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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 919 records · Page 51
All sourcesNASA NTRSDOE OSTI
DOE OSTI2018

Fast Determination of Distribution-Connected PV Impacts Using a Variable-Time-Step Quasi-Static Time-Series Approach

The increasing deployment of distribution-connected photovoltaic (DPV) systems requires utilities to complete ever more complex interconnection studies. Relatively simple interconnection study methods worked well for low penetrations of photovoltaic systems, but more complicated quasistatic time-series (QSTS) analysis is required to make better interconnection decisions as DPV penetration levels increase. Tools and methods must be developed to support this type of complex analysis to enable efficient completion of PV impact studies. This paper presents a variable-time-step solver for QSTS analysis that significantly shortens the computational time and effort to complete a detailed analysis of the operation of a distribution circuit with many DPV systems. Specifically, it demonstrates that the proposed variable-time-step solver can reduce the required computational time by as much as 84% without introducing any important errors to metrics, such as the number of voltage regulator tap operations and highest and lowest voltage occurring on the distribution circuit during a 1-yr simulation period. Further improvement in computational speed is possible with the introduction of only modest errors in these metrics, such as a 91% reduction with less than 5% error when predicting voltage regulator operations. Furthermore, an alternate variable time-step method is also shown to perform adequately and shows promise of even greater reductions in simulation runtime.

14 SOLAR ENERGY↗
DOE OSTI2020

A New Approach to Indoor Characterization of PV Module Energy Yield Parameters

Temperature and solar irradiance are among the most relevant parameters that affect the energy yield of photovoltaic cells and modules. A rise in the module temperature leads to a significant decrease in the open circuit voltage and a small increase in the short circuit current. Here we use a new tool to determine temperature coefficients as well as to assess power rating of multi c-Si, mono c-Si, CdTe, and CIGS-based PV modules in controlled indoor conditions per IEC 60891 and IEC 61853-1. We use the tool to explore how nonuniformities in module temperature affect the accuracy of the temperature coefficients.

14 SOLAR ENERGY↗
DOE OSTI2020

PV Degradation - Mounting & Temperature

Several studies have found indications that PV degradation may increase in hotter climates. We show in this paper that degradation does correlate to higher module temperatures, but that these temperatures are significantly affected by the module mounting and. The mechanisms of degradation vary with module technology and quality. Using data from different HIT module installations we were able to extract an activation energy that is consistent with hydrogen passivation layer degradation. In addition, we show that low degradation in hot climates can be achieved for Al-BSF technology if properly installed to reduce heat transfer in order to thermally decouple the modules from the roof. We also found that monofacial and bifacial PERC module degradation is in line with historical degradation rates of Al-BSF.

14 SOLAR ENERGY↗
DOE OSTI2020

Effect of Torque-Tube Parameters on Rear-Irradiance and Rear-Shading Loss for Bifacial PV Performance on Single-Axis Tracking Systems

The emergence of cost-competitive bifacial PV modules has raised the question of the additional value of bifacial 1-axis tracking arrays, in particular when considering rear-irradiance losses from the tracker system itself. In this work, the effect of different geometries and materials of torque tubes is evaluated through ray-trace simulations and found to cause rear irradiance shading factors between 2% to 8% for systems without gap between the modules in 2-UP configuration. Inclusion of a gap between the modules can offset the shading factor. Electrical mismatch is also evaluated for the various configurations, and a methodology to apply shading factor and electrical mismatch loss to rear irradiance from the calculated loss in DC power, which averages 1% for the systems explored here, is proposed.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2021

Performance Index Assessment for the PV Fleet Performance Data Initiative

We report on 250 PV systems throughout the United States, comprising 157 MWdc of system capacity and more than 10,000 monthly performance index (PI) values. Loss factors were isolated including first-year start-up issues, snowfall, soiling and inverter downtime. Inverter availability was found to contribute significant system energy loss during the first six months of operation, with an average of 8% loss occurring during this period, and 2.3% on average thereafter. Other start-up issues beyond inverter downtime, such as partial string outage, contributed additional underperformance in the first year of operation across the fleet. Winter performance was also found to be below summer performance on average, likely due to snowfall. A relationship was found between monthly snowfall accumulation in centimeters and monthly under-performance, indicating a 6%-40% loss in months with measured snowfall, depending on climate. After correcting for availability, snow and startup loss, over 90% of systems were performing within 10% of monthly expectation based on satellite resource data and PVWatts production estimates.

loss factors↗
DOE OSTI2024

Automatic Loss Factor Modeling and Attribution on Unlabeled PV Energy Data

We present a novel approach for modeling the loss factors of photovoltaic power generation systems (PV systems). This method is a white-box machine learning model built on convex optimization that is fast, interpretable, and auditable. It takes as an input the measured daily energy produced by the system, over a multi-year period, and returns a multiplicative decomposition model of the daily energy signal and full attribution of the total energy loss to each feature. The methods section of this paper has two major components: (1) the description of the signal decomposition (SD) model, expressed in the SD framework, and (2) the attribution of total energy losses via Shapley values. We validate the method on synthetic and open-source data sets and compare to similar methods from the literature.

artificial intelligence↗
DOE OSTI2021

Probabilistic Voltage Sensitivity Analysis to Quantify Impact of High PV Penetration on Unbalanced Distribution System

From an operational and planning perspective, it is important to quantify the impact of increasing penetration of photovoltaics on the distribution system. Most existing impact assessment studies are scenario-based where derived results are scenario specific and not generalizable. Moreover, stochasticity in the temporal behavior of spatially distributed PVs requires a large number of scenarios that increase with the size of the network and the level of penetration. Therefore, we propose a new computationally efficient analytical framework of voltage sensitivity analysis that allows for stochastic analysis of voltage change due to random changes in PV generation. We first derive an analytical approximation for voltage change at any node of the network due to change in power at other nodes in an unbalanced distribution network. The quality of this approximation is reinforced via bounds on the approximation error. Then, we derive the probability distribution of voltage change at a certain node due to random changes in power injections/consumptions at multiple locations of the network. The accuracy of the proposed PVSA is illustrated using a modified version of the IEEE 37 bus test system. As a result, the proposed PVSA can serve as a powerful tool for proactive monitoring/control and ease the computational burden associated with perturbation based cybersecurity mechanisms.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2019

Method and System for Accessing PV Resource Data from the NSRDB

This study developed a capability to provide spectrally resolved solar resource data from the National Solar Radiation Data Base (NSRDB) effectively providing 20 years of half-hourly data for all of the US at a 4kmx4km spatial resolution. This data is now available freely to users directly through a GIS based web interface (https://nsrdb.nrel.gov) as well as through an Application Programming Interface. Users of this data can conduct more accurate pre-feasibility studies and assess multiple PV technologies. It is expected that widely used models such as PVSyst, National Renewable Energy Laboratory’s (NREL’s) System Advisor Model (SAM) and First Solar designed PlantPredict will develop the capabilities to use this spectral data.

41 EE - Solar Energy Technologies Office (EE-4S)↗
DOE OSTI2011

Implementing Solar PV Projects on Historic Buildings and in Historic Districts

Many municipalities, particularly in older communities of the United States, have a large amount of historic buildings and districts. In addition to preserving these historic assets, many municipalities have goals or legislative requirements to procure a certain amount of energy from renewable sources and to become more efficient in their energy use; often, these requirements do not exempthistoric buildings. This paper details findings from a workshop held in Denver, Colorado, in June 2010 that brought together stakeholders from both the solar and historic preservation industries. Based on these findings, this paper identifies challenges and recommends solutions for developing solar photovoltaic (PV) projects on historic buildings and in historic districts in such a way as to notaffect the characteristics that make a building eligible for historic status.

14 SOLAR ENERGY↗
DOE OSTI2011

Financing Solar PV at Government Sites with PPAs and Public Debt (Brochure)

Historically, state and local governmental agencies have employed one of two models to deploy solar photovoltaic (PV) projects: (1) self-ownership (financed through a variety of means) or (2) third-party ownership through a power purchase agreement (PPA). Morris County, New Jersey, administrators recently pioneered a way to combine many of the benefits of self-ownership and third-party PPAsthrough a bond-PPA hybrid, frequently referred to as the Morris Model. At the request of the Department of Energy's Solar Market Transformation group, NREL examined the hybrid model. This fact sheet describes how the hybrid model works, assesses the model's relative advantages and challenges as compared to self-ownership and the third-party PPA model, provides a quick guide to projectimplementation, and assesses the replicability of the model in other jurisdictions across the United States.

14 SOLAR ENERGY↗
DOE OSTI2012

Strategic Sequencing for State Distributed PV Policies: A Quantitative Analysis of Policy Impacts and Interactions

State and local policymakers show increasing interest in spurring the development of customer-sited distributed generation (DG), in particular solar photovoltaic (PV) markets. Prompted by that interest, this analysis examines the use of state policy as a tool to support the development of a robust private investment market. This analysis builds on previous studies that focus on government subsidies to reduce installation costs of individual projects and provides an evaluation of the impacts of policies on stimulating private market development.

14 SOLAR ENERGY↗
DOE OSTI2012

Photovoltaic (PV) Pricing Trends: Historical, Recent, and Near-Term Projections

This report helps to clarify the confusion surrounding different estimates of system pricing by distinguishing between past, current, and near-term projected estimates. It also discusses the different methodologies and factors that impact the estimated price of a PV system, such as system size, location, technology, and reporting methods. These factors, including timing, can have a significant impact on system pricing.

14 SOLAR ENERGY↗
DOE OSTI2017

2015 South Carolina PV Soft Cost and Workforce Development Part 2: Six Month Confirmation of Anticipated Job Growth

In 2015, a program was initiated to carefully track and monitor the growth of the solar industry in SC. Prior to then, little information was available on the costs associated with distributed photovoltaic (PV) installations in the Southeastern US. For this report, data were collected from businesses on the number of hires they had at the end of 2014 and compared with data for 2015 and June 2016. It was found that the percentage of installers within the state who serve the residential sector increased to 82% from 67%. During the same time period, the average size of initiated installations for residential, commercial, and utility scale installations all trended upwards. Where residential installations were typically 5 kW-DC in 2014, they were typically 10 kW-DC by late 2015 and in mid-2016. For commercial installations, the average size grew from 84 kW-DC in 2014 to between 136-236 kW-DC in 2015 and then 188-248 kWDC in mid-2016. An exception was seen in utility scale installations where a 2.3 MW-DC system was common in 2014, the size grew to be 5-15 MW-DC in late 2015. The average size dropped 3.1-4.4 MWDC in mid-June 2016, though individual averages up to 20 MW-DC were reported.

99 GENERAL AND MISCELLANEOUS↗
DOE OSTI2018

Impact of Research and Development, Analysis, and Standardization on PV Project Financing Costs

The technical report discusses how R and D efforts focused on removing perceived risk from cash flows to investors have the potential to lower the cost of capital and increase the amount of leverage in a solar project. It also discusses how creating business efficiencies that allow financing transactions to occur more quickly with less effort can reduce the upfront costs associated with arranging financing for a solar project or group of projects. The paper then assesses the impact that these R and D activities might have on the volatility of PV asset cash flows and asset value, as well as the upfront costs of arranging a financial transaction. Finally, we insert these assumptions into financial models to analyze their impacts on the cost of capital for equity and debt investors, project leverage, and upfront financial transaction costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2021

PV Fleet Performance Data Initiative: Performance Index-Based Analysis

In this analysis, we report on 250 PV systems throughout the US, comprising 157 MWdc of system capacity and over 10,000 monthly PI values based on high-frequency (subhourly) energy data and satellite-based resource data. The distribution of PI values is analyzed, and multiple causes of underperformance are assessed, including first-year startup issues, snowfall and inverter downtime. An initial distribution of raw monthly PI values was collected with mean measured / modeled performance of PI = 0.935. After correcting for the three identifiable loss factors mentioned above, an average monthly performance of PI = 0.994 was obtained, with a distribution closely following a Gumbel Extreme Value distribution. In particular, inverter availability was found to contribute a system energy loss of 2.3% on average, except in the first six months of operation when availability losses are closer to 8%. Other startup issues beyond inverter downtime such as partial string outage contributed an additional 5% underperformance in the first year of operation across the fleet. Winter performance was also found to be 5%-10% below summer performance on average, likely due to snowfall. A simple linear relationship was found between snow loss and monthly snowfall accumulation in cm, indicating between 6% - 40% loss in months with nonzero snowfall, depending on climate.

14 SOLAR ENERGY↗
DOE OSTI2021

PV Reference Cells for Outdoor Use: An Investigation of Calibration Factors

Reference cells are widely used in the photovoltaic (PV) industry to measure irradiance. For field applications and outdoor use, a variety of products are on the market, and they are often perceived as a low-cost alternative to thermopile radiometers. But reference cell characteristics differ substantially from the latter, therefore measurements made by one category of instrument cannot be substituted for or directly compared with the other. The National Renewable Energy Laboratory's (NREL's) Solar Radiation Research Laboratory is currently in a multiyear effort to develop guidance and recommendations for the design and use of outdoor reference cells, with a view to reducing inconsistencies and measurement uncertainty. The core of this effort is the long-term deployment of 39 units of 10 distinct types made by 6 manufacturers. This report compares the calibration factors provided by manufacturers - which most users rely upon - to those measured at a high level of accuracy by NREL's Cell Lab. It also reports on the application of the Broadband Outdoor Radiometer Calibration (BORCAL) method to reference cells. As a group, the factory calibrations for the crystalline silicon cells showed a small positive bias of +0.7%, with a tight distribution around this value. This is a very good result for the manufacturers. The two World Photovoltaic Scale cells constituted outliers, with a mean deviation of -0.8%, which is still within the reported uncertainty ranges. The data collected during the BORCAL procedures show a strong and systematic fluctuation of the apparent responsivity of the reference cells over the course of a day, which was anticipated. Consequently, the standard BORCAL R@45 calibration factors underestimated the reference cell responsivities by 1%-4%. An alternate responsivity indicator was developed, R'@30, incorporating adjustments for temperature response, spectral response, and directional response. This reduced the mean deviation to -0.4% compared to the Cell Lab. Further, two independent BORCAL sessions were able to produce the same values to within ±0.2%. This could be a viable approach for verifying reference cells using existing BORCAL infrastructure, for example, to evaluate their long-term stability. The daily profiles of responsivity obtained during the BORCAL sessions (provided in Appendix A) show clear patterns of differences between models, which provides evidence that their directional responses differ from each other; thus, two sensors that are calibrated correctly might give the same reading at 30° zenith angle but systematically different readings at 60°. The impact of these inconsistencies in field operation will be quantified using the long-term observations started in 2020.

14 SOLAR ENERGY↗