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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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Junctions formed from materials that contain mobile charged species and fixed counterions can assist in photo-induced charge separation and lead to photovoltaic action, irrespective of whether the mobile charges are electronic or protonic.
Distributed photovoltaic (PV) generation often occurs ``behind the meter": a grid operator can only observe the net load, which is the sum of the gross load and distributed PV generation. This lack of observability poses a challenge to system operation at both bulk level and distribution level. The lack of real-time or near-future disaggregated estimates of gross load and PV generation will lead to over scheduling of energy production and regulation reserves, reliability constraints violations, wear and tear of controller devices, and potentially cascading failures of a system. In this paper we propose the use of a Bayesian Structural Time Series (BSTS) model with local solar irradiance measurements to disaggregate the summed PV generation and gross load signals at a downstream measurement site. BSTSs are a highly expressive model class that blends classic time series models with the powerful Bayesian state space estimation framework. Disaggregation is done probabilistically, which automatically quantifies the uncertainties of the estimated PV generation and gross load consumption. Depending on the data availability in real-time, it can be used to disaggragate PV and gross load at customer site, or can be used at the feeder level. In this paper, we focus on solving the problem at feeder level. We compare the performance of a BSTS model as well as a handful of state-of-the-art methods on a Pecan Street AMI dataset, using the National Solar Radiation Database (NSRDB) to estimate local irradiance.
Proposed are photovoltaic system tests and demonstrations covering a wide range of applications in order to develop low cost photovoltaic cells suitable for terrestrial applications. Program objectives are: (1) tests and model system demonstrations; (2) device performance and diagnostics; and (3) endurance of solar cell modules and arrays.
Quality assurance criteria are described for manufacturers and installers of solar photovoltaic tests and applications. Quality oriented activities are outlined to be pursued by the contractor/subcontractor to assure the physical and operational quality of equipment produced is included. In the broad sense, guidelines are provided for establishing a QA organization if none exists. Mainly, criteria is provided to be considered in any PV quality assurance plan selected as appropriate by the responsible Field Center. A framework is established for a systematic approach to ensure that photovoltaic tests and applications are constructed in a timely and cost effective manner.
Guidelines are provided for the organization, preparation and conduct of program reviews for photovoltaic applications. These criteria apply to all photovoltaic applications. They include, but are not limited to, the following: (1) Conceptual design review; (2) Preliminary design review; (3) Critical design review; and (4) Operational readiness review. Contract goal's and objectives are met through program monitoring for cost, schedule, and performance for both managerial and technical elements for all PV applications activities.
The criteria is defined for auditing photovoltaic system applications and experiments. The purpose of the audit is twofold: to see if the application is meeting its stated objectives and to measure the application's progress in terms of the National Photovoltaic Program's goals of performance, cost, reliability, safety, and socio-environmental acceptance. The information obtained from an audit will be used to assess the status of an application and to provide the Department of Energy with recommendations on the future conduct of the application. Those aspects are covered of a site audit necessary to produce a systematic method for the gathering of qualitative and quantitative data to measure the success of an application. A sequence of audit events and guidelines for obtaining the required information is presented.
Accurate prediction of the performance of solar arrays in space requires that the cells be tested in comparison with a space-flown standard. Recognizing that improvements in future solar cell technology will require an ever-increasing fidelity of standards, the Photovoltaics and Space Environment Branch at the NASA Glenn Research Center, in collaboration with the Ohio Aerospace Institute, designed a prototype facility to allow routine calibration, measurement, and qualification of solar cells on the International Space Station, and then the return of the cells to Earth for laboratory use. For solar cell testing, the Photovoltaic Engineering Testbed (PET) site provides a true air-mass-zero (AM0) solar spectrum. This allows solar cells to be accurately calibrated using the full spectrum of the Sun.
Extracting accurate soiling loss information from photovoltaic (PV) production data first requires segmenting the time series data per natural or manually occurring cleaning events. Maintenance logs are often incomplete, rain data are often unavailable, and the debate on rain thresholds for cleaning and dew or wind cleanings is still ongoing. The present work aims to overtake these issues by improving automated methods to detect these cleaning events and therefore improve extraction of soiling loss information. Time series power production data from 22 PV inverters were labeled for natural or manually occurring cleaning events. The data sets were carefully selected to include varying degrees of soiling, cleaning events, and noise. Several algorithms, including filtering logic and change point detection, were examined for efficacy at detecting the labeled cleanings. All the methods introduced except for changepoint detection showed significant improvement at detecting the labeled cleaning events per the mean F 1 score. Furthermore, the highest performing cleaning detection algorithm achieved an absolute increase in the mean F 1 score of 43% over the default version of the RdTools stochastic rate and recovery (SRR) algorithm. The highest performing algorithm included irradiance filtering and a cleaning detection threshold, adjusted based on the 40-day centered rolling median of the absolute day-to-day deviations in the daily performance index (PI). Furthermore, these improvements are promising as cleaning detection is an essential step in the automated analysis of PV soiling.
Abstract Fault detection and classification in photovoltaic (PV) systems through real‐time monitoring is a fundamental task that ensures quality of operation and significantly improves the performance and reliability of operating systems. Different statistical and comparative approaches have already been proposed in the literature for fault detection; however, accurate classification of fault and loss incidents based on PV performance time series remains a key challenge. Failure diagnosis and trend‐based performance loss routines were developed in this work for detecting PV underperformance and accurately identifying the different fault types and loss mechanisms. The proposed routines focus mainly on the differentiation of failures (e.g., inverter faults) from irreversible (e.g., degradation) and reversible (e.g., snow and soiling) performance loss factors based on statistical analysis. The proposed routines were benchmarked using historical inverter data obtained from a 1.8 MWp PV power plant. The results demonstrated the effectiveness of the routines for detecting failures and loss mechanisms and the capability of the pipeline for distinguishing underperformance issues using anomaly detection and change‐point (CP) models. Finally, a CP model was used to extract significant changes in time series data, to detect soiling and cleaning events and to estimate both the performance loss and degradation rates of fielded PV systems.
InGaAs heterostructures have demonstrated the means to maintain hot carrier behavior under practical operating conditions via valley photovoltaics. However, enhancing the extraction to create a fully operational hot carrier solar cell requires an enhanced understanding of the barriers inside such devices. A set of four InGaAs heterostructures altering the n + top layer of the structure has been produced to enable comparative analysis of the current density-voltage characterization and the relationship to the band and valley alignments.
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Photovoltaic (PV) modules on building rooftops provide shade from summer heating, leading to a reduction in cooling load during hot seasons. However, PV shading also reduces passive solar heating during winter months, leading to an increase in the building heating load during cold seasons. In this study, the heat transfer performance of an enclosure formed by adjacent PV modules is analyzed for three locations in the United States, comparing the daily heat flux through rooftops for the case of a PV-shaded rooftop as well as an unshaded roof. Several result metrics are developed as part of this work, including the saved energy load (SEL), or the energy conserved by adding PV-shading to the rooftop, and the additional energy load (AEL), or the supplemental building energy required to replace shaded solar heating. Lastly, this work calculates the utility factor, being the ratio of SEL and PV power output to AEL as a metric of PV effectiveness. SELs are 5.2, 6.2, and 11.7 kWh/m 2 · year for Dayton, Boise, and Phoenix, respectively, while the AELs for the same locations are 1.6, 1.5, and 2.1 kWh/m 2 · year. The utility factors for the same three locations are 61, 71, and 79. In general, locations with hot, non-cloudy summers and clear skies in winter see the largest utility factor. Further, it is shown that PV shading can conserve building energy during the winter months by preventing radiative losses to cold winter skies.
This slide deck is an appendix to a paper series that examines potential challenges related to planning future power systems with higher solar photovoltaic (PV) penetrations. The series uses the western U.S. power system for these investigations because it is a region the authors and their colleagues have already extensively studied. We are therefore well-suited to analyze even higher PV penetrations and then examine the results in multiple models to determine whether our current approaches are missing key details that only emerge at higher PV penetrations. This deck details the systems underlying those analyses and how they were modeled using the Resource Planning Model (RPM), a capacity expansion modeling tool. We examine both Western Interconnection-wide and regional results for three regions in the Western U.S. with significantly different existing power systems and connections to neighboring regions; this provides a more balanced picture as to how power systems with high PV penetration might emerge in different contexts and what the resulting grid challenges, if any, might be.
This report presents a performance analysis of 75 photovoltaic systems based on PV system production data collected as part of a FEMP Federal PV Performance Assessment project combined with co-incident insolation, and ambient temperature to analyze how actual performance compares with a performance model. FEMP collaborated with 17 Federal agencies and sub-agencies to collect the information required to analyze the performance of each system. The systems represent a total capacity of 30,714 kW and range in size from 1 kW to 4,043 kW, with an average size of 410 kW, and were installed between 2011 and 2020. The data is analyzed for Key Performance Indicators, Availability, Performance Ratio and Energy Ratio by comparing the measured production data to model production data. The System Advisor Model (SAM) combines a description of the system (such as inverter capacity, de-rating for temperature, balance-of-system efficiency) with environmental parameters (coincident solar and temperature data) to calculate predicted performance. The performance metrics are calculated by lining up the measured production data with the model estimate on an hour-by-hour, day-by-day, or month-by-month basis (depending on the interval resolution of the production data). A report with system description, photo of the system, special assumptions made for the site, graph of measured production and model production, table of key performance indicators, and links to O&M resources that might improve performance was produced and delivered to site and agency staff with a short on-line briefing.
Conference on radiation resistance and damage in silicon solar cells and photovoltaic devices