability and energy efficiency, the retrofitting of existing service buildings has become a strategic priority. With the increasing adoption of electric vehicles (EVs) and the need to reduce greenhouse gas emissions, adapting these buildings is essential to achieving low-carbon urban environments. This paper presents a numerical tool developed to simulate the energy performance of a service building and to evaluate the impact of multiple energy efficiency measures on energy consumption and CO₂ emissions. The assessed measures include the installation of photovoltaic panels on roofs and facades, optimization of Heating, Ventilation, and Air Conditioning (HVAC) systems through temperature set-point adjustments, improvements to the building envelope and integration of electric mobility infrastructure. The analysis focuses on an existing building in Lisbon, Portugal, considering both individual and combined effects of these strategies. The results indicate that combined implementation of all measures, including EV integration, can reduce energy demand and CO₂ emissions by up to approximately 50%. However, regulatory uncertainty regarding EV accounting remains a challenge, highlighting the need for clearer policies to support sustainable urban transformation. Full article

Solar insecticidal lamps are important physical control devices for green pest management, but faults in their photovoltaic power supply units can reduce trapping efficiency and shorten service life. To improve fault identification under complex agricultural environments, this study proposes a signal-image-level multimodal fusion network (SIL-MMFN) for detecting and classifying photovoltaic panel operating states in solar insecticidal lamps. The method combines time-series measurements with short-time Fourier transform (STFT)-based time–frequency images. A convolutional image branch extracts spatial features from time–frequency representations, whereas a bidirectional GRU branch with attention models temporal dependencies in the original signals. In addition, physics-informed features based on the illumination–current residual and output power are introduced to enhance discriminative fault information. Field data collected from four agricultural deployment nodes were used to classify normal, open-circuit, and mismatch states. Experimental results show that the proposed method achieved an accuracy of 97.5%, precision of 96.7%, recall of 97.8%, and macro-F1 score of 97.3%, outperforming single-modality and representative comparison models. The results indicate that multimodal fusion helps reduce confusion between open-circuit and mismatch faults and provides a potential approach for operating-state monitoring and maintenance of agricultural photovoltaic equipment. In this study, fault diagnosis refers to the detection and classification of photovoltaic panel operating states, including normal, open-circuit, and mismatch conditions. Full article

Horqin Sandy Land suffers from desertification, drought, and low fertility, limiting soybean production. Agrivoltaics provides a promising integrated model; however, the effects of agrivoltaics combined with water–fertilizer management on crop productivity remain unclear. A 2-year field experiment was conducted in a semi-arid area with three treatments, open-field control (Open), shaded area under panels (Under), and light-exposed area inter-panels (Gap). Results showed that photovoltaic systems combined with integrated water–fertilizer management improved soybean yield, soil water, and nutrient conditions. Soybean grain yield was 60.7% and 38.2% higher in the Gap and Under treatments, respectively, than in the Open. The highest yield in the Gap treatment resulted from both enhanced photosynthesis and improved root development. The Under endured light stress but exhibited morphological plasticity (plant height and leaf area increased by 43.1%, 48.2%), and shading alleviated water stress since soil water content was increased by 81.6–119.0% during growing seasons, transpiration rate (Tr) decreased by 55.1%, and leaf water use efficiency (WUE) increased by 48.8%. The Open suffered from soil degradation and water and fertilizer loss, resulting in severely limited yield. Agrivoltaics increased net income by 1466 CNY·ha⁻¹ and improved soil nutrients, demonstrating economic and ecological benefits. Thus, it is a suitable technical model for semi-arid sandy regions. Full article

This paper presents a novel approach to designing and implementing an Artificial Neural Network (ANN) for maximum power point tracking (MPPT), trained solely on unshaded photovoltaic (PV) manufacturer datasheets and capable of tracking and predicting the maximum power point (MPP) under changing shading conditions. This is also known as partial shading conditions (PSC). PSC arises when shade covers sections of the PV panel due to clouds, trees, dust, or man-made objects such as tall buildings. The proposed ANN-based MPPT technique addresses a common issue faced by conventional MPPT methods under PSC: inaccurate MPPT. PSC induces oscillations on the power-to-voltage curve, resulting in multiple local maxima (LMPPs). However, existing ANN-based MPPT methods are developed and trained on shaded PV datasets. This Neural Network (NN) tracking method complicates the training, development, and implementation processes. It increases the cost of development and requires physical, real-world data collection that requires hardware and a lot of time. All this can be avoided with unshaded PV datasheets. The input parameters used to train the model are temperature (T) and irradiance (G), and the output parameters are maximum power (Pmp) and maximum voltage (Vmp). The ANN-based MPPT technique demonstrated strong performance, accurately predicting the global MPP (GMPP) under PSC with high correlation and low prediction error. Full article

This paper presents a parametric numerical study on the cooling performance of photovoltaic panels using water and an Al₂O₃-based nanofluid. The increase in operating temperature leads to a decrease in electrical efficiency, making thermal management a key factor in optimizing these systems. The analysis was carried out through numerical simulations in ANSYS, aiming to evaluate the influence of volumetric flow rate and inlet temperature of the cooling fluid on the panel cooling time under transient conditions. The results show that the performance of the Al₂O₃ nanofluid depends on the flow rate of the cooling fluid. At a low flow rate of 0.05 m³/h and a concentration of 4%, the cooling time is reduced by approximately 18–22% compared to water, while this advantage diminishes as the flow rate increases. A favorable operating region was also observed within the investigated laminar and near-transitional range, beyond which increasing the flow rate produced only limited additional reductions in cooling time under the assumptions of the numerical model. The findings highlight the importance of correlating the thermophysical properties of the fluid with flow parameters in order to optimize the thermal management of photovoltaic panels. Full article

The increasing frequency of extreme weather events, particularly hailstorms, driven by climate change, poses growing threats to the resilience, environmental sustainability, and long-term performance of photovoltaic (PV) systems. This study evaluates the environmental impacts of a 12 kWp rooftop PV installation in Brescia, northern Italy, through a comparative Life Cycle Assessment (LCA) of three system configurations: a standard unprotected system (Scenario A), one equipped with a retractable polycarbonate hail-protection panel with automated weather-sensor activation (Scenario B), and one using thicker reinforced front-glass modules (Scenario C). The analysis follows a cradle-to-gate plus operational maintenance phase (30-year horizon, excluding end-of-life) system boundary and employs the ReCiPe 2016 Midpoint (H) methodology across 18 environmental impact categories. A novel integration of the Social Discount Rate (SDR) to the LCA framework—constituting a Discounted LCA (D-LCA)—incorporates both temporal discounting and risk dimensions into the environmental evaluation. A structured PESTEL-based risk taxonomy is applied to derive scenario-specific SDRs, with the Environmental risk category as the key differentiator between configurations. The static LCA identifies Scenario A as the lowest-impact option, while the D-LCA framework reverses this ranking: Scenario C achieves the highest Net Present Value of Emissions, followed by Scenario A. A negative NPV-E for Scenario B reflects the temporal cost of a large, front-loaded construction debt rather than absolute environmental harm. D-LCA framework should be interpreted as a complement to the full 18-category static LCIA profile, not a replacement. These results demonstrate that risk-informed D-LCA provides a more policy-relevant environmental sustainability assessment than static LCA for long-lived energy infrastructure subject to climate-driven operational risks. Full article