Reverse Bias Characteristics of Thin BSF/BSR Silicon Solar Cells For Advanced Photovoltaic Solar Array
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With the proliferation of rooftop solar photovoltaic installations, there is a need to proactively predict consumer potential for solar photovoltaic adoption, for improved electric utility planning and operation. Traditional analytical modeling approaches are limited to a few survey features and a larger part of the survey would remain untouched by the decision model. This article presents a novel, data-driven modeling approach that strategically prunes a large set of consumer profile features using a machine learning framework to train a model for predicting potential solar adoption. The approach utilizes the Gradient Boosting Decision Tree model through a Light Gradient Boosting framework that improves significantly over the poor prediction accuracy of the existing approaches. Model training using focal-loss based supervision is used to overcome the difficulty in identifying the potential adopters that is inherent in conventional data-driven models. In addition, to overcome possible data sparsity in a limited survey sample, a Generative Adversarial Network is presented to create synthetic user samples and its effectiveness on model performance is assessed. A Bayesian optimization approach is used to systematically arrive at the hyperparameters of the proposed model. Validation of the presented approach on a survey data collected by the National Rural Electric Cooperative Association in Virginia in 2018 demonstrates the excellent predictive capability of the machine learning based approach to modeling solar adoption reliably.
Through funding provided by the U.S. Department of Energy, the National Renewable Energy Laboratory (NREL) has used subject matter experts to compile a set of checklists to help Puerto Rico and other communities prepare for storms. Renewable energy and distributed energy systems have the potential to provide power to neighborhoods, vulnerable residents, and certain facilities within a community, if those systems are designed to provide power during a grid disruption. The storm-hardening checklists provide storm preparation actions that can increase the chances that solar photovoltaic (PV) systems are available when communities need them most. This resource was translated from English to Spanish for greater accessibility.
Through funding provided by the U.S. Department of Energy, the National Renewable Energy Laboratory (NREL) has used subject matter experts to compile a set of checklists to help Puerto Rico and other communities prepare for storms. Renewable energy and distributed energy systems have the potential to provide power to neighborhoods, vulnerable residents, and certain facilities within a community, if those systems are designed to provide power during a grid disruption. The storm-hardening checklists provide storm preparation actions that can increase the chances that solar photovoltaic (PV) systems are available when communities need them most. This resource was translated from English to Spanish for greater acessibility.
Solar photovoltaic (PV)-rich power distribution systems are networked Cyber-Physical Systems (CPS). These are control systems where multiple computing nodes and diverse intelligent agents interact with the physical world in real-time. However, the presence of networked components renders them vulnerable to potential cyber-attacks, cyber-intrusions, and other malicious events. This is because these systems depend on the measurements reported from their heterogeneous sensors. This makes them vulnerable to potential cyber-attacks where malicious agents can compromise the sensors or the communication networks carrying the sensor measurements. This paper proposes a novel methodology for enhancing the cyber-security and cyber-resilient post-attack safe operation of solar PV-rich power distribution systems against potential cyber-attacks through the Dynamic Watermarking (DW), using online system identification. The resiliency of the proposed technique is tested and validated with several attack scenarios on both a lab-scale 3kW grid-connected PV inverter and a Hardware-in-the-Loop (HiL) system. The proposed approach can be applied to other types of power distribution systems to enhance their cyber-secure and cyber-resilient safe operation. This paper thereby contributes to the field of cyber-security of Cyber-Physical Energy Systems (CPES).
Further deployment of rooftop solar photovoltaics (PV) hinges on the reduction of soft (non-hardware) costs—now larger and more resistant to reductions than hardware costs. The largest portion of these soft costs is the expenses solar companies incur to acquire new customers. In this study, we demonstrate the value of a shift from significance-based methodologies to prediction-oriented models to better identify PV adopters and reduce soft costs. We employ machine learning to predict PV adopters and non-adopters, and compare its prediction performance with logistic regression, the dominant significance-based method in technology adoption studies. Our results show that machine learning substantially enhances adoption prediction performance: The true positive rate of predicting adopters increased from 66 to 87%, and the true negative rate of predicting non-adopters increased from 75 to 88%. We attribute the enhanced performance to complex variable interactions and nonlinear effects incorporated by machine learning. With more accurate predictions, machine learning is able to reduce customer acquisition costs by 15% ($0.07/Watt) and identify new market opportunities for solar companies to expand and diversify their customer bases. Our research methods and findings provide broader implications for the adoption of similar clean energy technologies and related policy challenges such as market growth and energy inequality.
There are growing needs to understand how extreme weather events impact the electrical grid. Renewable energy sources such as solar photovoltaics are expanding in use to help sustainably meet electricity demands. Wildfires and, notably, the widespread smoke resulting from them, are one such extreme event that can impair the performance of solar photovoltaics. However, isolating the impact that smoke has on photovoltaic energy production, separate from ambient conditions, can be difficult. In this work, we seek to understand and quantify the impacts of wildfire smoke on solar photovoltaic production within the Western United States. Our analysis focuses on the construction of a random forest regression model to predict overall solar photovoltaic production. The model is used to separate and quantify the impacts of wildfire smoke in particular. To do so, we fuse historical weather, solar photovoltaic energy production, and PM2.5 particulate matter (primary smoke pollutant) data to train and test our model. The additional weather data allows us to capture interactions between wildfire smoke and other ambient conditions, as well as to create a more powerful predictive model capable of better quantifying the impacts of wildfire smoke on its own. We find that solar PV energy production decreases 8.3% on average during high smoke days at PV sites as compared to similar conditions without smoke present. Finally, this work allows us to improve our understanding of the potential impact on photovoltaic-based energy production estimates due to wildfire events and can help inform grid and operational planning as solar photovoltaic penetration levels continue to grow.
Abstract Co‐locating solar photovoltaics with vegetation could provide a sustainable solution to meeting growing food and energy demands. However, studies quantifying multiple co‐benefits resulting from maintaining vegetation at utility‐scale solar power plants are limited. We monitored the microclimate, soil moisture, panel temperature, electricity generation and soil properties at a utility‐scale solar facility in a continental climate with different site management practices. The compounding effect of photovoltaic arrays and vegetation may homogenize soil moisture distribution and provide greater soil temperature buffer against extreme temperatures. The vegetated solar areas had significantly higher soil moisture, carbon, and other nutrients compared to bare solar areas. Agrivoltaics in agricultural areas with carbon debt can be an effective climate mitigation strategy along with revitalizing agricultural soils, generating income streams from fallow land, and providing pollinator habitats. However, the benefits of vegetation cooling effects on electricity generation are rather site‐specific and depend on the background climate and soil properties. Overall, our findings provide foundational data for site preservation along with targeting site‐specific co‐benefits, and for developing climate resilient and resource conserving agrivoltaic systems.
Solar energy is directly available on the sunward lunar surface. Most, if not all, the materials are available on the Moon to make silicon based solar photovoltaic cells. A few additional types are possible. There is a small but growing literature on production of lunar derived solar cells. This literature is reviewed. Topics explored include trade-offs of local production versus import of key materials, processing options, the scale and nature of production equipment, implications of storage requirements, and the end-uses of the energy. Directions for future research and demonstrations are indicated.
Solar photovoltaic (PV) resources are the most common form of distributed generation in residential and commercial customer premises within electric distribution networks. A higher penetration of PV generation in distribution circuits will impose challenges on maintaining service voltages within the range of industry standards, power quality, and power flow. Buildings consume 74% of the electricity produced in the United States, and a significant portion of the building load is dispatchable, making them responsive to electrical grid needs. Oak Ridge National Laboratory—in collaboration with Southern Company; the University of Tennessee, Knoxville; and the Georgia Institute of Technology—is examining the PV integration issues in distribution-level electrical grids and developing integrated demand-side control and communication systems to enable responsive loads. The proposed responsive loads mechanism performs renewable generation following to increase the penetration of solar PV within each feeder. The specific objectives of this project are to (1) examine distribution-level PV integration scenarios to understand requirements, (2) undertake an end-to-end simulation-based design of a distributed control strategy of loads geographically near the PV generation asset to minimize the effect on the distribution feeder, (3) deploy and demonstrate the control technology developed in partnership with utilities, and (4) perform a scalability analysis at the utility scale. This 3-year integrated project aims to develop, demonstrate, and validate demand-side control technology to enable increased the penetration of renewables while mitigating challenges that arise due to their intermittency. Activities in Budget Period (BP) 1 focused on a literature review and the formal design of a control system for integrating local distribution with generation and loads. The team used modeling and simulation to evaluate the impact of varying buildings loads, variable PV generation, and power flow dynamics on the distribution circuit. The dynamic models developed in BP 1 were used in BP 2 to develop a model-based control design and a test bed. The test bed has enabled the simulation-based testing and comparison of different control designs and formulations applied to different configurations of the distribution grid, PVs, and building loads. The control approaches developed in BP 2 were implemented in BP 3 in the form of hardware deployed at the Central Baptist Church (CBC) in Knoxville, Tennessee, for testing and evaluation. The outcome of this project was the development and demonstration of open-source, low-cost, low-touch sensing and control retrofits to distributed PV generation and building loads that, in a coordinated fashion, provide the load-shaping response needed to integrate high levels of renewable penetration. This research addresses the target metrics by dynamically controlling a load with solar generation variability to minimize the extent of two-way power flow, enhance reliability, facilitate high PV penetration (>100% of peak load in a line segment), and generate scalable software and hardware solutions adaptable to any penetration levels. The research and development activities are focused and designed to be impactful within the relevant 2020 targets time frame.An accurate open-source integration simulation framework for end-to-end control design was developed and deployed at the CBC facility for testing and evaluation. This final report provides a detailed review of the technical results achieved during this 3-year integrated project. A novel spectral analysis of PV data is demonstrated to derive the requirements of the control design. A detailed simulation-based analysis of PV integration at increasing penetration levels is presented using 1 year of PV data to demonstrate the impact on the distribution circuits. Two different control strategies were developed and demonstrated via simulation to track variable PV generation with adaptive load dispatch. The report concludes with a summary of accomplishments and recommendations for a path forward.
Large-area photovoltaic solar cells based on direct current magnetron sputter deposition of indium tin oxide (ITO) into single-crystal p-InP substrates demonstrated both the radiation hardness and high performance necessary for extraterrestrial applications. A small-scale production project was initiated in which approximately 50 ITO/InP cells are being produced. The procedures used in this small-scale production of 4-sq cm ITO/InP cells are presented and discussed. The discussion includes analyses of performance range of all available production cells, and device performance data of the best cells thus far produced. Additionally, processing experience gained from the production of these cells is discussed, indicating other issues that may be encountered when large-scale productions are begun.
This report outlines the effectiveness and economics of a ground source heat pump system installed together with solar photovoltaic panels and a battery storage system in a local community building situated in a cold climate. The community building is a tribal building located in Ruby, Alaska. Power from solar photovoltaic panels is supplemented by electricity purchased from the local community microgrid. A ground source heat pump was commissioned three years after the building was occupied, and supplements the heat generated by a boiler for both heat and domestic hot water. Data collected over the 2021-2022 heating season shows that the heat pump is providing heat to the building about 19% of the time with an average coefficient of performance of 2.68, ranging from below 2.5 in winter to above 3.0 in summer. The solar photovoltaic panels provided 4,700 kWh of power used within the building and an additional 2,900 kWh exported to the microgrid. The solar-produced power used within the building nearly offsets the estimated annual electrical draw of the heat pump of 5,700 kWh. Due to the very high costs for shipping and installation to remote locations, projects such as this, while not economically feasible if self-funded, can save the community an estimated $76,051 over a period of 20-years. Fuel prices increasing by more than 25%, or subsidized electricity prices decreasing by more than 25%, make the ground source heat pump a more viable option economically. Unsubsidized electricity prices increasing by 25% or more make a solar photovoltaic system with battery storage nearly economically viable after a 20-year period for commercial or school buildings that are not eligible for Alaska's Power Cost Equalization program.
In photovoltaic systems, the encapsulant material that protects the solar cells should be highly transparent and very durable. Glass satisfies these two criteria and is considered a primary candidate for low-cost, photovoltaic encapsulation systems. In this paper, various aspects of glass encapsulation are treated that are important for the designer of photovoltaic systems. Candidate glasses and available information defining the state of the art of glass encapsulation materials and processes for automated, high volume production of terrestrial photovoltaic devices and related applications are presented. The desired characteristics of glass encapsulation are (1) low degradation rates, (2) high transmittance, (3) high reliability, (4) low-cost, and (5) high annual production capacity. The glass design areas treated herein include the types of glass, sources, prices, physical properties and glass modifications, such as antireflection coatings.
Large scale solar farms supply an increasing amount of the worlds electricity supply. However, high operation temperatures can strongly reduce efficiency and panel lifetime, negatively affecting the levelized cost of energy. In this work, the convective heat transfer coefficient for a utility-scale solar farm is studied with combined thermal and particle-image-velocimetry measurements in a scaled wind tunnel experiment. The measurements confirm the applicability of the scaled experimental setup to study large solar arrays. The velocity measurements indicate the complex flow structure within the solar array, governed by wakes directed upwards due to the orientation of the solar panels.
The rapid growth of rooftop solar photovoltaic systems can pose a number of financial challenges for electric utility shareholders and their customers. One potential pathway to resolving these perceived challenges involves allowing utilities to own and operate rooftop solar systems. However, the financial benefits and costs of this business model are not well understood. Here we model the financial performance of a large-scale utility-owned residential rooftop solar programme. Furthermore, over a 20 yr period, the programme increases shareholder earnings by 2–5% relative to a no-solar scenario, compared to a 2% earnings loss when an equivalent amount of rooftop solar is instead owned by non-utility parties. Such a programme could therefore be attractive from the perspective of utility investors. The impacts on utility customers, however, are more mixed, with average bills of non-solar customers increasing by 1–3% compared to the no-solar scenario, similar to the 2% increase under traditional, non-utility-ownership structures.
The Space Station Freedom photovoltaic solar array blanket assembly is comprised of several layers of materials having dissimilar elastic, thermal, and mechanical properties. The operating temperature of the solar array, which ranges from -75 to +60 C, along with the material incompatibility of the blanket assembly components combine to cause an elastic-plastic stress in the weld points of the assembly. The weld points are secondary structures in nature, merely serving as electrical junctions for gathering the current. The thermal mechanical loading of the blanket assembly operating in low earth orbit continually changes throughout each 90 min orbit, which raises the possibility of fatigue induced failure. A series of structural analyses were performed in an attempt to predict the fatigue life of the solar cell in the Space Station Freedom photovoltaic array blanket. A nonlinear elastic-plastic MSC/NASTRAN analysis followed by a fatigue calculation indicated a fatigue life of 92,000 to 160,000 cycles for the solar cell weld tabs. Additional analyses predict a permanent buckling phenomenon in the copper interconnect after the first loading cycle. This should reduce or eliminate the pulling of the copper interconnect on the joint where it is welded to the silicon solar cell. It is concluded that the actual fatigue life of the solar array blanket assembly should be significantly higher than the calculated 92,000 cycles, and thus the program requirement of 87,500 cycles (orbits) will be met. Another important conclusion that can be drawn from the overall analysis is that, the strain results obtained from the MSC/NASTRAN nonlinear module are accurate to use for low-cycle fatigue analysis, since both thermal cycle testing of solar cells and analysis have shown higher fatigue life than the minimum program requirement of 87,500 cycles.
Floating solar photovoltaic (FPV) installations are increasing globally. However, their interaction with the hosting water body and implications for ecosystem function is poorly understood. Understanding potential impacts is critical as water bodies provide many ecosystem services on which humans rely and are integral for delivering the United Nations Sustainable Development Goals (SDGs). Here, we used scientific evidence from a systematic review and stakeholder expertise, captured through an international survey and a workshop, alongside existing understanding of the role of water bodies in delivering ecosystem services and the SDGs. We found 22 evidence outcomes that indicated potential physical, chemical and biological impacts of FPV on water bodies. Assessment by stakeholders from across sectors indicated that reduced water evaporation is the greatest opportunity, whilst changes to water chemistry, including nitrification and deoxygenation, are the greatest threat. Despite these findings, FPV operators reported no observed water quality or ecosystem impacts. However, only 15% of respondents had performed water quality analysis; visual inspection alone cannot ascertain all water quality impacts. Based on the integration of these findings, we determined that FPV could impact nine ecosystem services. Furthermore, established linkages between ecosystem services and SDGs indicate the potential for impacts on eight SDGs, although whether the impact is positive or negative is likely to depend on FPV design and water body type. Our results further the understanding of the effects of FPVs on host water bodies and may help to ensure the anticipated growth in FPVs minimises threats and maximises opportunities, safeguarding overall sustainability.
The Advanced Photovoltaic Solar Array (APSA) program seeks to bring to flight readiness a solar array that effectively doubles the specific power of the Solar Array Flight Experiment/Solar Electric Propulsion (SAFE/SEP) design that was successfully demonstrated during the Shuttle 41-D mission. APSA is a critical intermediate milestone in the effort to demonstrate solar array technologies capable of 300 W/kg and 300 W/square m at beginning of life (BOL). It is not unreasonable to anticipate the development of solar array designs capable of 300 W/kg at BOL for operational power levels approx. greater than 25 kW sub e. It is also quite reasonable to expect that high performance solar arrays capable of providing at least 200 W/kg at end of life for most orbits now being considered by mission planners will be realized in the next decade.