StatisticalClearSky
Statistical estimation of a clear sky signal from PV system power data.
Meyers, Bennet↗
SEARCH · Engineering Papers
Results for “PV systems”
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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PVAnalytics [SWR-20-33]
PVAnalytics inputs long term (multiple years) photovoltaic (PV) outdoor sensor data and completes autonomous quality assurance (QA) analysis on the data. Time series data from individual sensors such as electric meters, inverter outputs, irradiance, and temperature sensors are compared to physically expected outputs of these sensors under clear sky conditions based on the latitude, longitude, and orientation of the PV system. PVAnalytics outputs as QA pass or fail for each sensor, reasons determined for failure, time series flags identifying QA thresholds have been exceeded, flags for sunny days, the time zone found for the given sensor, sensor drift, time periods for passing data, and results of orientation checks.
Muller, Matthew↗
PVWatts Calculator [Slides]
This slide deck is an introduction to PV Watts and how users can employ it. NREL's PVWatts® Calculator estimates the energy production of grid-connected photovoltaic (PV) energy systems.
14 SOLAR ENERGY↗
Overcoming Communications Outages in Inverter Downtime Analysis: Preprint
Inverters are often reported to be the highest-impact failure point in PV systems. This importance is belied by the simplistic assumptions about inverter downtime losses used in industrial energy modeling. Energy models often assume the extent of inverter-related losses is limited to 1% of annual production from scheduled inverter preventative maintenance. In reality, inverter-related production losses are much more variable, although data from large-scale surveys of fielded systems are rare. Here we present a method of detecting inverter downtime events and estimating the associated lost production using inverter- and meter-level power data. Because communications outages are of similar frequency to true production outages, the method pays particular attention to distinguishing communications outages from true production outages. The results of applying the method at fleet-scale are presented and discussed.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Maximizing Yield with Improved Single-Axis Backtracking on Cross-Axis Slopes: Preprint
Many tracking PV systems avoid row-to-row shading by backtracking in morning and afternoon. Because the common single-axis backtracking implementation assumes the array is in a horizontal plane, it does not fully prevent row-to-row shading in arrays installed on cross-axis slopes. This paper simulates the effect of reduced self-shading on annual production with two modified backtracking strategies. Depending on the array geometry and the steepness of the cross-axis grade, using a modified backtracking strategy improved annual yield by 3-10%.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Clear Sky Irradiance Year-to-Year Variations and Trends: Preprint
Solar radiation data for seven SURFRAD stations for the period 1996-2018 show an increase in irradiance under clear skies over time, with an interannual variability of the increase. A popular implementation of a clear sky model provided modeled irradiances for comparison with the SURFRAD data under conditions screened for clear skies. The use of the clear sky model, which did not consider year-to-year variances in atmospheric turbidity, was found problematic for resolving small changes in irradiance important for determining PV system degradation over three or five years, but less problematic for a period of ten years.
41 EE - Solar Energy Technologies Office (EE-4S)↗
Power Quality and Stability in a Cluster of Microgrids with Coordinated Power and Energy Management
Distributed Energy Resources (DER) such as photovoltaic (PV) systems and battery energy storage systems (BESS) can be operated collectively as microgrids. Microgrids can be effective in meeting local load requirements as well as in improving the power quality and stability in a modern power distribution system. Multiple microgrids can also be operated in a coordinated manner as a cluster, to improve the resiliency of the power distribution system. In the event of an outage caused by a transmission system failure, the microgrids in a cluster can use their distributed generation capacity and energy storage resources to recover and extend power availability to the critical loads in the system. In this paper, an illustrative cluster of two microgrids based on the IEEE 13-bus model with DERs has been used, to demonstrate the operation of the concept using real-time simulations. Using real-time simulations, capabilities such as switching reconfiguration following faults, identification of optimal DER placement for effective power quality management, and power electronic controller validation have been demonstrated. For the two-microgrid cluster, this paper also presents improvements in load voltage quality and
Chowdhury, Prithwiraj Roy↗
Houston, We Have a Problem—and a Solution: Solar Performance Initiative Helps Federal Building Boost Rooftop Production
Fact sheet describes evaluating and optimizing a PV system on the Mickey Leland Federal Building.
photovoltaic system, renewable energy, distributed↗
ADMS Test Bed Updates
This webinar will present results from a joint project with utility partner Xcel Energy in which we evaluated their ADMS application for volt-var optimization using different levels of model quality. We were able to help Xcel Energy understand the trade-offs of telemetry measurements and model quality when managing a feeder's voltage profile to maximize energy conservation. We simulated scenarios with varying levels of model quality and measurement density to evaluate Xcel's options for best using its ADMS. The results help Xcel and other utilities understand how network data affects voltage management as grid operations see continued growth in solar photovoltaic (PV) systems and electric vehicles (EVs).
ADMS↗
Verifying status of a termination end cap of a microinverter chain
A photovoltaic (PV) system includes a system control module that determines the presence of a microinverter chain termination end cap. The end cap includes an embedded circuit. The embedded circuit includes components having resistive, reactive, or impedance values. A signal source provides a signal through the microinverter chain. A parameter for a sensed signal detected by the system control module is used to determine the presence of the end cap using a change between the parameter for the sense signal and a reference parameter.
Chapman, Patrick L.↗
Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells
Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.
14 SOLAR ENERGY↗
DOE Zero Energy Ready Home Case Study HIA 2020: Hartford Area Habitat for Humanity, Roosevelt Zero Energy Home, Hartford, CT
Case study of a DOE 2020 Housing Innovation Award winning custom home in a cold climate that got a HERS -7 with PV, with 1,280 square feet, ENERGY STAR certified appliances, no-VOC products, and a 7.75-kW PV system.
Building America↗
Physiochemical Machine Learning Models Predict Operational Lifetimes of CH3NH3PbI3 Perovskite Solar Cells
Halide perovskites are promising photovoltaic (PV) materials with the potential to lower the cost of electricity and greatly expand the penetration of PV if they can demonstrate long-term stability under illumination in the presence of moisture and oxygen. The solar cell service lifetime as quantified by the T80 (the time required for the power conversion efficiency to drop to 80% of its starting value) is a useful metric to assess stability. The T80 for utility, commercial, or residential PV systems needs to be several decades in order to yield low-cost electricity, and thus it is not practical to directly measure the T80. It would be useful if T80 could be predicted from the initial dynamics of a solar cell’s performance, but until now no models have been developed to forecast T80. In this work, we report the development of machine learning models to predict T80 of ITO/NiOx/CH3NH3PbI3/C60/BCP/Ag solar cells operating at maximum power point under 1-sun equivalent photon flux in air at varying temperatures and relative humidities. Efficiency losses are driven by short-circuit current and fill factor, indicating that chemical decomposition of the perovskite is a major contributor to degradation. Spatial patterns evident from in situ dark field optical microscopy suggest that the electric field gradient at device edges plays a significant role in perovskite decomposition, along with photochemical reactions with O2 and H2O. Models are trained using a menu of features from three distinct categories: (i) features based on measurements of the initial rates of change of device parameters, (ii) features based on the ambient conditions during operation (temperature, & partial pressure of H2O), and (iii) features based on underlying physics and chemistry. We show that a theory-based physiochemical feature derived from a model of the chemical reaction kinetics of the rate of degradation of the CH3NH3PbI3 is particularly valuable for prediction. This physiochemical feature was selected as the first or second most dominant feature in the best performing models. With a dataset consisting of 45 accelerated degradation experiments with T80 that range over a factor of 30, the model predicts T80 with an accuracy of about 40% (|predicted T80 - observed T80| / observed T80) on samples not used in training. This hybrid ML approach should be effective when applied to other compositions, device architectures, and advanced packaging schemes.
14 SOLAR ENERGY↗
Community Solar
Community solar, also known as shared solar or solar gardens, is a distributed solar energy deployment model that allows customers to buy or lease part of a larger, offsite shared solar photovoltaic (PV) system and receive benefits of their participation.
achetypes of ownership↗
Hypothetical Solar Cyberattack Scenarios and Impacts
This report offers approachable, plausible scenarios of cyberattacks affecting photovoltaic assets and interconnected infrastructure. It may be used by State Energy Officials and Public Utility Commission Staff to educate themselves on the potential consequences of these scenarios and the practical, high-level actions that may be implemented now to mitigate future impacts. It highlights plausible consequences of inadequate cyber provisions for PV systems and offers potential state actions to alleviate the identified risks.
14 SOLAR ENERGY↗
Photovoltaic Reliability Workshop (PVRW) 2024 Proceedings: Posters
NREL hosts an annual Photovoltaic Reliability Workshop (PVRW) so that solar technology experts can discuss current and future issues in PV reliability. Longer-lasting PV systems make lower-cost solar electricity and have less impact on the environment, human health, and natural resources. PVRW offers a combination of oral presentations, panel discussions with vigorous audience participation, and poster presentations. This document collects posters that were presented at PVRW 2024.
degradation↗
Photovoltaic Reliability Workshop (PVRW) 2024 Proceedings: Presentations
NREL hosts an annual Photovoltaic Reliability Workshop (PVRW) so that solar technology experts can discuss current and future issues in PV reliability. Longer-lasting PV systems make lower-cost solar electricity and have less impact on the environment, human health, and natural resources. PVRW offers a combination of oral presentations, panel discussions with vigorous audience participation, and poster presentations. This document collects oral presentations that were presented at PVRW 2024.
degradation↗
Comparative PV LCOE Calculator (Comparative Photovoltaic Levelized Cost of Energy Calculator) [SWR-20-86]
The calculator calculates levelized cost of energy (LCOE) for photovoltaic (PV) systems based on cost, performance, and reliability inputs for a baseline and a proposed technology. The user can choose inputs and watch the effect on LCOE to determine whether a proposed technology is cost-effective, perform trade-off analysis among different technology options, and do break-even analysis of cost or performance.
Silverman, Timothy↗