Engineering Papers⌕ Search

SEARCH · Engineering Papers

Results for “solar power clipping”

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.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

16 records
All sourcesNASA NTRSDOE OSTI
DOE OSTI2020

Probabilistic Solar Power Forecasting Using Bayesian Model Averaging

There is rising interest in probabilistic forecasting to mitigate risks from solar power uncertainty, but the numerical weather prediction (NWP) ensembles readily available to system operators are often biased and underdispersed. We propose a Bayesian model averaging (BMA) post-processing method suitable for forecasting power from utility-scale photovoltaic (PV) plants at multiple time horizons up to at least the day-ahead timescale. BMA is a kernel dressing technique for NWP ensembles in which the forecast is a weighted sum of member-specific probability density functions. We tailor BMA for utility-scale PV forecasting by modeling power clipping at the AC inverter rating and advance the theory of BMA with a new beta kernel parameterization that accommodates theoretical constraints not previously addressed. BMA is demonstrated for a case study of 11 utility-scale PV plants in Texas, forecasting at hourly resolution for the complete year 2018. BMA's mixture-model approach mitigates underdispersion of the raw ensemble to significantly improve forecast calibration, while consistently outperforming an ensemble model output statistics (EMOS) parametric approach from the literature. At 4-hour lead time, the BMA post-processing achieves continuous ranked probability skill scores of 2--36% over the raw ensemble, with consistent performance at multiple lead times suitable for power system operations.

14 SOLAR ENERGY↗
DOE OSTI2023

Toward a Diverse Next-Generation Energy Workforce: Teaching Artificial Photosynthesis and Electrochemistry in Elementary Schools through Active Learning

Artificial photosynthesis is a promising approach to generate commodity chemicals using abundant chemical feedstocks and renewable energy sources. Despite its importance, affordable and effective hands-on classroom activities that demonstrate artificial photosynthesis and teach key concepts, especially for primary school students, are lacking. Educating young students on this topic is a critical step in the development of the next-generation energy workforce, especially one that is diverse in race and gender. Here, we hypothesize that an effective approach to educate a broad range of young students on the topic of artificial photosynthesis is through the use of an active learning-based lesson plan that employs cheap and accessible materials. This hypothesis is confirmed by evaluating the understanding of fifth grade students with a survey before and after a lesson plan on artificial photosynthesis that uses active-learning techniques and uses safe and highly accessible materials (baking soda, tap water, plastic jars, Ni coil, alligator clips, and a solar cell) to perform solar-powered water splitting. The lesson plan and survey questions are designed to align with the educational outcomes for fifth grade classrooms in California and to address four general learning objectives: (1) Motivations of Artificial Photosynthesis, (2) Applications of Artificial Photosynthesis, (3) Inputs and Outputs of Artificial Photosynthesis, and (4) Engineering Design for Artificial Photosynthesis. The survey data demonstrate a statistically significant improvement in overall student understanding from the lesson plan. Importantly, the data show that the lesson plan presented here is effective at narrowing the performance gap between minority students and overly represented groups.

37 INORGANIC, ORGANIC, PHYSICAL, AND ANALYTICAL CH↗
DOE OSTI2021

Performance Comparison of Clipping Detection Techniques in AC Power Time Series

In this research, a variety of methods were developed to detect clipping periods in AC power time series. AC power data streams associated with 36 unique systems across the United States were collected, and data points representing clipping periods were manually labeled by experts. Using this data set for training and validation, novel logic-based and machine learning (ML) approaches were developed to classify time series values as clipping or non-clipping. These approaches were compared to the RdTools method for detecting clipping periods. The logic-based and ML XGBoost approaches achieved F-scores of 85.0 and 77.6, respectively, when cross-validated against the manually labeled data, as compared to the current RdTools approach (F-score of 56.4), indicating a significant improvement at detecting clipping periods. Additionally, the effects of each clipping filter when evaluating system degradation rates were assessed, using 31 unique systems across the United States. Results indicate that estimated system degradation rate can vary based on the type of clipping filter used, by up to 0.6% degradation rate for some cases.

clipping↗
DOE OSTI2021

Validation of Subhourly Clipping Loss Error Corrections

Under-performance of solar PV systems is an important issue that increases risks for stakeholders, including developers, investors and operators. Recently some attention has focused on underestimation of inverter clipping losses as a possible source of over-prediction where sub-hourly solar variability is high. Several models and data sets have been analyzed over the past few years, with the aim of quantifying, predicting, and correcting underestimated clipping loss errors for systems with high DC/AC ratio and solar variability. In this research, we apply a machine learning model developed at NREL to two physical PV systems, to correct for subhourly clipping losses. For each system, we compare overall AC power output for the model taken at 1-minute intervals to AC power output taken at 1-hour intervals with the addition of the subhourly clipping correction. Our findings consistently show that the addition of the clipping loss correction lead to a reduction in mean bias error of 0.8% and 1.2% for systems A and B, respectively, with no additional filtering applied. When examining high solar variability periods where clipping is more pronounced, system A and B experienced a 1.8% and 2.7% reduction in mean bias error, respectively, when the clipping correction was applied.

machine learning↗
DOE OSTI2021

Validation of Subhourly Clipping Loss Error Corrections: Preprint

Under-performance of solar PV systems is an important issue that increases risks for stakeholders, including developers, investors and operators. Recently some attention has focused on underestimation of inverter clipping losses as a possible source of over-prediction where sub-hourly solar variability is high. Several models and data sets have been analyzed over the past few years, with the aim of quantifying, predicting, and correcting underestimated clipping loss errors for systems with high DC/AC ratio and solar variability. In this research, we apply a machine learning model developed at NREL to two physical PV systems, to correct for subhourly clipping losses. For each system, we compare overall AC power output for the model taken at 1-minute intervals to AC power output taken at 1-hour intervals with the addition of the subhourly clipping correction. Our findings consistently show that the addition of the clipping loss correction lead to a reduction in mean bias error of 0.6\% and 1.1\% for systems A and B, respectively, with no additional filtering applied. When examining high solar variability periods where clipping is more pronounced, system A and B experienced a 1.4\% and 2.5\% reduction in mean bias error, respectively, when the clipping correction was applied.

machine learning↗
DOE OSTI2023

A Reproducible Validation of Algorithms for Estimating Array Tilt and Azimuth from Photovoltaic Power Time Series

In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real- world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5- based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.

azimuth↗
DOE OSTI2023

A Reproducible Validation of Algorithms for Estimating Array Tilt and Azimuth from Photovoltaic Power Time Series

In this research, we assess the viability of four different, publicly available algorithms for estimating the azimuth and tilt parameters of solar photovoltaic systems using only the associated AC power time series data and site latitude-longitude coordinates. In this work, we curated a benchmarking data set of 44 fixed-tilt systems, comprising 275 measured AC power inverter data streams, with known azimuth and tilt parameters. Additionally, we isolated test cases in the data set with real-world issues, including shading and clipping, to determine how algorithm performance varies based on the presence of these phenomena. Using this data set for benchmarking, we evaluated the estimated vs. actual system characteristics for each algorithm, as well as the associated algorithm execution time using a standardized benchmarking process. The two highest performing algorithms were the Solar Data Tools and the PVWatts 5-based methods, which both achieved a median absolute error of approximately 5 and 1 degrees for azimuth and tilt, respectively. During run time analysis, the SDT method was approximately 5 times faster than the PVWatts 5-based method, with the median execution time for a stream varying between 6 and 8 seconds vs. a median run time of 31 seconds for the PVWatts 5-based method.

algorithm validation↗
DOE OSTI2022

The Effect of Inverter Loading Ratio on Energy Estimate Bias: Preprint

Subhourly effects, particularly variability in solar irradiance, can lead to underestimation of inverter clipping losses and overestimation of energy in hourly photovoltaic system performance models, particularly for systems with high inverter loading ratios. Direct simulation of this error can be complicated by factors such as the representation of spatial and temporal variability in hourly weather data and transient system conditions. In this work we take an alternative approach using real system power measurements to show that energy predictions from typical industry models suffer from a bias that increases with inverter loading ratio. We also show that this loading ratio-dependent bias is strongly correlated with an empirical subhourly inverter clipping bias derived from real power plant data. Finally, we show that this bias is not necessarily specific to any one model or weather dataset by recreating similar biases with alternatives of each.

clipping↗
DOE OSTI2022

The Roles and Impacts of PV-Battery Hybrids in a Decarbonized U.S. Electricity Supply

In this paper, we explore the potential impacts of growing industry interest in hybrid systems comprising PV and battery technologies on the results and findings of the Solar Futures Study (DOE 2021). We employ similar scenario definitions in the same ReEDS capacity expansion model, but we perform two versions of each scenario: one in which PV and battery technologies must be deployed separately (No Hybrids), and one in which the model has the option of deploying them together as PVB hybrids (With Hybrids). By comparing the No Hybrids and With Hybrids versions of each scenario, we isolate the impacts of hybridization on the outcomes and findings of the Solar Futures Study. We find that PVB hybrid configurations capture a sizable share of PV deployment, and the highest-net-value PVB hybrid configuration depends strongly on policy conditions. A power sector decarbonization policy generally increases the value proposition of a more forward-looking PVB hybrid configuration that involves significant oversizing of the PV arrays, a larger battery (which facilitates greater recovery and utilization of otherwise clipped energy), and a higher capacity factor. The growing deployment of PVB hybrid configurations primarily displaces standalone PV capacity, such that total installed PV capacity is largely unaffected by the availability of PVB hybrid configurations. However, the higher capacity factors associated with PVB hybrid configurations drive a modest (1-2 percentage point) increase in PV's share of U.S. electricity supply in 2050. Finally, introducing the PVB hybrid configurations influences the future role and makeup of battery storage technologies, and it reduces the required transmission expansion, particularly under scenarios that involve a power sector decarbonization policy.

14 SOLAR ENERGY↗
NASA NTRS1999

Clustering of the Diffuse Infrared Light from the COBE DIRBE Maps: Power Spectrum Analysis and Excess Isotropic Component of Fluctuations - 3

The cosmic infrared background (CIB) radiation is the cosmic repository for energy release throughout the history of the universe. The spatial fluctuations of the CIB resulting from galaxy clustering are expected to be at least a few percent on scales of a degree, depending on the luminosity and clustering history of the early universe. Using the all-sky data from the COBE DIRBE instrument at wavelengths 1.25 - 100 microns we attempt to measure the CIB fluctuations. In the near-IR, foreground emission is dominated by small scale structure due to stars in the Galaxy. There we find a strong correlation between the amplitude of the fluctuations and Galactic latitude after removing bright foreground stars. Using data outside the Galactic plane (absolute value of b > 20 deg) and away from the center (90 deg < l < 270 deg) we extrapolate the amplitude of the fluctuations to cosec absolute value of b = 0. We find a positive intercept of delta.F(sub rms) = 15.5(sup +3.7, sub -7.0), 5.9(sup +1.6, sub -3.7), 2.4(sup +0.5, sub -0.9), 2.0(sup +0.25, sub -0.5) nW/sq m.sr at 1.25, 2.2, 3.5 and 4.9 microns respectively, where the errors are the range of 92% confidence limits. For color subtracted maps between band 1 and 2 we find the isotropic part of the fluctuations at 7.6(sup +1.2, sub -2.4) nW/sq m.sr. Based on detailed numerical and analytic models, this residual is not likely to originate from the Galaxy, our clipping algorithm, or instrumental noise. We demonstrate that the residuals from the fit used in the extrapolation are distributed isotropically and suggest that this extra variance may result from structure in the CIB. We also obtain a positive intercept from a linear combination of maps at 1.25 and 2.2 microns. For 2 deg < theta < 15 deg, a power-spectrum analysis yields limits of (theta/5 deg) x delta.F(sub rms)(theta) < 6, 2.5, 0.8, 0.5 nW/sq m.sr at 1.25, 2.2, 3.5 and 4.9 microns respectively. From 10 - 100 microns, the dominant foregrounds are emission by dust in the Solar system and the Galaxy. There the upper limits on the CIB fluctuations are below 1 nW/sq m.sr and are lowest (< equal 0.5 nW/sq m.sr) at 25 microns.

Kashlinsky, A.↗
DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models

Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to sub-hourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of these approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating the Allen and Walker correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons were performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The two models predict annual clipping loss more accurately than simple hourly power limit clipping, with the Allen method typically being slightly more accurate at typical ILR values and the Walker method often being slightly more accurate at high ILR values The models can improve accuracy over the status quo clipping approach up to 3 percentage points in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗
DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models

Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to subhourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of these approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating the Allen and Walker correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons were performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The two models predict annual clipping loss more accurately than simple hourly power limit clipping, with the Allen method typically being slightly more accurate at typical ILR values and the Walker method often being slightly more accurate at high ILR values The models can improve accuracy over the status quo clipping approach up to 3 percentage points in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.

accuracy↗
DOE OSTI2021

Performance Comparison of Clipping Detection Techniques in AC Power Time Series: Preprint

In this research, a variety of methods were developed to detect clipping periods in AC power time series. Novel logic-based and machine learning (ML) approaches were developed to classify time series values as clipping or non-clipping. These approaches were compared to the RdTools method for detecting clipping periods. The logic-based and ML XGBoost approaches achieved F-scores of 82.6 and 74.4, respectively, as compared to the current RdTools approach (F-score of 56.4), indicating a significant improvement at detecting clipping periods. Additionally, the effects of using more accurate clipping filters when evaluating system degradation rates will be assessed in our final manuscript.

clipping↗
DOE OSTI2024

Subhourly Clipping Correction Model Comparison

This work will compare the Allen method and Walker method of accounting for subhourly inverter clipping power losses in hourly PV performance models. The Allen method uses a matrix lookup based on DNI clearness and clipping potential to assign a clipping correction loss at each simulation timestep. The Walker method models the PV DC power input to the inverter as a distribution over the hourly timestep and uses integration over the timestep to determine the amount of clipping that occurs within the timestep. Both these models have been recently implemented in the System Advisor Model's (SAM) open-source code, and will be applied to hourly SURFRAD datasets to analyze the subhourly clipping loss predicted by each model for different system designs and inverter loading conditions. Both models will be compared to "true" 1-minute SURFRAD data simulations to see their accuracy against more accurate 1-minute clipping correction loss predictions. This model comparisons will be investigated in more detail at the PVSC conference in Seattle, Washington June 2024.

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗
DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models: Preprint

Over the past several years there have been numerous attempts at quantifying the inherent power clipping of inverters due to inter-hourly irradiance variability that is not captured in hourly PV performance models. Different models have been proposed to correct for these clipping losses in PV performance estimates, including matrix lookup models, distribution modeling of the PV power performance within a given hour, and machine learning methods. To date, there have been few comprehensive quantitative comparisons of these inverter clipping correction modeling approaches to evaluate the effectiveness of said approaches in predicting the actual behavior of PV system inverter clipping. In this study, we perform such a comparison, evaluating two different clipping correction loss modeling approaches recently implemented in the System Advisor Model (SAM) against clipping losses modeled with 1-minute climate data. These comparisons will be performed across a variety of climate locations and inverter loading ratios to thoroughly analyze the effectiveness of these modeling approaches relative to each other. Results from this analysis reveal that both clipping correction approaches improve annual energy accuracy to within 2% of 1-minute modeled energy yield. The models can improve accuracy up to 3% in systems with ILR of 2.0, showing the importance of this modeling factor in energy yield estimates.

clipping↗
DOE OSTI2024

Artificial ground reflector size and position effects on energy yield and economics of single‐axis‐tracked bifacial photovoltaics

Abstract Artificial ground reflectors improve bifacial energy yield by increasing both front and rear‐incident irradiance. Studies have demonstrated an increase in energy yield due to the addition of artificial reflectors; however, they have not addressed the effect of varying reflector dimensions and placement on system performance and the impact of these parameters on the reflectors' financial viability. We studied the effect of high albedo (70% reflective) artificial reflectors on single‐axis‐tracked bifacial photovoltaic systems through ray‐trace modeling and field measurements. In the field, we tested a range of reflector configurations by varying reflector size and placement and demonstrated that reflectors increased daily energy yield up to 6.2% relative to natural albedo for PERC modules. To confirm the accuracy of our model, we compared modeled and measured power and found a root mean square error (RMSE) of 5.4% on an hourly basis. We modeled a typical meteorological year in Golden, Colorado, to demonstrate the effects of artificial reflectors under a wide range of operating conditions. Seventy percent reflective material can increase total incident irradiance by 1.9%–8.6% and total energy yield by 0.9%–4.5% annually after clipping is considered with a DC–AC ratio of 1.2. Clipping has a significant effect on reflector impact and must be included when assessing reflector viability because it reduces reflector energy gain. We calculated a maximum viable cost for these improvements of up to $2.50–4.60/m 2 , including both material and installation, in Golden. We expanded our analysis to cover a latitude range of 32–48°N and demonstrated that higher‐latitude installations with lower energy yield and higher diffuse irradiance content can support higher reflector costs. In both modeling and field tests, and for all locations, the ideal placement of the reflectors was found to be directly underneath the module due to the optimized rear irradiance increase.

14 SOLAR ENERGY↗