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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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DOE OSTI2022

Investigation of Stochastic Unit Commitment to Enable Advanced Flexibility Measures for High Shares of Solar PV

As the share of solar photovoltaics (PV) in the power system increases, there is a growing need for flexibility from multiple, possibly interdependent sources to adjust to PV's variability, uncertainty, and diurnal dependence. This paper investigates how stochastic unit commitment leveraging probabilistic solar forecasts can support other flexibility measures under high solar shares. We consider two flexibility measures relevant to day-ahead scheduling: battery energy time-shifting and solar ancillary service provision. Unit commitment and economic dispatch simulations are conducted on a realistic test system based on Texas using day-ahead solar trajectories. The benefits of the two flexibility measures are pronounced when the instantaneous solar share is high, offering cost savings of 10%-20% in the spring. For a Texas-sized system, this translates to hundreds of millions of dollars in cost savings once the installed PV capacity enables instantaneous solar shares regularly exceeding 40%. Using probabilistic forecasts also greatly increases the reliability of upward reserve provision from solar PV, reducing unserved reserves by 50%-100%. Both day-ahead forecast resolution and errors can impact system reliability at high solar shares, but the stochastic formulation has significant value, mitigating reliability impacts on over-forecast days.

ancillary services↗
DOE OSTI2022

South Texas Industrial Assessment Center (Final Report)

The South Texas Industrial Assessment Center (ST-IAC) provides small to mid-size manufacturing companies and water treatment plants in the Rio Grande Valley of Texas, free energy assessments to help them reduce costs and stay competitive for sustainable development. The ST-IAC operates within the University of Texas Rio Grande Valley (UTRGV), and is the largest Hispanic-serving IAC in the U.S. Created in 2016, the ST-IAC, situated in the most economically disadvantaged region in the country, has been successful in training students and assessing local industries served by a predominantly Hispanic population. UTRGV, now the second largest Hispanic Serving Institution (HSI) of higher education in the U.S., has a student population of 32,000, of which 90% is Hispanic. While Covid-19 had a negative impact to our program during Budget Periods (BP) 4 and 5, the center still managed to provide opportunities for individuals, manufacturing and water industries in the local region. The program developed an energy engineering program in the region and further advanced national priorities. Those include resilience and sustainability of operations by implementing energy efficiency processes, renewable energy sources, decarbonization, battery storage, and cybersecurity in south Texas.

14 SOLAR ENERGY↗
DOE OSTI2026

Scheduler Modeling of Distributed Energy Resources for Providing Ancillary Services

Distribution energy resources (DERs) have been integral components of modern power systems, and their capability to provide grid services has been widely studied. To promote the deployment of these resources in providing grid services in real-world utility operations, this paper proposes a day-ahead scheduler model for a distribution system connected DER plant. A certain amount of generation capacity of this DER plant is reserved for frequency services, and some ancillary services for the distribution system-including peak load reduction, voltage regulation, and power factor control-are integrated into the model. The model is tested on a real-world distribution system. From the simulation results, the energy and reserve schedule of the solar photovoltaic (PV) unit and battery energy storage system (BESS) can be determined, and voltage and power factor are well maintained. Additionally, in order to demonstrate the specific characteristics of the co-located and hybrid operation modes for the PV and BESS, these two modes are analyzed both theoretically and through real-time simulation. Simulation results show that most of PV's variability is transferred to the net power in the co-located mode, whereas it is transferred to the BESS in the hybrid mode. This proposed scheduler model and the comparison of co-located and hybrid modes can provide practical guidance for the applications of DER plant in the real-world utility.

14 SOLAR ENERGY↗
DOE OSTI2018

Seasonal Trends of Soiling on Photovoltaic Systems: Preprint

This work investigates the seasonal variability of PV soiling losses over a 12-month period for sixteen soiling stations deployed in the USA. A new parameter able to rank the sites according to the cumulative losses occurring over 3- and 6- month periods is presented. The relations between soiling losses and particulate matter are briefly discussed as well. Moving from long-term to shorter-term data increases the complexity of the analysis: monthly correlations are found to have lower accuracy than the longer term ones presented in the literature.

14 SOLAR ENERGY↗
DOE OSTI2023

Considering the Variability of Soiling in Long-Term PV Performance Forecasting

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term photovoltaic (PV) forecasting. Independent engineering firms typically build forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions are constructed through a Monte Carlo analysis that includes various factors, such as satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within long-term performance uncertainty. In this work soiling information from 16 sites in the U.S. Southwest are combined with 24 years of rainfall data to generate 24 years of energy production with soiling losses and then subsequently generate probability of exceedance values (e.g., P50, P90, P95…). The results show that the size of the 90% confidence interval (P5–P95) can increase from –0.7% to 10.1% when interannual soiling variability and soiling rate uncertainty is included.

14 SOLAR ENERGY↗
DOE OSTI2024

Regional Real-Time PV Spinning Reserve Estimator

Curtailed photovoltaic (PV) generation is a zero-marginal-cost spinning reserve that can be used for a number of active power control services. Unlike traditional spinning reserve providers, however, i.e., fossil-fueled generators, which 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 knowledge of the PV PHL is essential. It ensures that enough headroom is reserved by the PV plants to deliver the award services in real time and informs feasible dispatch decisions made by the market operator. To tackle this challenge, a novel reference-control grouping-based PV plant reserve estimation method has been proposed by the National Renewable Energy Laboratory under past projects funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Solar Energy Technologies Office. The estimation method separates inverters within a plant into two groups: a control group and a reference group. While the reference group is reserved to operate at its PHL, the control group can be curtailed to provide the grid services. Real-time outputs from the reference inverters are used to estimate the PHL for the whole plant based on the ratio between capacities of the reference group and of the plant. This work further enhances the methodology by (1) improving the model accuracy through machine learning; (2) automating the reference inverter selection through correlation analysis; (3) considering estimation look-ahead windows; and (4) applying to regional spinning reserve estimation. Significant performance improvement has been observed based on real-world data collected by CAISO, Southern Company, and Terabase Energy. Compared with the original scaling method, the newly proposed machine learning-based approach reduces the estimation errors by 30% and 13% at the plant level and region level, respectively. Results obtained from this project are intended to be used by grid operators, market operators, balancing authorities, and PV plant owners and operators to facilitate PV participation in ancillary service markets. Regulators, policymakers, and system planners can also consider the results of this work in their decision-making processes. In addition to the performance improvement on the existing reference-control based grouping method, we also investigated how the variability of PV generation from a single PV inverter can be used to represent the variability of PV generation at the plant level.

24 POWER TRANSMISSION AND DISTRIBUTION↗
DOE OSTI2023

Considering the Variability of Soiling in Long-Term PV Performance Forecasting: Preprint

This study presents the development of a methodology for evaluating the variability associated with soiling on long-term PV forecasting. Independent engineering firms typically build P50 forecasts for large PV plants through the use of the PVsyst software, where monthly soiling losses are one of many inputs to the P50 model. Subsequently, long-term performance distributions, or Pvalues, are constructed through a Monte Carlo analysis that includes various factors such as: satellite irradiance modeling uncertainty, uncertainty in the PVsyst model, and long-term irradiance variability. Often the PVsyst model uncertainty is increased to account for sites with significant soiling concerns but no systematic method has been presented in the literature to specifically include soiling variability within Pvalues. In this work soiling information from 16 sites in the U.S. Southwest are combined with 20 years of rainfall data to generate 20 years of energy production with soiling losses and then subsequently generate Pvalues. The results show that the spread of Pvalues (P1-P99) can increase from 0-13% when interannual soiling variability is included.

interannual variability↗
DOE OSTI2020

Data-Driven Day-Ahead PV Estimation Using Autoencoder-LSTM and Persistence Model

Inherent variability in photovoltaic (PV) and associated impacts on power systems is a challenging problem for both the PV owners and the grid operators. Existing statistical and machine learning algorithms typically work well for weather conditions similar to historical data. Furthermore, uncertain weather conditions pose a great challenge to the estimation accuracy of the estimation models. With the enhanced integration of intelligent electronic devices and the realization of associated automation in the power grid, renewable energy data is becoming more accessible, which can be utilized by deep learning models and improve the PV power generation estimation accuracy. In this paper, a hybrid deep learning model driven by external weather data is proposed to do day-ahead PV output forecasting at 15-minute-interval. The proposed model is motivated by the recent advancement of Long-Short-Term-Memory (LSTM) networks and AutoEncoder (AE), which estimates uncertainties in sequence while making the prediction for complex weather conditions. Meanwhile, the persistence model (PM) is used to predict continuous sunny weather conditions. The forecasting result is validated with data from multiple locations

42 ENGINEERING↗
DOE OSTI2026

Assessing Photovoltaic Capacity Factor Variability Using Long-Term Satellite Derived Solar Resource Data Under Brazilian Climate

Accurate estimation of photovoltaic (PV) energy yield and its variability is essential for reducing financial risk and supporting reliable system planning for rapidly expanding PV markets. In Brazil, high solar adoption and increasing levels of distributed energy resources are beginning to introduce operational challenges such as curtailment and evolving grid requirements. Understanding how natural variability in solar resource propagates into PV system performance is therefore increasingly important for both project design and grid integration. Modern PV yield assessments commonly rely on multi-year meteorological datasets and probabilistic exceedance metrics (e.g., P50/P90) to quantify energy yield uncertainty for project financing. However, the implications of long-term solar resource variability for PV system design choices and high-adoption grid conditions remain less well characterized for rapidly expanding markets such as Brazil. In particular, understanding how weather-driven variability propagates into PV production distributions and capacity factor expectations is important for evaluating curtailment exposure, deployment strategies, and storage requirements in regions experiencing rapid growth of distributed and utility-scale PV. Seasonal and interannual variability in atmospheric conditions can produce substantial fluctuations in monthly PV energy production, which propagate into uncertainty in annual energy yield and capacity factor expectations. Characterizing this variability using long-term meteorological datasets allows probabilistic estimation of PV system performance and provides improved insight into the range of expected PV energy outcomes. This study explores the use of long-term satellite-derived meteorological data from the National Solar Radiation Database (NSRDB) to evaluate the variability of photovoltaic system performance across multiple locations in Brazil. Using a 27-year dataset (1998-2024), PV system simulations are performed to characterize the distribution of annual and seasonal capacity factors and energy yield outcomes, while propagating key sources of meteorological variability and model uncertainty through the PV modeling chain. The analysis also investigates the sensitivity of PV performance outcomes to key system design assumptions within the PV modeling chain, including tracking configuration and system sizing parameters. The resulting probabilistic performance characterization provides insight into how weather-driven variability influences PV production expectations and capacity factor distributions. These results provide a foundation for evaluating how weather-driven variability interacts with high PV adoption and potential storage or curtailment mitigation strategies.

14 SOLAR ENERGY↗
DOE OSTI2023

Techno-economic Analysis of the Cryogenic Flux Capacitor Compared to Other forms of Hydrogen Production and Storage

The Cryogenic Flux Capacitor (CFC) is a cold, dense energy storage core that is being studied in the cryo-compressed, about 300 bar and 80K, region of gaseous hydrogen (GH2) storage and liquid hydrogen (LH2) region near the normal boiling point. The hydrogens storage is improved by physically bonding the molecules within the nanoscale pores of the aerogel composite blanket material. The process of bonding or debonding is governed by principles of physical adsorption (physisorption) and thermodynamics. The large surface area afforded by the nanoporous aerogel (~1,000 m2/g) allows its storage performance to easily exceed capacities of high-pressure GH2 storage for an equivalent volume. With the integrated aerogel, subscale tests have shown that storage is increased by about 49% over a simple tank filled with GH2 at the same operating temperature and pressure. For LH2 conditions, the CFC is shown to operate at equivalent densities. For the techno-economic analysis (TEA), the source of hydrogen is compared between onsite steam methane reforming (SMR) and onsite solar photovoltaic (PV) panels providing power to electrolyzers to produce GH2. The TEA compares pure hydrogen burning in a combined cycle gas turbine (CCGT) to hydrogen fuel cells with an overall net power output of 650 MW. The SMR system uses natural gas as an input and includes a carbon capture and storage (CCS) system. The levelized cost of electricity is developed based on the capital cost and operating cost of the systems. Sensitivities are discussed around the cost of natural gas, ranging from 1.93 USD per MMBTU to 6.75 USD per MMBTU, and carbon dioxide disposal, ranging from 7 USD per tonne to 10 USD per tonne. For comparison to the conventional CCGT baseline, a baseload scenario is adopted with 85% capacity factor. The results of the study show that onsite hydrogen generation from SMR is about 1 to 3 USD per kg over the life of the plant and the PV hydrogen production produces at 4 to 5 USD per kg. The cost of storage for CFC is compared to other systems, including high-pressure GH2 and atmospheric LH2. The system is shown to provide the lowest costs for all these options at the grid scale, due to its higher capacity than high-pressure GH2 and ability to operate at 80K, receiving refrigeration from liquid nitrogen systems reducing capital and operating costs when compared LH2 storage systems. SMR is competitive with CCGT at the gas prices, both of which have lower LCOE than the PV system. When accounting for variability in gas prices, the PV and electrolyzer system is less sensitive to these changes and provides the lowest LCOE across the whole range.

08 HYDROGEN↗
DOE OSTI2019

A Model for Joint Probabilistic Forecast of Solar Photovoltaic Power and Outdoor Temperature

In this paper, a stochastic model is proposed for a joint statistical description of solar photovoltaic (PV) power and outdoor temperature. The underlying correlation emerges from solar irradiance that is responsible in part for both the variability in solar PV power and temperature. The proposed model can be used to capture the uncertainty in solar PV power and its correlation with the electric power consumption of thermostatically controlled loads. First, a model for solar PV power that explicitly incorporates the stochasticity due to clouds via a regime-switching process between the three classes of sunny , overcast and partly cloudy is proposed. Then, the relationship between temperature and solar power is postulated using a second-order Volterra model. This joint modeling is leveraged to develop a joint probabilistic forecasting method for solar PV power and temperature. Real-world datasets that include solar PV power and temperature measurements in California are analyzed and the effectiveness of the joint model in providing probabilistic forecasts is verified. The proposed forecasting methodology outperforms several reference methods thus portraying that the underlying correlation between temperature and solar PV power is well defined and only requires a simple lower-complexity sampling space.

14 SOLAR ENERGY↗
DOE OSTI2023

PV Module Operating Temperature - Data and Resources

The Photovoltaic Systems Evaluation Laboratory (PSEL) at Sandia National Laboratories (SNL) in Albuquerque, NM has an extensive test site where PV modules and other system components are deployed and monitored for testing and evaluation. For this dataset PV Performance Labs has assembled one year of measurements from the Systems Long-Term Evaluation (SLTE) project (formerly known as PV Lifetime) providing the main variables needed to investigate and validate PV module operating temperature models: irradiance, ambient temperature, wind speed and back-of-module temperature. For use with more advanced thermal modeling, an estimate of down-welling long-wave radiation is also included.

14 SOLAR ENERGY↗
DOE OSTI2023

Impact of system parameters and geospatial variables on the reliability of residential systems with PV and energy storage

A reliable power supply is the foundation of modern society, enabling technologies used to function within a society. Residential systems are places where the end users directly consume power, enabling technologies to sustain life. With the emergence of behind-the-meter resources, the end-users have some control over power supply reliability. The intermittency and variability of these resources impact residential system reliability. In this work, we study the reliability performance of a grid-supplemented residential system with behind-the-meter Distributed Energy Resources(DER) subject to various system parameters and geospatial variables. We propose a multistate reliability model for the behind-the-meter microinverter-based Photo Voltaic (PV) system and integrated inverter-based energy storage (ES) system. A sequential Monte Carlo method is then presented to evaluate the reliability indices for the residential system with behind-the-meter DERs as the main supply and the grid as the backup. The sequential Monte Carlo method is used to analyze the reliability performance of the residential model at the top 100 populous counties of the United States, where actual load and solar irradiance data at the counties is used. In the analysis, the sensitivity of indices to system parameters such as DER size and the sensitivity of the indices to climate zone and gross horizontal irradiation (GHI), which affects the load conditions and the PV output of the residential system, is performed. The analysis results show that the system parameters and the geospatial variables significantly impact the residential system’s reliability. The insights from this analysis will be of immense value to the distribution system planners to provide zone-specific guidelines for DER system sizing and toward the evolution of utility business models. Finally, the methodology developed can be used to extend the analysis to other locations.

14 SOLAR ENERGY↗
DOE OSTI2018

Consequences of neglecting the interannual variability of the solar resource: A case study of photovoltaic power among the Hawaiian Islands

The interannual variability of the solar irradiance and meteorological conditions are often ignored in favor of single-year data sets for modeling power generation and evaluating the economic value of photovoltaic (PV) power systems. Yet interannual variability significantly impacts the generation from one year to another of renewable power systems such as wind and PV. Consequently, the interannual variability of power generation corresponds to the interannual variability of capital returns on investment. The penetration of PV systems within the Hawaiian Electric Companies' portfolio has rapidly accelerated in recent years and is expected to continue to increase given the state's energy objectives laid out by the Hawaii Clean Energy Initiative. We use the National Solar Radiation Database (1998-2015) to characterize the interannual variability of the solar irradiance and meteorological conditions across the State of Hawaii. These data sets are passed to the National Renewable Energy Laboratory's System Advisory Model (SAM) to calculate an 18-year PV power generation data set to characterize the variability of PV power generation. We calculate the interannual coefficient of variability (COV) for annual average global horizontal irradiance (GHI) on the order of 2% and COV for annual capacity factor on the order of 3% across the Hawaiian archipelago. Regarding the interannual variability of seasonal trends, we calculate the COV for monthly average GHI values on the order of 5% and COV for monthly capacity factor on the order of 10%. We model residential-scale and utility-scale PV systems and calculate the economic returns of each system via the payback period and the net present value. We demonstrate that studies based on single-year data sets for economic evaluations reach conclusions that deviate from the true values realized by accounting for interannual variability.

14 SOLAR ENERGY↗
DOE OSTI2022

The economic value of photovoltaic performance loss mitigation in electricity spot markets

Photovoltaic (PV) performance is affected by reversible and irreversible losses. These can typically be mitigated through responsive and proactive operations and maintenance (O&M) activities. However, to generate profit, the cost of O&M must be lower than the value of the recovered electricity. This value depends both on the amount of recovered energy and on the electricity prices, which can vary significantly over time in spot markets. The present work investigates the impact of the electricity price variability on the PV profitability and on the related O&M activities in Italy, Portugal, and Spain. Here, it is found that the PV revenues varied by 1.6 × to 1.8 × within the investigated countries in the last 5 years. Moreover, forecasts predict higher average prices in the current decade compared to the previous one. These will increase the future PV revenues by up to 60% by 2030 compared to their 2015–2020 mean values. These higher revenues will make more funds available for better maintenance and for higher quality components, potentially leading to even higher energy yield and profits. Linearly growing or constant price assumptions cannot fully reproduce these expected price trends. Furthermore, significant price fluctuations can lead to unexpected scenarios and alter the predictions.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
NASA NTRS2000

Development of a Battery-Free Solar Refrigerator

Recent technology developments and a systems engineering design approach have led to the development of a practical battery-free solar refrigerator as a spin-off of NASA's aerospace refrigeration research. Off-grid refrigeration is a good application of solar photovoltaic (PV) power if thermal storage is incorporated and a direct connection is made between the cooling system and the PV panel. This was accomplished by integrating water as a phase-change material into a well insulated refrigerator cabinet and by developing a microprocessor based control system that allows direct connection of a PV panel to a variable speed compressor. This second innovation also allowed peak power-point tracking from the PV panel and elimination of batteries from the system. First a laboratory unit was developed to prove the concept and then a commercial unit was produced and deployed in a field test. The laboratory unit was used to test many different configurations including thermoelectric, Stirling and vapor compression cooling systems. The final configuration used a vapor compression cooling cycle, vacuum insulation, a passive condenser, an integral evaporator/ thermal storage tank, two 77 watt PV panels and the novel controller mentioned above. The system's only moving part was the variable speed BD35 compressor made by Danfoss. The 365 liter cabinet stayed cold with as little as 274 watt-hours per day average PV power. Battery-free testing was conducted for several months with very good results. The amount of thermal storage, size of compressor and power of PV panels connected can all be adjusted to optimize the design for a given application and climate. In the commercial unit, the high cost of the vacuum insulated refrigerator cabinet and the stainless steel thermal storage tank were addressed in an effort to make the technology commercially viable. This unit started with a 142 liter, mass-produced chest freezer cabinet that had the evaporator integrated into its inner walls. Its compressor was replaced with a Danfoss DC compressor slightly larger than the one used in the laboratory unit. The control system was integrated onto a single electronics card and packaged with its starting capacitors. The water for thermal storage was placed behind a liner that was made to fit inside the original factory liner. The original condenser was also augmented with additional surface area to improve performance. PV panels with a total rated power of 180 watts were used. The unit was tested with very successful results in an outside ambient environment, demonstrating its potential for widespread use in many off-grid applications for solar refrigeration.

Ewert, Michael K.↗
DOE OSTI2020

Life-Cycle Cost and Optimization of PV Systems Based on Power Duration Curve with Variable Performance Ratio and Availability

A new derivation of Power Duration Curve is presented and applied to analysis of photovoltaic system life cycle cost. The effect of non-unity Performance Ratio and non-unity Availability on the power duration curve are illustrated. The effect of these two key performance indicators on calculation of life cycle cost and PV system design optimization (optimal DC/AC ratio) are illustrated as example applications of the method.

14 SOLAR ENERGY↗