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Search indexed NASA NTRS and DOE OSTI research on propulsion, heat transfer, battery materials and energy systems. Follow report and document links to the original sources.

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At least 55 records · Page 3
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Field-Aging Test Bed for Behind-the-Meter PV + Energy Storage

Small DC-coupled battery test systems are deployed at the National Renewable Energy Laboratory to evaluate capacity fade models and report on performance parameters such as round-trip efficiency under indoor and outdoor deployment scenarios. Initial commercial battery products include LG Chem RESU lithium-ion (Li-ion) and Avalon vanadium redox flow batteries. Adapting indoor lab-scale test methods to outdoor systems has challenges, including maintaining constant temperature and fully controlling batteries through standard discharge curves. Initial measurements show the Li-ion battery systems performing within expectations, near 85% round-trip efficiency. Initial lifetime modeling and measurements indicate that battery capacity could degrade by 20%-35% over 10 years at current rates.

14 SOLAR ENERGY↗
DOE OSTI2021

Thermal model to quantify the impact of sub-bandgap reflectance on operating temperature of fielded PV modules

Minimizing module heating is an effective way to increase the lifetime energy output of photovoltaic systems. Maximizing the reflection of light that is unusable for energy conversion is one of the most promising ways to reduce the operating temperature of fielded modules. We derive a model based on a steady-state energy balance to quantify the temperature benefit of cell or module optical modifications aimed at improving reflection of light with photon energies below the photovoltaic cell bandgap energy. This more detailed model is then simplified so that, from outdoor measured data, temperature differences arising from reflectance can be isolated from those arising from irradiance, wind speed, and module standard-test-condition efficiency.

14 SOLAR ENERGY↗
DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Laboratory-based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Additionally, there is a growing concern on characterizing the non-uniformity of field exposure. Therefore, data from field surveys are critical to assess the performance of component lifetimes. Using a documented field survey protocol, 19 field surveys were conducted. The focus of this survey strategy is to investigate spatial continuity in degradation modes. By combining field survey data with real-time satellite weather data, stressor / response models have been trained. Generalized additive Models (GAM) model was created to predict the value of degradation based on measured predictors. Two different GAM constructions were testing using different implementations of basis splines. The model includes variables on the environmental stressors of the system and the location of each measurement in the PV mounting structure. The incorporation of hierarchical structure into the models allowed for material specific degradation rates, while maintaining the assumption of a global trend. The model performed well with an adjusted R2 of 0.975 for yellowness index prediction.

backsheet↗
DOE OSTI2023

A Comparison of Emerging and Industry Benchmark Photovoltaic Backsheets Between Different Outdoor Locations

Recent interest within the photovoltaic (PV) module industry is largely directed toward enhanced lifetimes in the field, balanced with improved recyclability. Traditionally, fluoropolymer-based backsheets have been used, however, are difficult to recycle. Emerging polyolefin (PO)-based backsheets are more recyclable and can be formulated to be robust. Properties of different fluoropolymer- and non-fluoropolymer-based backsheet coupons and in encapsulated silicon mini modules that have been fielded in Albuquerque, NM and Cocoa, FL are reported here. Seven backsheets were examined: two novel PO's, TPT, APO, PPE, AAA, and KPf. Methods of examination include module electrical performance (I - V flash test), surface morphology (optical microscope and gloss), polymer chemical structure (FTIR), EL imaging, mechanical tensile testing, DC breakdown voltage, DSC (phase transitions), and optical performance (reflectance spectra).

industries↗
DOE OSTI2024

Machine Learning-Driven Reliability Estimation of PV Inverters Considering Alert-Ambient Variability

Weather-induced spatio-temporal degradation limits outdoor PV inverter lifetime and reliability, necessitating advanced data analysis. This study employs a top-down, data-driven approach utilizing multiple machine learning (ML) algorithms to estimate inverter reliability in a 1.4 MW PV power plant, considering factors such as irradiance, humidity, temperature, time of day, and weather conditions. An extensive alert dataset from 17 identical inverters, including alert types, propagation, and frequency, reveals significant correlations with environmental factors and inverter output power, enabling the construction of a performance reliability model. Dual-stage supervised-ML models are evaluated for accuracy, with the ‘classification-regression’ model by an artificial neural network (ANN) tested on the averaged “Alert-Ambient” dataset, which is outperformed by ‘clustering-regression’ models using random forest (RF) and K-Nearest Neighbors (KNN) on individual inverter datasets. K-means clustering applies principal component analysis to reduce dimensions, achieving improved accuracy beyond the 80% achieved by ANN on the averaged dataset. Second-stage regression estimates inverter reliability with a mean square error of 0.0195 on the averaged dataset and as low as 0.002 on individual inverter datasets using RF. Furthermore, these findings highlight the method's suitability for estimating PV inverter output reliability under ambient conditions, essential for digital twin development and related applications.

14 SOLAR ENERGY↗
DOE OSTI2022

Life after 30 years - a PV system in Colorado

The PV system consisted of 155 monocrystalline Si Arco M53 panels, installed in 1987. 80 were installed on a fixed tilt rack, and 75 on 5 separate dual-axis passive trackers. The system was decommissioned in 2017 due a campus re-design and building expansion at the Platte River Power Authority (PPRA) site. Prior to decommissioning a site visit established the following failure type observations: EVA browning (100%), cracked cells (60%), and cracked wires or grommets (9%, or more if replacements were made selectively on cracked parts). EVA browning is typical in modules of this vintage, whereas the occurrence of so many cracked cells is surprising and is likely weather-related. The fairly frequent occurrence of cracked wires and grommets serves as a reminder that accelerated testing of these components is important for goals of low-cost 50-year energy generation. In smaller numbers, thermal effects (7%), bent frames (1%), and cell corrosion (<1%) were observed. A sample of 20 modules was selected and brought to NREL for in-depth characterization. Because baseline measurements were not available using the nameplate rating, which can be inaccurate, revealed a mean module sample degradation of 0.7 %/year. Sporadic production data during the 30 years determined a system degradation rate closer to 1.2 %/year. Subsequently, four of these modules were re-mounted outdoors at NREL and are now approaching a total lifetime of 34 years.

durability↗
DOE OSTI2022

Nafion Passivation of c-Si Surface and Edge for Electron Paramagnetic Resonance

Effective surface passivation of crystalline silicon (c-Si) surface by reducing the carrier recombination rate has led to modern c-Si solar cells with efficiencies > 25% in both laboratory and industrial settings. Typical mainstream surface passivation techniques include high-temperature silicon oxide (SiOx), amorphous silicon (a-Si:H), hydrogen-rich silicon nitride (SiNx), and aluminum oxide (Al2O3) [1]. They have demonstrated excellent surface recombination velocity of < 1 cm/s owing to both chemical passivation (via the hydrogen saturation of Si dangling bonds at the c-Si surface), and field-effect passivation (via the band bending from the fixed charge of the dielectric layers). Recently, several groups have studied solution-based organic materials for c-Si passivation, including bis(trifluoromethane)sulfonimide (TFSI), polystyrenesulfonate, and Nafion [2, 3] via spin or dip coating. All films were processed at ambient room temperature, and a high lifetime of 12 ms, as well as a low saturation current density (J0) of 16 fA/cm2, have been demonstrated using Nafion passivation [2]. However, despite the air instability, Nafion has several advantages when introduced to PV applications: (a) Wafer quality can be measured at high throughput after several process stages. (b) It is compatible with PL mapping, compared to HF liquid passivation which cannot be performed in room ambient. (c) Nafion process is fast and poses fewer constraints on process complexity, and cleanness, compared to Al2O3 and a-Si:H passivation. (d) Nafion is the ideal room temperature passivation, which can be applied onto a small fragment of a degraded module to investigate microscopic mechanisms when studying degradation mechanisms. Edge passivation is needed for advanced characterization tests such as Electron Paramagnetic Resonance (EPR), Electrically Detected Magnetic Resonance (EDMR), Deep Level Transient Spectroscopy (DLTS), local cell current-voltage (J-V), and Suns-Voc. In such cases, the laser-damaged minicell edges will obscure the true degradation mechanisms, and Nafion can effectively passivate them without causing further degradation through elevated-temperature processes. In this contribution, we show the passivation results of Nafion on bare nCz and compare it with a Al2O3 witness. We highlight the importance of edge pre-treatment before Nafion to achieve good passivation. We show that Nafion can reach decent passivation without undergoing high-temperature processes. We also explore the temperature dependency of Nafion through photoluminescence (PL) study and demonstrate the application of Nafion under cryogenic temperature (~6 K) through EPR. Our results reveal that Nafion can reduce surface and edge Si dangling bonds at low temperatures. Thus, it can be used as an effective room temperature passivation technique for advanced characterization methods.

c-Si↗
DOE OSTI2026

CdTe Core: Final Technical Report (FTR)

CdTe is presently the cost-leading thin-film PV technology, directly competing with Si at scale, even when domestically manufactured. While an impressive technology, its efficiency remains much below the detailed balance limit with the largest cause due to its low photovoltage and fill factor. To realize gains, the carrier concentration, minority carrier lifetime, and interface recombination all need to be improved simultaneously over historic levels. Using a new defect chemistry (group V doping instead of copper) has been identified as a viable route using single crystal systems. This project focused on implementing this new defect chemistry in scalable, polycrystalline thin-film photovoltaic CdTe devices with tasks focusing improvements to the front interface, absorber, and rear interface as well as capability development & stakeholder engagement. The goal of the project was to establish a strategy using devices, test structures, detailed characterization, and modeling to quantify the sources of losses in state-of-the-art CdTe photovoltaic devices. Using this strategy and advanced synthesis, losses at the front interface, absorber, and rear interface were worked on in parallel. The final objective was to significantly improve the voltage deficit in CdTe devices to enable improvements in photovoltage and efficiency that can be implemented by industry in the near-term. Over the course of the project, the team developed new characterization techniques, analysis, and modeling which were then applied to state-of-the-art materials generated internally and collaboratively. In particular to enable rapid progress, NREL worked closely with First Solar where NREL grew complete devices as well as ones that interleaved process steps where First Solar had completed different steps such as absorber growth or absorber growth and activation using their baseline methods. Using detailed characterization and analysis including photoemission, photoluminescence, and scanning probe techniques enabled understanding of the loss pathways and area for improvements in our own and First Solar s materials. Ultimately, this contributed to the first series of new world record CdTe efficiencies since 2016, culminating in a 23.1% certified cell that was P-doped along with As-doped cells of similar performance. Internally, NREL improved the statistical variation in baseline As-doped devices and improved average photovoltage by over 100 mV. This was done through an improvement in absorber quality, changed front interface, and improved back contact. In addition to materially improving the fabrication processes at NREL, characterization, analysis, and modeling were developed and disseminated. NREL also played a pivotal role in community building over the course of this project working closely with the Cadmium Telluride Accelerator Consortium. NREL worked in a series of collaborations with academic and industry partners, leveraging knowledge and innovations from this project, as well as helped organize a series of workshops to ensure rapid progress in the field. Working closely with the academic community has led to a dissemination of knowledge; working with First Solar as increased US competitiveness First Solar expanded domestic production to ~10 GW and opened new facilities.

14 SOLAR ENERGY↗
DOE OSTI2022

Spatiotemporal Modeling of Real World Backsheets Field Survey Data: Hierarchical (Multilevel) Generalized Additive Models: Preprint

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Lab based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Field surveyed data is critical to assess the performance of component lifetime. Using a documented field survey protocol, 13 field surveys where conducted. Each measurement is encoded with it's spatial location in respect to the other modules. By combining field survey data on degradation predictors with real time satellite weather data, data-driven predictive models of backsheet degradation were trained. LOESS models were constructed to investigate the spatial dependence of measurements. It was found that micro-climatic effects like treelines, ground surface changes, and elevation changes effected the magnitude and variance of the measurements. A GAM model was created to predict the value of degradation based on measured predictors. The model includes variables on the climate of the system and the location of each measurement in the PV mounting structure. The model performed well with an adj:R2 of 0:95 for yellowness index prediction. The model was cross-validated using k-folds.

backsheet↗
DOE OSTI2021

Spatio-Temporal Modeling of Field Surveyed Backsheet Degradation

Assessing photovoltaic module backsheet durability is critical to increasing module lifetime. Lab based accelerating testing has recently failed to predict large scale failures of widely adopted polymeric materials. Field surveyed data is critical to assess the performance of component lifetime. Using a documented field survey protocol, 13 field surveys where conducted. Each measurement is encoded with it's spatial location in respect to the other modules. By combining field survey data on degradation predictors with real time satellite weather data, data-driven predictive models of backsheet degradation were trained. LOESS models were constructed to investigate the spatial dependence of measurements. It was found that micro-climatic effects like tree-lines, ground surface changes, and elevation changes effected the magnitude and variance of the measurements. A GAM model was created to predict the value of degradation based on measured predictors. The model includes variables on the climate of the system and the location of each measurement in the PV mounting structure. The model performed well with an adj.R 2 of 0.95 for yellowness index prediction. The model was cross-validated using k-folds.

backsheet↗
DOE OSTI2021

Perovskite Photovoltaic Devices with Carbon-Based Electrodes Withstanding Reverse-Bias Voltages up to –9 V and Surpassing IEC 61215:2016 International Standard

One of the key challenges of perovskite photovoltaics (PV) is the long-term stability. Although efforts are made to improve the lifetime of perovskite PV devices, their degradation under reverse-bias conditions is barely addressed. Herein, perovskite solar cells with carbon-based electrodes are presented which demonstrate superior resilience against reverse-bias-induced degradation. Although their breakdown voltage is identified to be at approximately -3.6 V, cells do not degrade until the applied reverse-bias exceeds -9 V. Two main degradation mechanisms are identified: 1) iodine loss due to hole tunneling into perovskite, which takes place even at low reverse-bias but decomposes the perovskite only after long time durations; and 2) rapid heating at large reverse-bias leading to formation of PbI2, which starts at shunts and then follows the path of the least resistance for the cell current, which is primarily influenced by the electrode sheet resistances. Finally, perovskite solar modules with carbon-based electrodes are demonstrated, which are subjected to a "hotspot" test described in the IEC 61215:2016 international standard at an accredited module testing laboratory. Passing this accelerated test for the first time confirms the superior stability of perovskite PV devices with carbon-based electrodes and highlights their large industrialization potential.

14 SOLAR ENERGY↗
DOE OSTI2024

CIGS Technology Advancement via Fundamental Modeling of Defect/Impurity Interactions (Final Technical Report)

The primary goals of the proposed work were to provide modeling tools (and the associated insight which comes along with model development) for design and optimization of CuIn x Ga 1-x Se 2 (CIGS) and CdSeTe (CST) solar cell manufacturing processes and to establish the foundation for comprehensive end-to-end predictive modeling tools to enable optimization of thin film photovoltaic technology for performance, cost, yield, and reliability. The initial focus of efforts within this project was to develop coupled process/optical/device models for CIGS PV technology and to work with Siva Power to apply that TCAD (technology computer-aided design) system to improve the efficiency and reduce manufacturing costs for CIGS solar cells. Our approach to that end was to generate an extensive database of DFT calculations and to use those calculations via statistical thermodynamics methods and Monte Carlo simulation to develop and characterize models for the behavior of native defects as well as intentional and unintentional impurities, including the redistribution of the primary components of CIGS films. Increased effort went toward coupling those models for defect behavior and composition evolution to the performance of multicrystalline CIGS solar cells via prediction of doping level and recombination lifetime as function of manufacturing process. In the second budget period, the project pivoted to developing a similar system for the CdSeTe system, focused especially on understanding the role of Se/Te alloy concentration. Execution of the project resulted in the successful development of TCAD systems for both CIGS and CdSeTe thin film PV within the Synopsys Sentaurus framework by utilizing the Alagator interface. In the first budget period of the project, we developed quantitative models for the major components of CIGS PV and implemented them within a framework that couples process, optical, and device simulation. From the insights we have gained, we identified novel opportunities for enhancing CIGS solar cell performance and have laid the groundwork to further optimize the layer structure, composition profile, and thermal cycles for substantially improved efficiency and lower manufacturing costs. For the CIGS system, process changes to achieve greater than 1% absolute enhancement in efficiency were identified, but testing of those approaches was stymied by lack of a domestic CIGS manufacturing partner after the closure of Siva Power as well as Miasole. For CdSeTe, a fully capable TCAD system only became ready to apply near the end of the project period, so substantial opportunities remain to apply those models to enhance the leading thin film PV technology.

14 SOLAR ENERGY↗
DOE OSTI2021

Evaluating Non-Fluoropolymer-Based Co-Extruded Backsheets Using Combined-Accelerated Stress Testing and Materials Forensics

Co-extruded, non-fluoropolymer backsheet films for photovoltaic (PV) modules have gained popularity in recent years based on their cost competitiveness. However, their viability has been thrown into question as a result of widespread, early-life field-failures of some materials, particularly the polyamide (PA)-based “AAA” backsheet product. Failure to detect weaknesses in those earlier products could be due, in part, to insufficient quality testing practices. New testing protocols have recently been developed to better evaluate such materials. Here we show the testing results for a non-fluoropolymer, co-extruded PA-based backsheet film, which demonstrates greater durability than AAA and some commercial fluoropolymers. Using material characterization techniques including Fourier-transform infrared spectroscopy (FTIR), wide-angle x-ray scattering (WAXS), differential scanning calorimetry (DSC), X-ray photoelectron spectroscopy (XPS), elongation-to-break (ETB), and optical microscopy we perform a comprehensive failure analysis to better understand the materials weaknesses. This analysis can ultimately inform how best to improve the material and extend its lifetime. This work serves as a demonstration that some materials should not be discounted due to a single poorly designed product and that more appropriate qualification testing with subsequent materials analysis can be used to develop better materials.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2022

Photovoltaic System Health-State Architecture for Data-Driven Failure Detection

The timely detection of photovoltaic (PV) system failures is important for maintaining optimal performance and lifetime reliability. A main challenge remains the lack of a unified health-state architecture for the uninterrupted monitoring and predictive performance of PV systems. To this end, existing failure detection models are strongly dependent on the availability and quality of site-specific historic data. The scope of this work is to address these fundamental challenges by presenting a health-state architecture for advanced PV system monitoring. The proposed architecture comprises of a machine learning model for PV performance modeling and accurate failure diagnosis. The predictive model is optimally trained on low amounts of on-site data using minimal features and coupled to functional routines for data quality verification, whereas the classifier is trained under an enhanced supervised learning regime. The results demonstrated high accuracies for the implemented predictive model, exhibiting normalized root mean square errors lower than 3.40% even when trained with low data shares. The classification results provided evidence that fault conditions can be detected with a sensitivity of 83.91% for synthetic power-loss events (power reduction of 5%) and of 97.99% for field-emulated failures in the test-bench PV system. Finally, this work provides insights on how to construct an accurate PV system with predictive and classification models for the timely detection of faults and uninterrupted monitoring of PV systems, regardless of historic data availability and quality. Such guidelines and insights on the development of accurate health-state architectures for PV plants can have positive implications in operation and maintenance and monitoring strategies, thus improving the system’s performance.

photovoltaics↗
DOE OSTI2023

DuraMAT FY 2022 Annual Report: Towards Predicting Lifetime

The Durable Module Materials Consortium (DuraMAT) launched in November 2016 with five years of funding from the U.S. Department of Energy s (DOE's) Solar Energy Technologies Office (SETO). The program renewed in 2022 for an additional 6 years. DuraMAT is a multi-lab consortium led by the National Renewable Energy Laboratory, with Sandia National Laboratories (Sandia) and Lawrence Berkeley National Laboratory (LBNL) as core research labs. DuraMAT's overarching goal is to accelerate a sustainable, just, and equitable transition to zero-carbon electricity generation by 2035. We work in partnership with our 22-member industry advisory board and the technical management team at SETO. DuraMAT transitioned from our first five-year program into a new six-year program in 2022. It was a very busy year; DuraMAT wrapped up projects from DuraMAT 1 and kicked off new laboratory-led projects for DuraMAT 2. This transition brings new goals and a renewed focus on accelerating the energy transition by improving photovoltaic (PV) module reliability.

accelerated stress testing↗
DOE OSTI2024

Contactless Production Testing of Silicon Solar Cells

Critical cost reductions in silicon solar cells are made by minimizing silver usage in their metal contacts. The reason why is both clear and unavoidable—silver is not only expensive, but also a potential limiting resource for scale-up (the PV industry alone used 10.3 % of the global silver supply in 2020 [1]). The result is solar cells with vanishing electrical contact area that are extremely difficult to test prior to module manufacture, when they must be sorted for quality to maximize power output and reliability of the subsequent modules. This project developed a new measurement instrument that enables the transition to solar cells with very low silver content, including cell designs with both minimal busbars and no busbars at all to electrically contact. The resulting instrument at project completion demonstrated the ability to sort cells with comparable—or better—results than existing technology. Compared to existing cell-test instruments, the new tool has minimal contacting requirements, which lowers the maintenance costs and use of consumable parts. We demonstrated innovative, yet pragmatic, solutions for reporting a comprehensive set of measurement results useful for both cell sorting (going forward in the line for module manufacture) and process control (looking backward in the line to identify cell manufacturing issues from wafer to cell-test). In addition to the traditional current-voltage characterization at cell test—short circuit current, open-circuit voltage, power, efficiency and fill factor—the new tool maintains all the advanced parameter characterization of our existing product line such as a substrate doping measurement, carrier recombination (lifetime) analysis, and surface recombination analysis.

14 SOLAR ENERGY↗
DOE OSTI2020

Activation Energy for Solder Bond Degradation: Thermal Cycling of Field-aged Modules at Multiple Upper Temperatures

The reliability of solder joints in the solar cell metallization-interconnect system influences the lifetime of photovoltaic modules. Two field-aged modules-one with Sn 62 Pb 36 Ag 2 solder at the solder joints (Solarex MSX 60), and the other with the standard Sn 60 Pb 40 solder (Siemens M55)-were subjected to a modified thermal cycling (TC) test of IEC 61215. Three sections in each module were maintained at 85 degrees C, 95 degrees C, and 105 degrees C during the 15-minute high temperature dwell time. Current equivalent to the module short-circuit current was injected through the module when the chamber temperature was above 25 degrees C to simulate regular field operation. This novel approach aims to induce thermomechanical fatigue (TMF) at the solder joints and intermetallic compound (IMC) formation at the metal/solder interfaces. The activation energy (E a ) for solder bond degradation was calculated based on the series resistance (R s ) increase in TC testing rather than power drop to avoid the effect of confounding variables. Module-level R s increase in MSX 60 module after TC800 cycles and in M55 module after TC400 cycles was 1.22% and 183.7%, respectively. The E a determined for the module with 2wt% Ag is 0.24 eV and for that with the standard solder is 0.27 eV. The solder bond degradation seems to have been driven by TMF rather than IMC formation for both modules.

14 SOLAR ENERGY↗
DOE OSTI2024

Analyzing the impact of design factors on solar module thermomechanical durability using interpretable machine learning techniques

Solar modules in utility-scale systems are expected to maintain decades of lifetime to rival conventional energy sources. However, cyclic thermomechanical loading often degrades their long-term performance, highlighting the importance of effective design to mitigate thermal expansion mismatches between module materials. Given the complex composition of solar modules, isolating the impact of individual components on overall durability remains a challenging task. In this work, we analyze a comprehensive data set that comprises bill-of-materials (BOM) and thermal cycling power loss from 251 distinct module designs to identify the predominant design factors and their impacts on the thermomechanical durability of modules. The methodology of our analysis combines machine learning modeling (random forest) and Shapley additive explanation (SHAP) to correlate design factors with power loss and interpret the model’s decision-making. The interpretation reveals that silicon type (monocrystalline or polycrystalline), encapsulant thickness, busbar numbers, and wafer thickness predominantly influence the degradation. With lower power loss of around 0.6% on average in the SHAP analysis, monocrystalline cells present better durability than polycrystalline cells. This finding is further substantiated by statistical testing on our raw data set. The SHAP analysis also demonstrates that while thicker encapsulants lead to reduced power loss, further increasing their thickness over around 0.6 to 0.7 mm does not yield additional benefits, particularly for the front side one. In addition, other important BOM features such as the number of busbars are analyzed. This study provides a blueprint for utilizing explainable machine learning techniques in a complex material system and can potentially guide future research on optimizing the design of solar modules.

14 SOLAR ENERGY↗