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At least 253 records · Page 14

All sources NASA NTRS DOE OSTI

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

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 OSTI2021

Demonstrating SolarPILOT’s Python API 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’s1 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 paper presents a new, full-featured, Python-based application programmable interface (API) for SolarPILOT, which we hereafter refer to as CoPylot. CoPylot provides access to all SolarPILOT’s capabilities to generate and characterize power tower CSP systems seamlessly through Python. Supported capabilities include (i) creating and destroying a model instance with message reporting tools; (ii) accessing and setting any SolarPILOT variable including custom land boundaries for field layouts; (iii) programmatically managing receiver and heliostat objects with varied attributes for systems with multiple receiver or heliostat types; (iv) generating, assigning, and modifying solar field layouts including the ability to set individual heliostat locations, aimpoints, soiling rates, and reflectivity levels; (v) simulating solar field performance; (vi) returning detailed results describing performance of individual heliostats, the aggregate field, and receiver flux distribution; and, (vii) exporting Python-based model instances to multiple file formats. CoPylot enables Python users to perform detailed CSP tower analysis utilizing either the Hermite expansion technique (analytical) or the SolTrace ray-tracing engine. In addition to CoPylot’s functionality, Python users have access to the over 100,000 open-source libraries to develop, analyze, optimize, and visualize power tower CSP research. This enables CSP researchers to perform analysis that was previously not possible through SolarPILOT’s existing interfaces. This paper discusses the capabilities of CoPylot and presents a use case wherein we demonstrate optimal solar field aiming strategies.

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

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 OSTI2015

MO-FG-213-04: Managing the Relationship

Academic-industrial collaborations are playing an increasingly prominent role in medical physics research. Successful partnerships have enabled truly translational, benchtop-to-bedside research that has significantly and positively impacted our field. Given the current flat or depleting trends in federal funding, and the increasing emphasis in the medical physics community on demonstrating translatability, efficacy, and safety prior to widespread clinical use, it is anticipated that such partnerships will become ever more important in advancing our field. In the future, it is likely that neither academia alone, nor industry alone, will be able to bring significant innovation to successfully impact patient care in radiotherapy. Such innovation will require partnership and symbiosis through the lifetime of the project. This symposium is aimed at professionals from academia and the industry who are interested in either initiating new collaborations or maintaining and improving existing partnerships. We will hear from four thought leaders from industry and academia on critical aspects of building and maintaining successful academic-industrial research partnerships. The following topics will be addressed: 1. Motivations: The industry perspective [Kevin Brown, Elekta] What goes into the development of a company’s R&D roadmap What are the motivations and expectations of the industry from academic partners How can academics make a successful pitch by identifying projects aligned with a company’s roadmap Why are some relationships successful and some not Milestones and progress reports - Why do we need them 2. Motivations: The academic perspective [T. Rock Mackie, Univ. of Madison, WI; Tomotherapy] What are the motivations and expectations of academic partners from the industry What are the cultural differences in academia vs industry on sharing information, exchanging ideas Partnership or entrepreneurship? When does each option make sense 3. Industrial Grants and Research Contracts – a Primer [Norbert Pelc; Stanford University] What topics are suited to industrial grants vs. academic grants How do resources (personnel effort, tuition) get allocated and charged in academia What are reasonable resource and timeline requests for small and big projects; from small and big companies “Fair market value” considerations Sections of a research agreement (IP including software confidential information, publications, data, exclusivity, support) 4. Managing the relationship [John Wong, Johns Hopkins University] How to effectively collaborate with each other How to adapt to changes in the relationship How to renew funding with the industry Moderated Panel Discussion [All speakers + moderators] Learning Objectives: 1. Understand the motivations of each partner 2. Elements that contribute to a successful partnership 3. Avoiding pitfalls - Challenges on either side that may derail a project 4. How to frame a research grant and agreement 5. How to sustain the collaborative relationship Amit Sawant: Funding from: NIH. Varian, Elekta and VisionRT; Michelle Svatos: Employee and shareholder: Varian, Board Member, TopasMC; Kevin Brown: Employee, Elekta; T. Rock Mackie: Consultant, Accuray; Norbert Pelc: Research support: Philips Healthcare, GE Healthcare; John Wong: Funding from NIH, Xstrahl and Elekta. Co-founder and advisor for JPLC Associates, LLC.

07 ISOTOPE AND RADIATION SOURCES↗

DOE OSTI2025

The Right Time at the Right Places

In my undergraduate studies at the Massachusetts Institute of Technology (MIT), the short-lived chemical physics major allowed me to evade a number of courses required for chemistry majors. Thus, it was possible to take many physics courses and most of the advanced PhD-level courses in physical chemistry. I also took the introductory electrical engineering course in computer programming. The latter allowed me to write lots of computer code as a part of my (passing, but largely unsuccessful) senior thesis directed graciously by Professor Walter Thorson. As recommended by MIT Professor John C. Slater, I moved to Stanford University with Professor Frank Harris as my PhD supervisor. Frank was the perfect advisor for me, providing very close direction during the first year, and then allowing me to develop more and more independently. Within a few days of my twenty-fifth birthday, I became an assistant professor of chemistry at the University of California, Berkeley. Eighteen years later, I moved to the University of Georgia as director of a new research institute.

autobiography↗

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 OSTI2017

SAM Process Heat Model Development and Validation: Liquid-HTF Trough and Direct Steam Generation Linear Focus Systems

Two new solar industrial process heat (SIPH) models have been added to the System Advisor Model (SAM) energy simulation tool. The two process heat models were: i) liquid-heat transfer fluid (HTF) parabolic troughs; and ii) direct steam generation (DSG) linear-focus collectors. The DSG model can simulate either DSG parabolic troughs (PT) or DSG Linear Fresnel (LF) fields, based on the optical characteristics supplied. These models help to meet the need for independent, validated tools that reliably simulate the effects of adding a solar field to provide heat for potential IPH applications. During the pre-validation of the liquid-HTF model, operational data was provided and preprocessed by SPF and evaluated at ZAE Bayern. The beta liquid-HTF model provided excellent agreement between the simulations and the operational data for the solar thermal yield generation on a yearly and monthly basis (4.4% difference). The final liquid-HTF process heat model has been further optimized with test users such as ZAE Bayern and SPF. Key improvements in the SIPH models to better model SIPH fields include the use of smaller piping systems and heat capacities, a single collector field, and a higher time-step resolution than for electricity generation (down to 10 minutes). Both the final liquid-HTF and DSG models were successfully validated against operational data for respective solar fields, and showed less than 5% deviation to actual results. A brief application where the liquid-HTF process heat model is used for a brewery in California and the value of reliably simulating a potential SIPH solar field are highlighted.

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 OSTI2022

PyPVRPM: Photovoltaic Reliability and Performance Model in Python

The ability to perform accurate techno-economic analysis of solar photovoltaic (PV) systems is essential for bankability and investment purposes. Most energy yield models assume an almost flawless operation (i.e., no failures); however, realistically, components fail and get repaired stochastically. This package, PyPVRPM, is a Python translation and improvement of the Language Kit (LK) based PhotoVoltaic Reliability Performance Model (PVRPM), which was first developed at Sandia National Laboratories in Goldsim software (Granata et al., 2011) (Miller et al., 2012). PyPVRPM allows the user to define a PV system at a specific location and incorporate failure, repair, and detection rates and distributions to calculate energy yield and other financial metrics such as the levelized cost of energy and net present value (Klise, Lavrova, et al., 2017). Our package is a simulation tool that uses NREL’s Python interface for System Advisor Model (SAM) (National Renewable Energy Laboratory, 2020b) (National Renewable Energy Laboratory, 2020a) to evaluate the performance of a PV plant throughout its lifetime by considering component reliability metrics. Besides the numerous benefits from migrating to Python (e.g., speed, libraries, batch analyses), it also expands on the failure and repair processes from the LK version by including the ability to vary monitoring strategies. These failures, repairs, and monitoring processes are based on user-defined distributions and values, enabling a more accurate and realistic representation of cost and availability throughout a PV system’s lifetime.

97 MATHEMATICS AND COMPUTING↗

DOE OSTI2014

Services du Centre de Solutions Pour Les Energies Propres (Fact Sheet) [Clean Energy Solutions Center Services]

This is the French translation of the Clean Energy Solutions Center services fact sheet. The Clean Energy Solutions Center (Solutions Center) helps governments, advisors and analysts create policies and programs that advance the deployment of clean energy technologies. The Solutions Center partners with international organizations to provide online training, expert assistance, and technical resources on clean energy policy.