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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 127 records · Page 7
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DOE OSTI2021

Simulation of PV Variability as a Function of PV Generation and Plant Size

The deployment of photovoltaic (PV) systems continues to show significant expansion; however, this growth has brought added attention to issues around the variability of the solar resource. Both spatial and temporal variability exist. Temporal scales can range from the sub-second to multiyear, whereas spatial scales can range from a few meters to tens of kilometers. There are multiple methods described in the literature to quantify PV variability at various spatial and temporal scales. This study focuses on short-term temporal variability and uses similar approaches with the addition of PV plant size a parameter to quantify variability. The method employed here incorporates the normalization of clear-and cloudy-sky conditions and PV plant size to quantify nominal variability metrics. The distribution and fluctuations of these metrics provide relevant information that is useful for system operations. The National Solar Radiation Database (NSRDB) is used to simulate PV variability as a function of PV generation and plant size. Hypothetical but realistic system information at 33 locations is used to model PV generation by feeding NSRDB solar irradiance data to the National Renewable Energy Laboratory’s System Advisor Model (SAM). Over the selected region, it is found that the aggregated ramp rates for the 1-minute data are associated with standard deviations ranging from 0.002–0.055 on a daily basis; however, hourly intervals induce higher aggregated ramp rates than the other timescales. Even though minute-to-minute variations are significant for the 1-minute time-scale, the standard deviation aggregated into a daily metric is smaller because of the cancellation of values.

irradiance↗
DOE OSTI2017

Solar Field Optical Characterization at Stillwater Geothermal/Solar Hybrid Plant

Concentrating solar power (CSP) can provide additional thermal energy to boost geothermal plant power generation. For a newly constructed solar field at a geothermal power plant site, it is critical to properly characterize its performance so that the prediction of thermal power generation can be derived to develop an optimum operating strategy for a hybrid system. In the past, laboratory characterization of a solar collector has often extended into the solar field performance model and has been used to predict the actual solar field performance, disregarding realistic impacting factors. In this work, an extensive measurement on mirror slope error and receiver position error has been performed in the field by using the optical characterization tool called Distant Observer (DO). Combining a solar reflectance sampling procedure, a newly developed solar characterization program called FirstOPTIC and public software for annual performance modeling called System Advisor Model (SAM), a comprehensive solar field optical characterization has been conducted, thus allowing for an informed prediction of solar field annual performance. The paper illustrates this detailed solar field optical characterization procedure and demonstrates how the results help to quantify an appropriate tracking-correction strategy to improve solar field performance. In particular, it is found that an appropriate tracking-offset algorithm can improve the solar field performance by about 15%. The work here provides a valuable reference for the growing CSP industry.

14 SOLAR ENERGY↗
DOE OSTI2022

Dispatch optimization of a concentrating solar power system under uncertain solar irradiance and energy prices

The integration of thermal energy storage into a concentrating solar power system allows for mitigating some of the risk associated with uncertain solar irradiance and uncertain energy prices. We solve a 48 h dispatch optimization model with continually updated conditional point forecasts of both direct normal irradiance (DNI) and electricity prices with a rolling-horizon scheme at hourly resolution over the course of a year. Joint, conditional forecasts for DNI and prices are formed using an autoregressive moving-average time series model with exogenous weather predictors. We guide dispatch using a mixed-integer programming model, but in order to evaluate performance we use the System Advisor Model (SAM) of the National Renewable Energy Laboratory. SAM is a techno-economic simulation model that accounts for plant thermodynamics with higher fidelity. Our conditional DNI forecasts improve annual revenue by 4%–12% over using historical forecasts based on data from previous years. Conditional price forecasts improve annual revenue by 6%–19% in the real-time market over analogous historical forecasts. Updating these forecasts every six hours, rather than every 24 h, further improves annual revenue by 5%–6%. Here, we also investigate a method that values terminal inventory in our dispatch optimization model, again when used in a rolling-horizon scheme.

14 SOLAR ENERGY↗
DOE OSTI2022

Technoeconomic Cost Analysis of NREL Concentrating Solar Power Gen3 Liquid Pathway

The National Renewable Energy Laboratory is leading the liquid (molten salt) power tower pathway for the U.S. Department of Energy's concentrating solar power Gen3 initiative. The Gen3 liquid pathway required updated designs to three major components: the tower and receiver, the thermal energy storage tanks, and the power cycle. We assume a 100 MWe net system output and used the System Advisor Model (SAM) to complete a technoeconomic cost analysis of the Gen3 liquid pathway design and estimate its levelized cost of electricity. This paper summarizes the methodology and results of that analysis.

chloride salt↗
DOE OSTI2024

Short-Term Probabilistic Solar Forecasting via Reinforcement Learning over ECMWF

In this paper, we present an innovative reinforcement learning approach for short-term solar forecasting, leveraging data from the European Centre for Medium-Range Weather Forecasts (ECMWF). The methodology begins with the application of the System Advisor Model (SAM) to transform various ECMWF numerical weather prediction members into predictive photovoltaic power generation. To enhance the precision of deterministic forecasting, we introduce a dynamic model selection algorithm based on Q-learning. This algorithm dynamically identifies and utilizes the most accurate ensemble member for forecasting purposes. Furthermore, we employ a support vector regression surrogate model with a Gaussian distribution to generate probabilistic forecasts, providing a holistic view of solar energy generation uncertainty. To expedite the training process and make it more practical for real-world applications, we integrate a rolling update workflow. This innovative workflow reduces the training period from months to a mere 19 days, making our method highly efficient. Numerical results of the case study show that in comparison to benchmark models, the proposed method improves the deterministic and probabilistic solar forecasting accuracy by up to 40.84% and 48.42%, respectively.

ensemble forecasting↗
DOE OSTI2018

Simulating PV System Performance with Component Reliability Distributions

A feature developed by Sandia National Laboratories (SNL) for simulating PV system component reliability has been integrated into the National Renewable Energy Laboratory's System Advisor Model (SAM). The PV Reliability Performance Model (PV-RPM) uses fault/failure and repair distributions to estimate impacts to power, energy production and operational costs. Realization results include the number of failures per component, failure rate, O&M costs incurred based on maintenance strategies, and energy lost based on the failure estimates. As the inputs are inherently uncertain and based on probability distributions, the model realizations include statistical confidence intervals to help bracket the probability that the sample interval contains the true mean value of the variable in question. This paper presents the current validation efforts and provides detail on how this feature is utilized in a PV performance model.

14 SOLAR ENERGY↗
DOE OSTI2020

Improvements to PVWatts for Fixed and One-Axis Tracking Systems

This work presents improvements to the widely used NREL PVWatts photovoltaic system energy model to improve modeling accuracy for typical fixed and one axis system designs. The aim is to calculate losses in the PV system assuming typical modern system design practices, while maintaining simplicity by keeping the required set of input parameters small. These improvements allow users to more credibly and quickly evaluate competing system designs in early stage feasibility. Common submodels for module cover, spectral, snow, tracker, transformer, plant controller, and self-shading losses, in addition to a bifacial gain option, are incorporated into the PVWatts model, and are shown to improve PVWatts' system performance prediction capabilities without major impact to ease of use. We anticipate including these improvements in a future release of NREL's open source PVWatts code, and some of the features may become available in the System Advisor Model (SAM) desktop software as well as the popular PVWatts web application.

14 SOLAR ENERGY↗
DOE OSTI2023

Design and Techno-Economic Analysis of a 150-MW Hybrid CSP-PV Plant

The interest in concentrated solar power (CSP) has increased significantly over the years since it is dispatchable and requires thermal storage instead of electric storage. When compared to photovoltaics (PV), CSP has a higher Levelized Cost of Electricity (LCOE). In this paper, we present the design of a hybrid power plant using CSP and PV technologies. The hybrid system offers a lower LCOE than CSP but will still have dispatchability. The 150-MW hybrid system consists of 100-MW CSP and 50-MW PV capacity. Furthermore, the CSP system (central receiver system) has a molten salt-based thermal storage of 12 hours. The geographical focus of this study is Pakistan, which is a developing country struggling with energy crises, but which has high solar potential. The hybrid system is modeled using the System Advisor Model (SAM). The results show that by hybridizing CSP with PV, the LCOE can be reduced by 18.5%.

concentrated solar power↗
DOE OSTI2022

Demonstrating solarpilot ’s Python Application Programmable Interface Through Heliostat Optimal Aimpoint Strategy Use Case

solarpilot is a software package that generates solar field layouts and characterizes the optical performance of concentrating solar power (CSP) tower systems. solarpilot was developed by the National Renewable Energy Laboratory (NREL) as a stand-alone desktop application but has also been incorporated into NREL’s System Advisor Model (SAM) in a simplified format. Prior means for user interaction with solarpilot have included the application’s graphical interface, the SAM routines with limited configurability, and through a built-in scripting language called “LK.” This article presents a new, full-featured, python-based application programmable interface (API) for solarpilot, which we hereafter refer to as CoPylot. CoPylot enables python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. CoPylot’s enables CSP researchers to perform analysis that was previously not possible through solarpilot’s existing interfaces. This article discusses the capabilities of CoPylot and presents a use case wherein we populate a model that obtains optimal solar field aiming strategies.

14 SOLAR ENERGY↗
DOE OSTI2016

Revisiting Parabolic Trough Concentrators for Industrial Process Heat in the United States

After significant interest in the 1970s, but relatively few deployments, the use of concentrating solar collectors for thermal applications, including enhanced oil recovery, desalination, and industrial process heat (IPH), is again increasing in global interest. In particular, recent advances in collector design and manufacturing have led to reduced cost per square meter of aperture area. In this study, analysis of a modern parabolic trough that is suited for use in small solar IPH (SIPH) applications predicts that the installed solar field cost can be as low as $170/m2. A slightly higher cost of $200/m2 is estimated for facilities typical of a SIPH plant size. Full project costs will include additional costs for contingency, piping and heat exchanger interface, and project indirect costs. The cost for solar-generated heat by SIPH is quantified by defining the levelized cost of heat (LCOH). California offers a favorable environment for SIPH given its good insolation, gas prices typically higher than the national average, and policies promoting solar-thermal deployment. Given historically low gas prices, competing with natural gas remains the primary challenge to deployment. However, this study finds that the solar LCOH for many regions in California is lower than the LCOH from natural gas, using a representative installed solar hardware price and the average price for industrial natural gas in California. Lastly, modification are in progress to the parabolic trough model within NREL's System Advisor Model (SAM) to allow users to more easily predict performance for these steam-generation applications.

14 SOLAR ENERGY↗
DOE OSTI2017

LK Scripting Language [SWR-16-07]

The LK scripting language is a simple and fast computer programming language designed for easy integration with existing software to enable automation of tasks. The LK language is used by NREL’s System Advisor Model (SAM), the SAM Software Development Kit (SDK), and SolTrace products. LK is easy extensible and adaptable to new software due to its small footprint and is designed to be statically linked into other software. It is written in standard C++, is cross-platform (Windows, Linux, and OSX), and includes optional portions that enable direct integration with graphical user interfaces written in the open source C++ wxWidgets Version 3.0+ toolkit.

Source record↗
DOE OSTI2020

reV (The Renewable Energy Potential Model - Open Source) [SWR-21-59, SWR-20-20 and SWR-17-34]

The Renewable Energy Potential (reV) model is a platform for the detailed assessment of renewable energy resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP), and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for the assessment of resource potential, technical potential, and supply curves at varying levels of detail. The platform runs on the National Renewable Energy Laboratory’s (NREL’s) high-performance computing system, providing scalable and efficient performance from a single location up to a continent, for a single year or decades of time-series resource data. Coupled with NREL’s System Advisor Model (SAM), reV supports resource assessments from 5-minute to hourly temporal resolutions and supports the analysis of long-term (i.e., year-on-year) variability of renewable generation (e.g., interannual variability and exceedance probabilities).

Maclaurin, Galen↗
DOE OSTI2016

Solar Process Heat Potential in California, USA

The cost for solar industrial process heat (SIPH) is quantified by defining the levelized cost of heat (LCOH). The state of California, in the United States offers a favorable environment for SIPH given its excellent insolation, industrial gas prices typically higher than the national average, and policies promoting solarthermal deployment. Given historically low gas prices, competing with natural gas remains the primary challenge to deployment. However, this study finds that the solar LCOH from Concentrating Solar Power (CSP) collectors for many regions in California is expected to be lower than the LCOH from natural gas, using a representative installed solar hardware price and the average price for industrial natural gas in California. Lastly, modifications are in progress to the parabolic trough and Direct Steam Generation (DSG) trough and Linear Fresnel models within NREL's System Advisor Model (SAM) to allow users to more easily predict performance for these industrial heat and steam-generation applications.

14 SOLAR ENERGY↗
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 OSTI2018

Global Value Chain and Manufacturing Analysis on Geothermal Power Plant Turbines

Currently, geothermal project developers customize the size of the power plant to fit the resource being developed. The turbine is designed and sized to optimize efficiency and resource utilization for electricity production; most often, other power plant components are then chosen to complement the turbine design. These custom turbine designs demand one-off manufacturing processes, which result in higher manufacturing setup costs, longer lead-times, and higher capital costs overall in comparison to larger-volume line manufacturing processes. In contrast, turbines produced in standard increments, manufactured in larger volumes, could result in lower costs per turbine. In this study, we developed a manufacturing cost model to identify requirements for equipment, facilities, raw materials, and labor. Then we used discounted cash flow (DCF) analysis to calculate the minimum sustainable price (MSP). We analyzed three different cases 1) 1 MWe geothermal ORC turboexpander 2) 5 MWe ORC turboexpander and 3) 20 MWe geothermal steam turbine, and calculated the cost of manufacturing the major components, such as the impellers/blades, shaft/rotor, nozzles, inlet guide lanes, disks, and casings. The results showed that MSP could highly vary between 893 /kW and 30 /kW based on turbine size, standardization and volume of manufacturing. The analysis also showed that the economy of scale applies both to the size of the turbine and the number manufactured in a single run. As an example, the unit price of a 5 MW standard design turbine could be 150 /W cheaper than the custom design. Sensitivity analysis indicated that these savings come largely from reduced labor costs for design and engineering and manufacturing setup. In addition to manufacturing cost savings, there is a delivery time saving up to 10 months, which could have a positive effect to construction financing operation time. Another advantage of these standard turbines is their adaptability to different geothermal systems by operating at off-design conditions. We also modeled off-design turbine efficiencies within the commercially-favorable operating range of a standard ORC compared to custom-designed ORC equipment. Than we used these performance calculations and power output capacities in a discounted cash flow (DCF) analysis of plant operations and financing, thereby creating representative techno economic models of a total geothermal power plant. I this scope we used NREL's System Advisor model (SAM) and performed DCF analysis of standard and custom design turbines using results from IPSEpro over range of temperatures of interest. In this scope 63 different off-design cases were analyzed. This helped us to answer the question; 'Can today's capital cost savings compensate the future revenue losses due to lower electricity generation?' The results showed that, the net capital cost savings for the modeled 5 MW case study may reach up to plus 1,298,672. Also, the difference between NPV of standard design and custom design turbine could reach up to 733,940. To sum up, when all the things mentioned above are taken into consideration, it can be said that, the standard turbines are competitive over a wide range of temperatures and flow rates and the give positive NPV differences for certain cases near their design point.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Reducing PV Performance Uncertainty by Accurately Quantifying the PV Resource

Reducing uncertainty in the prediction and/or verification of photovoltaic (PV) plant output can directly increase the expected return on investment for each party in a contract, likely leading to more favorable terms for the contract, including a possible reduction in interest rates. Note that a reduction of 1% in the interest rate is estimated to reduce the levelized cost of energy by approximately 0.5 cents/kWh. This project developed a capability to provide spectrally resolved solar information from the National Solar Radiation Data Base (NSRDB), effectively providing 20 years of half-hourly data for all of the United States at a 4-km by 4-km spatial resolution. These data are now available freely to users directly through a geographic information system-based Web interface (https://nsrdb.nrel.gov) as well as through an application programming interface. Users of these data can conduct more accurate prefeasibility studies and assess multiple PV technologies. It is expected that widely used models such as PVSyst, the National Renewable Energy Laboratory's System Advisor Model (SAM), and PlantPredict, designed by First Solar, will develop the capabilities to use these spectral data.

14 SOLAR ENERGY↗
DOE OSTI2021

The Renewable Energy Potential (reV) Model: A Geospatial Platform for Technical Potential and Supply Curve Modeling

The Renewable Energy Potential (reV) model is a platform for detailed assessment of renewable energy (RE) resources and their geospatial intersection with grid infrastructure and land use characteristics. The reV model currently supports photovoltaic (PV), concentrating solar power (CSP) and land-based wind turbine technologies. Modules in the reV framework function at different spatial and temporal resolutions, allowing for assessment of resource potential, technical potential and supply curves at varying levels of detail. The platform runs on NREL's High Performance Computing system, providing scalable and efficient performance from a single location all the way up to continental scales, for a single year or decades of time series resource data. Coupled with NREL's System Advisor Model (SAM), reV supports resource assessment from 5-minute to hourly temporal resolution and provides for analysis of long-term (i.e., year-on-year) variability of RE generation (e.g., interannual variability and exceedance probabilities). Technical potential is measured as a function of resource potential and limitations put on developable land area defined by the user. For example, the user can limit development by land ownership, terrain, land use/cover, and urban areas, as well as custom inputs. Technology, grid interconnection and operation costs, based on the latest market data and future projections, are also embedded in the model. The supply curve module is a spatial sorting algorithm based on plant siting, grid interconnection cost, and regional competition, which provides a geographically discrete estimate of levelized cost of electricity (LCOE) and supply (i.e., capacity) for specific renewable technologies. The reV model currently provides broad coverage across North America, South and Central Asia, South America and South Africa to inform national- and international-scale analyses as well as regional infrastructure and deployment planning.

13 HYDRO ENERGY↗
DOE OSTI2018

National Solar Radiation Database (NSRDB)

The National Solar Radiation Database (NSRDB) is a serially complete collection of meteorological and solar irradiance data sets for the United States and a growing list of international locations for 1998-2023. The NSRDB is updated annually and provides foundational information to support U.S. Department of Energy programs, research, industry and the general public. The NSRDB provides time-series data at 30-minute resolution of resource averaged over surface cells of 0.038 degrees in both latitude and longitude, or nominally 4 km in size. Additionally time series data at 5 minutes for the US and 10 minutes for North, Central and South America at 2 km resolution are produced from the next generation of GOES satellites and made available from 2019. The solar radiation values represent the resource available to solar energy systems. The data was created using cloud properties which are generated using the AVHRR Pathfinder Atmospheres-Extended (PATMOS-x) algorithms developed by the University of Wisconsin. Fast all-sky radiation model for solar applications (FARMS) in conjunction with the cloud properties, and aerosol optical depth (AOD) and precipitable water vapor (PWV) from ancillary source are used to estimate solar irradiance (GHI, DNI, and DHI). The Global Horizontal Irradiance (GHI) is computed for clear skies using the REST2 model. For cloud scenes identified by the cloud mask, FARMS is used to compute GHI and FARMS DNI is used to compute the Direct Normal Irradiance (DNI). The PATMOS-X model uses radiance images in visible and infrared channels from the Geostationary Operational Environmental Satellite (GOES) series of geostationary weather satellites. Ancillary variables needed to run REST2 and FARMS (e.g., aerosol optical depth, precipitable water vapor, and albedo) are derived from NASA's Modern Era-Retrospective Analysis (MERRA-2) dataset. Temperature and wind speed data are also derived from MERRA-2 and provided for use in NREL's System Advisor Model (SAM) to compute PV generation.

aerosol↗