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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 OSTI2015

System Advisor Model (SAM) Enhancements for CSP

Previous work led by NREL has produced a suite of hourly CSP performance and annual financial models that are currently implemented in SAM. These analysis tools are used within the DOE national laboratory system and worldwide by researchers, industry members, academics, and policymakers to determine the potential performance and financial impact of renewable energy technologies. SAM includes models for the following CSP solar-field technologies: parabolic troughs, both molten-salt and direct-steam central receivers, dish/Stirling, and direct-steam linear Fresnel systems. SAM also includes a generic solar system model that represents the solar field using a set of optical efficiency values for different sun angles and can be used for any solar technology that uses solar energy to generate steam for electric power generation. Each technology model requires information about the receiver and collector, power block, thermal storage, and operating conditions to predict component and overall system performance. The CSP models also include an optional auxiliary fossil backup system, as appropriate. This project aims to maintain SAM’s unique ability to efficiently model pertinent CSP technologies by improving the functionality of SAM and improving current or creating new technology models.

14 SOLAR ENERGY↗
DOE OSTI2016

Using Measured Plane-of-Array Data Directly in Photovoltaic Modeling: Methodology and Validation

Measured plane-of-array (POA) irradiance may provide a lower-cost alternative to standard irradiance component data for photovoltaic (PV) system performance modeling without loss of accuracy. Previous work has shown that transposition models typically used by PV models to calculate POA irradiance from horizontal data introduce error into the POA irradiance estimates, and that measured POA data can correlate better to measured performance data. However, popular PV modeling tools historically have not directly used input POA data. This paper introduces a new capability in NREL's System Advisor Model (SAM) to directly use POA data in PV modeling, and compares SAM results from both POA irradiance and irradiance components inputs against measured performance data for eight operating PV systems.

14 SOLAR ENERGY↗
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 OSTI2015

SAM Photovoltaic Model Technical Reference

This manual describes the photovoltaic performance model in the System Advisor Model (SAM). The U.S. Department of Energy’s National Renewable Energy Laboratory maintains and distributes SAM, which is available as a free download from https://sam.nrel.gov. These descriptions are based on SAM 2015.1.30 (SSC 41).

14 SOLAR ENERGY↗
DOE OSTI2018

SAM Photovoltaic Model Technical Reference 2016 Update

This manual describes the photovoltaic performance model in the System Advisor Model (SAM) software, Version 2016.3.14 Revision 4 (SSC Version 160). It is an update to the 2015 edition of the manual, which describes the photovoltaic model in SAM 2015.1.30 (SSC 41). This new edition includes corrections of errors in the 2015 edition and descriptions of new features introduced in SAM 2016.3.14, including: 3D shade calculator Battery storage model DC power optimizer loss inputs Snow loss model Plane-of-array irradiance input from weather file option Support for sub-hourly simulations Self-shading works with all four subarrays, and uses same algorithm for fixed arrays and one-axis tracking Linear self-shading algorithm for thin-film modules Loss percentages replace derate factors. The photovoltaic performance model is one of the modules in the SAM Simulation Core (SSC), which is part of both SAM and the SAM SDK. SAM is a user-friedly desktop application for analysis of renewable energy projects. The SAM SDK (Software Development Kit) is for developers writing their own renewable energy analysis software based on SSC. This manual is written for users of both SAM and the SAM SDK wanting to learn more about the details of SAM's photovoltaic model.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2019

Case Study of a Californian Brewery to Potentially Use Concentrating Solar Power for Renewable Heat Generation

With the increasing relevance and acceptance of solar thermal for industrial process heat (IPH), a case study for a proposed concentrating solar power (CSP) integration into an existing Californian brewery is highlighted. The thermal generation from a 1 MWth solar field was modelled in the System Advisor Model (SAM), and the economics considered the viability of the project where different variables such as the installed solar field cost were investigated on metrics such as Net Present Value (NPV) and payback period. For the Base Case, without the 30% Investment Tax Credit (ITC), and using a 2017 Federal income tax rate of 35%, the project’s NPV reached $0 (i.e., the project becomes viable), when the installed solar field cost reached $158/m2. With the 30% ITC and 35% tax, the solar field installed cost is estimated at $324/m2 to obtain an NPV = $0. The biggest influences on project viability measured by NPV were the project life, Federal tax rate, and the solar field cost.

case study↗
DOE OSTI2019

DC-Connected Solar Plus Storage Modeling for Behind-the-Meter Systems in SAM

A detailed model for PV plus DC-connected batteries was developed. This model was compared to an existing AC connected battery model in the System Advisor Model (SAM) tool using a hypothetical Honolulu residence with a PV plus storage system. The stand-alone PV system was shown to provide the most value; however due to the increased difficulty of PV systems being approved for grid interconnection in Hawaii, it is valuable to install an AC or DC connected battery to enable full PV self-consumption under a less restrictive interconnection option. In that case both battery systems enabled full PV self consumption at the residence, requiring no power to be exported to the grid. The DC-connected battery system captured energy from the PV array which would have also been clipped due to inverter limitations. For a PV system with DC/AC ratio of 1.23, the battery configurations produced net-present values within 1% of each other, but when the DC/AC ratio increased to 1.85, the DC-connected system increased the net-present value by 13%.

14 SOLAR ENERGY↗
DOE OSTI2024

Presenting a Model to Predict Changing Snow Albedo for Improving Photovoltaic Performance Simulation

As photovoltaic (PV) deployment increases worldwide, PV systems are being installed more frequently in locations that experience snow cover. The higher albedo of snow, relative to the ground, increases the performance of PV systems in northern and high-altitude locations by reflecting more light onto the PV modules. Accurate modeling of the snow’s albedo can improve estimates of PV system production. Typical modeling of snow albedo uses a simple two-value model that sets the albedo high when snow is present, and low when snow is not present. However, snow albedo changes over time as snow settles and melts and a binary model does not account for transitional changes, which can be significant. Here, we present and validate a model for estimating snow albedo as it changes over time. The model is simple enough to only require daily snow depth and hourly average temperature data, but can be improved through the addition of site-specific factors, when available. We validate this model to quantify its ability to more accurately predict snow albedo and compare the model’s performance against satellite imagery-based methods for obtaining historical albedo data. In addition, we perform modeling using the System Advisor Model (SAM) to show the impact of changes in albedo on energy modeling for PV systems. Overall, our albedo model has a significantly improved ability to predict the solar insolation on PV modules in real time, especially on bifacial PV modules where reflected irradiance plays a larger role in energy production.

Pike, Christopher (ORCID:0000000155888033)↗
DOE OSTI2021

Understanding Solar Photovoltaic System Performance: An Assessment of 75 Federal Photovoltaic Systems

This report presents a performance analysis of 75 photovoltaic systems based on PV system production data collected as part of a FEMP Federal PV Performance Assessment project combined with co-incident insolation, and ambient temperature to analyze how actual performance compares with a performance model. FEMP collaborated with 17 Federal agencies and sub-agencies to collect the information required to analyze the performance of each system. The systems represent a total capacity of 30,714 kW and range in size from 1 kW to 4,043 kW, with an average size of 410 kW, and were installed between 2011 and 2020. The data is analyzed for Key Performance Indicators, Availability, Performance Ratio and Energy Ratio by comparing the measured production data to model production data. The System Advisor Model (SAM) combines a description of the system (such as inverter capacity, de-rating for temperature, balance-of-system efficiency) with environmental parameters (coincident solar and temperature data) to calculate predicted performance. The performance metrics are calculated by lining up the measured production data with the model estimate on an hour-by-hour, day-by-day, or month-by-month basis (depending on the interval resolution of the production data). A report with system description, photo of the system, special assumptions made for the site, graph of measured production and model production, table of key performance indicators, and links to O&M resources that might improve performance was produced and delivered to site and agency staff with a short on-line briefing.

14 SOLAR ENERGY↗
DOE OSTI2018

Bifacial Simulation in SAM

A bifacial irradiance model is introduced within the System Advisor Model (SAM). The modeling steps and implementation in SAM are presented. The results of a bifacial simulation are compared against several other modeling programs and shown to match within 1 - 2 percent for fixed-axis and horizontal single axis tracked systems. An example case study is presented to demonstrate how the model can be used to evaluate bifacial systems, with a variety of results presented to illustrate different sensitivities.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2018

SAM International Case Studies: DPV Analysis in Mexico

Presentation demonstrates the use of the System Advisor Model (SAM) in international analyses, specifically Mexico. Two analyses are discussed with relation to SAM modelling efforts: 1) Customer impacts from changes to net metering and billing agreements and 2) Potential benefits of PV for Mexican solar customers, the Mexican Treasury, and the environment. Along with the SAM analyses, integration of the International Utility Rate Database (I-URDB) with SAM and future international SAM work are discussed. Presentation was created for the International Solar Energy Society's (ISES) webinar titled 'International use of the NREL System Advisor Model (SAM) with case studies'.

29 ENERGY PLANNING, POLICY, AND ECONOMY↗
DOE OSTI2015

Technoeconomic Modeling of Battery Energy Storage in SAM

Detailed comprehensive lead-acid and lithium-ion battery models have been integrated with photovoltaic models in an effort to allow System Advisor Model (SAM) to offer the ability to predict the performance and economic benefit of behind the meter storage. In a system with storage, excess PV energy can be saved until later in the day when PV production has fallen, or until times of peak demand when it is more valuable. Complex dispatch strategies can be developed to leverage storage to reduce energy consumption or power demand based on the utility rate structure. This document describes the details of the battery performance and economic models in SAM.

14 SOLAR ENERGY↗
DOE OSTI2015

Performance Modeling and Testing of Distributed Electronics in PV Systems; NREL (National Renewable Energy Laboratory)

Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order to give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses with and without distributed power electronics, along with experimental validation results. Computer modeling is able to predict the performance of distributed power electronics (microinverters, power optimizers) in PV systems. However, details about partial shade and other mismatch must be known in order to give the model accurate information to go on. This talk will describe recent updates in NREL’s System Advisor Model program to model partial shading losses.

14 SOLAR ENERGY↗
DOE OSTI2024

Side-by-Side Comparison of Subhourly Clipping Models

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

ENERGY PLANNING, POLICY, AND ECONOMY,MATHEMATICS A↗
DOE OSTI2020

Behind the Meter Storage for Electric Vehicle Charging, Electrochemical and Thermal Energy Storage, and Solar Photovoltaic

In response to the potentially large and irregular demand from EVs, along with changing load profiles from buildings with on-site generation, utilities are evaluating multiple options for managing dynamic loads, including time-of-use pricing, demand charges, battery storage, and curtailment of variable generation. Buildings, as well as commercial, public, and workplace EV charging operations, can use a combination of electrochemical battery storage and thermal energy storage coupled with on-site generation to manage energy costs as well as provide resiliency and reliability for EV charging and building energy loads. We are completing a behind the meter storage analysis that focuses on determining the optimal system designs and energy flows for thermal and electrochemical behind the meter storage with on-site solar photovoltaic (PV) generation enabling electric vehicle charging in various climates, building types, and utility rate structures. In completing this analysis, we have developed a tool that combines existing battery models via the System Advisor Model (SAM) and building modeling software via EnergyPlus into a single interface. This tool allows us to simulate a building with a detailed battery model to properly size the battery, thermal energy storage, and solar PV systems to maximize profit for the system owner. This also allows us to assess how the battery degrades under various supervisory control dispatch algorithms to control charging/discharging; we can also see how thermal energy storage is created and used to complement the battery to reduce thermal loads in the building. With this project, we can analyze new batteries that are designed specifically for energy storage, rather than designed to be extremely energy dense for electric vehicle applications, using battery lifetime models from other national labs and the existing SAM battery model, which has detailed lifetime and degradation parameters. We can also assess novel thermal storage technologies by integrating them into the whole building energy simulation program EnergyPlus. Because the model calls both SAM and EnergyPlus, required inputs need to be compatible for both models. These inputs include, on a high-level, the following: weather files, building and electric vehicle load profiles, electricity rate tariff information, and system cost information for the stationary battery, solar PV, and thermal storage system. The various buildings we are studying for this analysis are retail big-box grocery store, commercial office building, fleet vehicle depot and operations facility, multi-family residential, and electric vehicle charging station. For these different applications, the battery and thermal storage will be dispatched differently, and the various technologies are sized differently to optimize cost.

30 DIRECT ENERGY CONVERSION↗
DOE OSTI2020

Off-design performance of molten salt-driven Rankine cycles and its impact on the optimal dispatch of concentrating solar power systems

This paper presents a model for improving off-design performance predictions for molten salt-driven Rankine power cycles, such as in concentrating solar power tower applications. The model predicts cycle off-design performance under various boundary conditions, including molten salt inlet temperature, mass flow rate, and ambient temperature. The model is validated using industry performance data and benchmarked with results from the literature. A complete concentrating solar power plant, inclusive of solar heliostat field and receiver, is then considered, by implementing the Rankine cycle off-design performance results into the National Renewable Energy Laboratory’s System Advisor Model software, which includes a tool that determines optimal power production schedules. The work improves upon the current System Advisor Model by updating off-design performance characteristics. A case study demonstrates the impact of cycle off-design behavior on annual performance for a stand-alone concentrating solar power system and a concentrating solar power-photovoltaic hybrid system. In addition, we demonstrate how cycle off-design performance influences optimal operator dispatch decisions and, thereby, overall system design and economics. We conclude that off-design cycle performance impacts “optimal” sub-system sizing, especially for a concentrating solar power-photovoltaic hybrid configuration in which concentrating solar power must dispatch in conjunction with photovoltaic generation.

14 SOLAR ENERGY↗