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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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All sourcesNASA NTRSDOE OSTI
DOE OSTI2019

Overbuilding & curtailment: The cost-effective enablers of firm PV generation

Current thinking considers that PV output curtailment is a last resort measure to be avoided. In this article, we argue that supply-shaping, achieved through proactive curtailment associated with PV oversupply, is actually critical to achieving intermittency mitigation and delivering firm PV generation at the lowest cost. We investigate the premium to transform a low-cost, but intermittent solar kWh into a firm, effectively dispatchable kWh. We show that a fundamental ingredient of minimizing this premium is to optimally overbuild and, as necessary and appropriate, curtail PV generation. Drawing on a case study in the State of Minnesota, we show that firm, high-penetration-ready PV generation could be achieved at a production cost at or below current conventional generation, especially when optimally coupled with wind generation. Here, we conclude with a recommendation that in order to achieve this lowest cost firm generation potential, proactive curtailment strategies should inform future transactional PV remuneration systems.

14 SOLAR ENERGY↗
DOE OSTI2019

Data-Driven Day-Ahead PV Estimation Using Hybrid Deep Learning

Ongoing smart grid activities and associated automation resulted in rich set of data. Here, these data can be utilized for monitoring and estimation of real time photovoltaic (PV) generation. Inherent variability in PV and related impact on power systems is a challenging problem. Improving the accuracy of PV generation estimation is beneficial for both the PV owners and the grid operators. Recently, deep learning algorithms possible by the availability of data have shown its advantages for time series estimation; however, its application on PV generation estimation is still in the early stage. In this paper, a hybrid estimation model with a combination of long-short-term-memory network (LSTM) and persistence model (PM) is developed to provide day-ahead PV estimation at 15-minute time interval with high accuracy and robustness. Simulation results show the superior performance of the proposed method over existing methods for most of the test cases.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 99% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗
DOE OSTI2013

PSCAD Modules Representing PV Generator

Photovoltaic power plants (PVPs) have been growing in size, and the installation time is very short. With the cost of photovoltaic (PV) panels dropping in recent years, it can be predicted that in the next 10 years the contribution of PVPs to the total number of renewable energy power plants will grow significantly. In this project, the National Renewable Energy Laboratory (NREL) developed a dynamic modeling of the modules to be used as building blocks to develop simulation models of single PV arrays, expanded to include Maximum Power Point Tracker (MPPT), expanded to include PV inverter, or expanded to cover an entire PVP. The focus of the investigation and complexity of the simulation determines the components that must be included in the simulation. The development of the PV inverter was covered in detail, including the control diagrams. Both the current-regulated voltage source inverter and the current-regulated current source inverter were developed in PSCAD. Various operations of the PV inverters were simulated under normal and abnormal conditions. Symmetrical and unsymmetrical faults were simulated, presented, and discussed. Both the three-phase analysis and the symmetrical component analysis were included to clarify the understanding of unsymmetrical faults. The dynamic model validation was based on the testing data provided by SCE. Testing was conducted at SCE with the focus on the grid interface behavior of the PV inverter under different faults and disturbances. The dynamic model validation covers both the symmetrical and unsymmetrical faults.

14 SOLAR ENERGY↗
DOE OSTI2016

Final Report Report: Smart Grid Ready PV Inverters with Utility Communication

In 2011, EPRI began a four-year effort under the Department of Energy (DOE) SunShot Initiative Solar Energy Grid Integration Systems - Advanced Concepts (SEGIS-AC) to demonstrate smart grid ready inverters with utility communication. The objective of the project was to successfully implement and demonstrate effective utilization of inverters with grid support functionality to capture the full value of distributed photovoltaic (PV). The project leveraged ongoing investments and expanded PV inverter capabilities, to enable grid operators to better utilize these grid assets. Developing and implementing key elements of PV inverter grid support capabilities will increase the distribution system’s capacity for higher penetration levels of PV, while reducing the cost. The project team included EPRI, Yaskawa-Solectria Solar, Spirae, BPL Global, DTE Energy, National Grid, Pepco, EDD, NPPT and NREL. The project was divided into three phases: development, deployment, and demonstration. Within each phase, the key areas included: head-end communications for Distributed Energy Resources (DER) at the utility operations center; methods for coordinating DER with existing distribution equipment; back-end PV plant master controller; and inverters with smart-grid functionality. Four demonstration sites were chosen in three regions of the United States with different types of utility operating systems and implementations of utility-scale PV inverters. This report summarizes the project and findings from field demonstration at three utility sites.

14 SOLAR ENERGY↗
DOE OSTI2018

Terms, Trends, and Insights on PV Project Finance in the United States, 2018

This brief is a compilation of data points and market insights that reflect the state of the project finance market for solar photovoltaic (PV) assets in the United States as of the third quarter of 2018. This information can generally be used as a simplified benchmark of the costs associated with securing financing for solar PV as well as the cost of the financing itself (i.e., the cost of capital). This work represents the third DOE sponsored effort to benchmark financing costs across the residential, commercial, and utility-scale PV markets, as part of its larger effort to benchmark the components of PV system costs. capital). This work represents the third DOE sponsored effort to benchmark financing costs across the residential, commercial, and utility-scale PV markets, as part of its larger effort to benchmark the components of PV system costs.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

New Approaches to Distributed PV Interconnection: Implementation Considerations for Addressing Emerging Issues

As the cost of installing distributed solar photovoltaics (PV) has fallen, the number of requests to interconnect PV systems to distribution grids has risen substantially in many utility service areas. In response, states and utilities are modifying their interconnection practices to address new DER challenges. This report examines emerging issues associated with interconnecting residential- and commercial-scale PV to facilitate sharing of lessons learned and best practices across jurisdictions. It covers issues related to allocating and understanding costs, understanding grid conditions to inform PV siting, interconnecting storage, automating processes, and requiring the availability of advanced inverter functions that can help increase PV hosting capacity. This report builds on an earlier study that reviews interconnection practices and costs across the western states (Bird et al. 2018). Both reports were developed as part of a larger, joint project between the Western Interstate Energy Board and the National Renewable Energy Laboratory, funded by the U.S. Department of Energy's Solar Energy Technologies Office, that examines barriers to distributed PV in the 11 states within the Western Interconnection.

14 SOLAR ENERGY↗
DOE OSTI2026

Public Reference Data for Megawatt-Scale Hydrogen Electrolysis - NLR Historical Solar PV

The U.S. Department of Energy and National Laboratory of the Rockies (NLR) demonstrate hydrogen electrolysis from variable sources, hydrogen compression and storage, and hydrogen fuel cell power production using megawatt-scale equipment at NLR’s Flatirons Campus as part of the Advanced Research on Integrated Energy Systems (ARIES) research platform. This dataset represents part of that effort and is intended for academic, national laboratory, industrial, and other stakeholders to plan, design, and validate models of megawatt-scale hydrogen technologies and diverse energy infrastructure nationwide. These data provide a baseline for how existing hydrogen electrolysis technologies perform when coupled with various energy technologies. Future datasets will demonstrate how existing hydrogen fuel cell technologies can provide controllable, dispatchable, and variable power output for artificial intelligence data centers and other variable loads. This dataset entry describes the behavior of a 1.25-MW proton exchange membrane MC250 electrolyzer system, manufactured by Nel Hydrogen , [1] when fed historical data generated by the 430-kW, fixed-axis solar photovoltaic (PV) array located at NLR’s Flatirons Campus. (While the electrolyzer balance of plant supports up to 2.5 MW of electrolysis, NLR only has a single 1.25-MW electrolysis stack.) Solar PV power output data for the 2020 calendar year were categorized on a daily basis by total energy generation and standard deviation. Each day was then ranked by these metrics, and the 25th, 50th, and 100th percentiles were selected. The 75th percentile day did not exhibit sufficient variability to make for a valuable experiment. A similar process was used for the related historical wind dataset . [2] The historical days in 2020 that represented these percentiles are Dec. 19, March 29, and May 4, respectively. The entire solar day’s power profile was then fed through the MC250 electrolyzer. Due to its length, the 100th percentile day experiment was split into two parts, and the final 3 hours of the solar day were not captured. These final 3 hours contained no spikes or dips of interest and simply represented a slow decay of input solar power. Also, a single timestamp (13:13:47 on Jan. 14, 2026) was lost in the hydrogen system supervisory control and data acquisition. Finally, during the 25th percentile experiment (solar day Dec. 19, 2020) data recording was lost from 11:00:13 to 11:14:45. The roughly 15 minutes of the solar profile were rerun at the end of the experiment and spliced into this time slot during post-processing. The electrolysis system controls hydrogen production by varying direct current applied to the stack, from a maximum of 3,000 A to a minimum safe operation of 300 A, or 10%. Because the current–voltage characteristic changes as the stack ages and efficiency degrades, the actual minimum safe operating power changes over time. The historical solar profiles were translated from power (kilowatts) to current (amperes) using a curve fit with calibration data and sent to the electrolyzer power supply at 1-Hz frequency. For more details on the statistical analysis process, see the slide deck “Public Reference Data for Megawatt-Scale Hydrogen Electrolysis: NLR Historical Solar PV Analysis and Profile Generation” accessible with this data entry. These datasets report relevant hydrogen balance-of-plant and system data, all captured at 1 Hz, including hydrogen mass production measured with an Emerson Coriolis flow meter. Each .zip file represents a single solar PV electrolysis experiment and is formatted as: {technology}_{percentile}_{scaling factor} For instance, “solarPV-430kW_25_2x.zip” reports the experiment using the 25th percentile solar data from the historical 2020 solar PV dataset, scaled to 200%. Scaling factors were applied to the generated solar PV power output files to more closely match the 1.25-MW capacity of the electrolyzer. Each .zip folder contains the following files: A .csv file containing raw data. An .xlsx file explaining all the fields in the raw data. A .png plot showing the time series of hydrogen production, electrolysis power consumption, and solar power input. A PDF file detailing the historical solar data statistical analysis used to generate the solar profile. An experiment labeled “characterization_200.zip” demonstrates the MC250 electrolyzer steady-state response with 30-minute load steps for a total duration of 5 hours. Finally, a .csv file is provided with all experiments combined into one dataset labeled "combined_solarPV_experiments.csv". [1] nelhydrogen.com/product/mc-series-electrolyser . [2] data.nlr.gov/submissions/316 .

08 HYDROGEN↗
DOE OSTI2018

The Effects of Market Concentration on Residential Solar PV Prices: Competition, Installer Scale, and Soft Costs

Competition among residential solar photovoltaic (PV) installers may reduce PV price markups and yield lower prices. At the same time, competition may reduce installer experience and opportunities for cost reductions through learning-by-doing and economies of scale. These dynamics suggest that PV non-hardware or 'soft' costs and installed prices depend on the distribution of market shares among installers, also known as market structure. This study leverages a rich data set of 226,769 residential PV systems to examine the relationship between market structure, soft costs, and PV prices. The results show that PV prices are lower, on average, in more concentrated markets, i.e., markets with fewer installers or where a subset of installers holds a large market share. The study provides evidence that this relationship is non-linear, such that prices are minimized in markets with an optimal balance of the benefits of market concentration and the benefits of competition.

14 SOLAR ENERGY↗
DOE OSTI2018

Economic Dispatch for DC-Connected Battery Systems on Large PV Plants

Falling costs of energy storage combined with an increasing need to mitigate variable generation integration issues is leading to more PV projects that include batteries. Large PV plants that include batteries face challenges optimizing the value of the power they produce, especially when the batteries are DC-coupled and otherwise clipped power (due to high DC/AC ratio) is available. Multiple charging times and sources must be considered, including: grid charging, charging from PV that would otherwise be exported to the grid, and charging from clipped PV power. Then discharge schedules must be optimized against time-of-day pricing. The National Renewable Energy Laboratory in partnership with Southern Company has developed a heuristic algorithm in the System Advisor Model (SAM) which strategically charges the battery from PV and discharges the battery depending on the time-of-day prices of the PPA, the forecast PV production, and the estimated wear cost of the battery. The algorithm and case study results from a generic plant will be presented with recommendations for future work.

25 ENERGY STORAGE↗
DOE OSTI2018

Sequential Mitigation Solutions to Enable Distributed PV Grid Integration: Preprint

This paper provides a method to assess the impact of multiple mitigation solutions including distribution grid upgrades and advanced control technologies on increasing distributed PV penetration in distribution feeders, and thus paves the way to estimate the costs needed for reaching certain PV penetration level. Based on three assumptions of creating increasing PV penetration scenarios, this paper first studies the distribution impact of PV power and quantifies the PV hosting capacity in distribution feeders. Then, it studies multiple mitigation solutions to increase PV penetration on distribution feeders. These mitigation solutions are implemented in a sequential manner according to their typical costs. The proposed study is demonstrated on two representative distribution feeders.

14 SOLAR ENERGY↗
DOE OSTI2018

Evaluating Energy Impacts and Costs from PV Component Failures: Preprint

The PV Reliability Performance Model (PV-RPM) tool is used to simulate the cost and energy impacts of component faults and failures for a hypothetical PV system. This model, originally developed by Sandia National Laboratories, is a new feature in the National Renewable Energy Laboratory's System Advisor Model (SAM), which performs stochastic analysis sampling of probability distributions for faults, failures, and repairs. One PV system was created for this analysis to be representative of a portfolio of maintenance data maintained by Sandia National Laboratories. Fault, failure, and repair distributions from this system are varied from current baseline conditions to simulate different reliability and repair scenarios and resulting energy and cost impacts. Results highlight ways to express the uncertainty around PV system performance when considering the probabilistic behavior of components in the system, and ways the PV-RPM model can be used to explore different failure and repair scenarios.

14 SOLAR ENERGY↗
DOE OSTI2023

Real-Time Regional PV Spinning Reserve Estimator with AGC Look-Ahead Windows: Preprint

Curtailed PV generation is a zero-marginal cost spinning reserve that can be used for a number of active power control services. However, unlike the traditional spinning reserve providers, i.e., fossil-fueled generators, who have well-defined operating characteristics, e.g., available headroom or potential high limit (PHL), PV plants have by nature variable and uncertain operating characteristics. To ensure the effective coordination between PV plants and the system operator during an active power control event, accurate forecasts of the PV PHL are essential. A novel reference-control grouping based scaling method has been proposed by NREL to estimate the PV PHL in real-time. This work further enhances the methodology by: 1) improving the model accuracy through machine learning; 2) considering look-ahead windows introduced by the computation and communication latencies; 3) applying the method to regional spinning reserve estimation. A significant performance improvement, over 1.6% of estimation error reduction, has been observed based on real-world data collected by CAISO and PV plant operators.

potential high limit↗
DOE OSTI2023

Extreme Weather Events and PV Systems Impact

The impact of extreme weather events on PV performance was studied by comparing the National Oceanic and Atmospheric Administration database on severe weather with the National Renewable Energy Laboratory's (NREL) PV Fleet database on continuous PV performance. We identified 170 systems that were immediately impacted by weather events. These severe weather events lead to a median loss of only 1 % of annual production. However, flooding and high wind events were found to have an extremely long tail extending to 60 % loss, showing that these discrete events can pose a substantial risk to PV systems. Besides the short-term impact of lost production due to outages, we also found a statistically significant increased performance loss rate (PLR) for high wind events above 90 kilometer/hour, by comparing PLR before and after these events. Similarly, hail events caused higher PLR for hail sizes of and above 25 mm indicating that more stringent hail testing for PV modules is required. In addition, very high snow loads may also cause increased PLRs, but more data are required to better quantify the impact. These findings illustrate the substantial risk extreme weather events pose short-and long-term to fielded PV installations.

extreme weather↗
DOE OSTI2024

Introducing the Baseline Performance Reference (BPR) for Irradiance in PV System Applications

Photovoltaic (PV) reference cells, modules, and arrays respond in a complex manner to the many variables that define their operating environment. PV reference cells for irradiance measurement have similarities but also differences to operational PV modules. For this reason, a more neutral and a well-defined generic PV reference cell is needed, whose characteristics are close enough to most operational PV devices to make stable performance indicators possible but whose characteristics are not necessarily identical to any of them. This poster describes a new well-defined reference quantity for outdoor PV measurements, called the baseline performance reference (BPR).

baseline performance reference↗
DOE OSTI2015

Is $50/MWh solar for real? Falling project prices and rising capacity factors drive utility-scale PV toward economic competitiveness

Recently announced low-priced power purchase agreements (PPAs) for US utility-scale photovoltaic (PV) projects suggest $50/MWh solar might be viable under certain conditions. To explore this possibility, this paper draws on an increasing wealth of empirical data to analyze trends in three of the most important PPA price drivers: upfront installed project prices, operations, and maintenance (O&M) costs, and capacity factors. Average installed prices among a sample of utility-scale PV projects declined by more than one third (from 5.8/W AC to 3.7/WAC) from the 2007–2009 period through 2013, even as costlier systems with crystalline-silicon modules, sun tracking, and higher inverter loading ratios (ILRs) have constituted an increasing proportion of total utility-scale PV capacity (all values shown here are in 2013 dollars). Actual and projected O&M costs from a very small sample of projects appear to range from $20–$40/kW AC -year. Furthermore, the average net capacity factor is 30% for projects installed in 2012, up from 24% for projects installed in 2010, owing to better solar resources, higher ILRs, and greater use of tracking among the more recent projects. Based on these trends, a pro-forma financial model suggests that $50/MWh utility-scale PV is achievable using a combination of aggressive-but-achievable technical and financial input parameters (including receipt of the 30% federal investment tax credit). Although the US utility-scale PV market is still young, the rapid progress in the key metrics documented in this paper has made PV a viable competitor against other utility-scale renewable generators, and even conventional peaking generators, in certain regions of the country.

14 SOLAR ENERGY↗
DOE OSTI2020

Divanadium substituted keggin [PV 2 W 10 O 40 ] on non-reducible supports-Al 2 O 3 and SiO 2 : synthesis, characterization, and catalytic properties for oxidative dehydrogenation of propane

Molecular metal oxide cluster, K 5 [α-1,2-PV 2 W 10 O 40 ] (PV 2 W 10 ), was found to have intrinsic catalytic activity for the oxidative dehydrogenation of propane with high selectivity (> 80%) to propylene at low propane conversion (0.3%). Synthesis of dispersed PV 2 W 10 in non-reducible supports, γ-Al 2 O 3 and SiO 2 , was done by incipient wetness impregnation. The supported catalysts were characterized by IR, Raman spectroscopy, nitrogen adsorption, x-ray powder diffraction (PXRD), elemental analysis, hydrogen temperature-programmed reduction (H 2 –TPR), and ammonia temperature-programmed desorption (NH 3 –TPD). Catalytic testing of the supported PV 2 W 10 at equimolar cluster concentration revealed that when supported in γ-Al 2 O 3 it is more active (sevenfold increase in propane conversion) but in SiO 2 it is more selective to propylene (94%). The observed performance was due to both an increase in reducibility and higher concentration of strong acid sites for PV 2 W 10 supported in γ-Al 2 O 3 versus SiO 2 . Lastly, PV 2 W10 was shown to remain intact under reaction conditions indicating its thermal and oxidative stability.

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

Resampling and data augmentation for short-term PV output prediction based on an imbalanced sky images dataset using convolutional neural networks

Integrating photovoltaics (PV) into electricity grids is challenged by potentially large fluctuations in power generation. In recent years, sky image-based PV output prediction using convolutional neural networks (CNNs) has emerged as a promising approach to forecasting fluctuations. A key challenge is imbalanced sky image datasets: because of the geography of solar PV system installations, sky image datasets are often rich in sunny condition data but deficient in cloudy condition data. This imbalance contrasts with the fact that model errors are dominated by cloudy condition performance. In this study, we attempt to remedy this by exploring the enrichment and augmentation of an imbalanced sky images dataset for two PV output prediction tasks: nowcasting (predicting concurrent PV output) and forecasting (predicting 15-minute-ahead future PV output). We empirically examine the efficacy of using different resampling and data augmentation approaches to create a rebalanced dataset for model development. A three-stage greedy search is used to determine the optimal resampling approach, data augmentation techniques and over-sampling rate. The results show that for the nowcast problem, resampling and data augmentation can effectively enhance the model performance, reducing overall root mean squared error (RMSE) by an average of 4%, or a 15 std. (standard deviation) of improvement compared to the variability of the baseline model. In contrast, the treatment RMSE for the forecast problem nearly always overlaps the baseline performance at the ± 2 std. level. The optimal resampling approach expands on the original dataset by over-sampling the minority cloudy data, with the best results from large over-sampling rate (e.g., 4 ~ 6 times over-sampling of cloudy images).

14 SOLAR ENERGY↗