This document provides an overview of the System Advisor Model (SAM) developed by the National Renewable Energy Laboratory. SAM is a free software tool that models the performance and financial metrics of renewable energy projects. It includes detailed performance models for photovoltaics, solar thermal, wind, and other technologies. SAM also contains financial models for residential, commercial, and utility-scale projects. Users can customize inputs, run simulations, and view results in tables and graphs to evaluate technology and financing options. SAM is widely used to assess renewable energy projects and policy.
The Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (WONDER) is a massive database that compiles detailed mortality data from every county and state in America.
The granularity of the CDC WONDER data set makes it a powerful tool to understand community health. You can explore data at the national, state and county level, broken down by sex, age, race, and ethnicity.
A Comparative Study of Pre Prandial and Post Prandial Heart Rate Variability ...ijsrd.com
Background: In obesity, as excessive adipose tissue accumulates, an altered metabolic profile occurs along with a variety of adaptations/alterations in the cardiac structure and function even in the absence of co-morbidities. Objective: To compare pre prandial and post prandial heart rate variability between young healthy non obese and obese females aged 18-25 years. Methods: 50 obese and 50 non obese young healthy females aged 18-25 years were selected based on body mass index (BMI). Heart rate variability was recorded in both pre prandial and post prandial state using ECG V: 52 (HRV) power spectral analysis to identify separate frequency components, i.e., total power (TP), low-frequency power (LF), high frequency power (HF) and low frequency to high frequency ratio. Results: All the statistical methods were carried out using the SPSS software for Windows (version 16.0) and Minitab (version 11.0). The Paired-Samples T Test procedure was done to compare the means of two variables for a single group. The Independent-Samples T Test procedure was done to compare the means for two groups of cases. P value less than or equal to 0.001 was considered statistically significant. HRV analysis found significantly lower (p≤0.001) values of TP, LF(ms2), HF(ms2) and HF (n.u.) and significantly higher values (p≤0.001) of LF (n.u.) and LF/HF ratio among the obese group in both pre prandial and post prandial state respectively compared to non obese group. Conclusion : Our data indicate that obese subjects have decreased parasympathetic activity as evidenced by decrease in TP, LF(ms2), HF(ms2) and HF (n.u.) and increase in sympathetic activity as evidenced by increase in LF (n.u.) and LF/HF in both pre prandial and post prandial state.
The Centers for Disease Control and Prevention Wide-ranging Online Data for Epidemiologic Research (WONDER) is a massive database that compiles detailed mortality data from every county and state in America.
The granularity of the CDC WONDER data set makes it a powerful tool to understand community health. You can explore data at the national, state and county level, broken down by sex, age, race, and ethnicity.
A Comparative Study of Pre Prandial and Post Prandial Heart Rate Variability ...ijsrd.com
Background: In obesity, as excessive adipose tissue accumulates, an altered metabolic profile occurs along with a variety of adaptations/alterations in the cardiac structure and function even in the absence of co-morbidities. Objective: To compare pre prandial and post prandial heart rate variability between young healthy non obese and obese females aged 18-25 years. Methods: 50 obese and 50 non obese young healthy females aged 18-25 years were selected based on body mass index (BMI). Heart rate variability was recorded in both pre prandial and post prandial state using ECG V: 52 (HRV) power spectral analysis to identify separate frequency components, i.e., total power (TP), low-frequency power (LF), high frequency power (HF) and low frequency to high frequency ratio. Results: All the statistical methods were carried out using the SPSS software for Windows (version 16.0) and Minitab (version 11.0). The Paired-Samples T Test procedure was done to compare the means of two variables for a single group. The Independent-Samples T Test procedure was done to compare the means for two groups of cases. P value less than or equal to 0.001 was considered statistically significant. HRV analysis found significantly lower (p≤0.001) values of TP, LF(ms2), HF(ms2) and HF (n.u.) and significantly higher values (p≤0.001) of LF (n.u.) and LF/HF ratio among the obese group in both pre prandial and post prandial state respectively compared to non obese group. Conclusion : Our data indicate that obese subjects have decreased parasympathetic activity as evidenced by decrease in TP, LF(ms2), HF(ms2) and HF (n.u.) and increase in sympathetic activity as evidenced by increase in LF (n.u.) and LF/HF in both pre prandial and post prandial state.
Strength Characteristics of Concrete with Sintered Fly Ash Aggregateijsrd.com
This paper gives a research on Sintered fly ash. Which is a waste material of coal firing Thermal power plants (TPPs) and its accumulation near power plant . The sintered fly ash is procuder from Gallantt group of industries .This industries is located near Sector-23, GIDA Sahjanwa city in Gorakhpur district in Uttar Pradesh state. The plant is an establishment of 14th November, 2006 .It has a generation capacity of 25 MW. These theses explain the various utilization of sintered fly ash and its ordinary Portland cement and properties in concretecauses severe pollution problems. Its utilization as a raw material for cube (Brick) making will be a very usefully solution in our economical and environmental aspects. Ordinary Portland cement (OPC) namely: 33, 43 and 53 which are commonly used in construction industry.This research paper in use of the Ordinary Portland cement (OPC) 43grade and use the sintered fly ash many percentage 5%, 10% ,15%, and 20% remove the aggregate. The high emission of CO2 from cement industry, steel plants, waste products from the thermal plants for the generation of electric power to meet the demand of growing population resulted in the production of 70 million tons of coal ash per year from burning about 200 million tons of coal per year for electric power generation .This research paper explain the various utilization of sintered fly ash and its ordinary Portland cement and properties in concrete.
The total installed capacity of wind generation in the United States surpassed
25 gigawatts (GW) at the end of 2008. Despite the global financial crisis, another
4.5 GW was installed in the first half of 2009. Because many states already have mandates in place for renewable energy penetration, significant growth is projected for the foreseeable future. In July 2008, the U.S. Department of Energy (DOE) published the findings of a year-long assessment of the costs, challenges, impacts and benefits of wind generation providing 20% of the electrical energy consumed in the United States by 2030 (EERE 2008). Developed through the collaborative efforts of a wide-ranging cross section of key stakeholders, that final report (referred to here as the 20% Report) takes a broad view of the electric power and wind energy industries. The 20% Report evaluates the requirements and outcomes in the areas of technology, manufacturing, transmission and integration, environmental impacts, and markets that would be necessary for reaching the 20% by 2030 target. The 20% Report states that although significant costs, challenges, and impacts
are associated with a 20% wind scenario, substantial benefits can be shown to overcome the costs. In other key findings, the report concludes that such a scenario is unlikely to be realized with a business-as-usual approach, and that a major national commitment to clean, domestic energy sources with desirable environmental attributes would be required. The growth of domestic wind generation over the past decade has sharpened the focus on two questions: Can the electrical grid accommodate very high amounts
of wind energy without jeopardizing security or degrading reliability? And, given that the nation’s current transmission infrastructure is already constraining further development of wind generation in some regions, how could significantly larger amounts of wind energy be developed? The answers to these questions could hold the keys to determining how much of a role wind generation can play in the U.S. electrical energy supply mix.
Strength Characteristics of Concrete with Sintered Fly Ash Aggregateijsrd.com
This paper gives a research on Sintered fly ash. Which is a waste material of coal firing Thermal power plants (TPPs) and its accumulation near power plant . The sintered fly ash is procuder from Gallantt group of industries .This industries is located near Sector-23, GIDA Sahjanwa city in Gorakhpur district in Uttar Pradesh state. The plant is an establishment of 14th November, 2006 .It has a generation capacity of 25 MW. These theses explain the various utilization of sintered fly ash and its ordinary Portland cement and properties in concretecauses severe pollution problems. Its utilization as a raw material for cube (Brick) making will be a very usefully solution in our economical and environmental aspects. Ordinary Portland cement (OPC) namely: 33, 43 and 53 which are commonly used in construction industry.This research paper in use of the Ordinary Portland cement (OPC) 43grade and use the sintered fly ash many percentage 5%, 10% ,15%, and 20% remove the aggregate. The high emission of CO2 from cement industry, steel plants, waste products from the thermal plants for the generation of electric power to meet the demand of growing population resulted in the production of 70 million tons of coal ash per year from burning about 200 million tons of coal per year for electric power generation .This research paper explain the various utilization of sintered fly ash and its ordinary Portland cement and properties in concrete.
The total installed capacity of wind generation in the United States surpassed
25 gigawatts (GW) at the end of 2008. Despite the global financial crisis, another
4.5 GW was installed in the first half of 2009. Because many states already have mandates in place for renewable energy penetration, significant growth is projected for the foreseeable future. In July 2008, the U.S. Department of Energy (DOE) published the findings of a year-long assessment of the costs, challenges, impacts and benefits of wind generation providing 20% of the electrical energy consumed in the United States by 2030 (EERE 2008). Developed through the collaborative efforts of a wide-ranging cross section of key stakeholders, that final report (referred to here as the 20% Report) takes a broad view of the electric power and wind energy industries. The 20% Report evaluates the requirements and outcomes in the areas of technology, manufacturing, transmission and integration, environmental impacts, and markets that would be necessary for reaching the 20% by 2030 target. The 20% Report states that although significant costs, challenges, and impacts
are associated with a 20% wind scenario, substantial benefits can be shown to overcome the costs. In other key findings, the report concludes that such a scenario is unlikely to be realized with a business-as-usual approach, and that a major national commitment to clean, domestic energy sources with desirable environmental attributes would be required. The growth of domestic wind generation over the past decade has sharpened the focus on two questions: Can the electrical grid accommodate very high amounts
of wind energy without jeopardizing security or degrading reliability? And, given that the nation’s current transmission infrastructure is already constraining further development of wind generation in some regions, how could significantly larger amounts of wind energy be developed? The answers to these questions could hold the keys to determining how much of a role wind generation can play in the U.S. electrical energy supply mix.
Explore the innovative world of trenchless pipe repair with our comprehensive guide, "The Benefits and Techniques of Trenchless Pipe Repair." This document delves into the modern methods of repairing underground pipes without the need for extensive excavation, highlighting the numerous advantages and the latest techniques used in the industry.
Learn about the cost savings, reduced environmental impact, and minimal disruption associated with trenchless technology. Discover detailed explanations of popular techniques such as pipe bursting, cured-in-place pipe (CIPP) lining, and directional drilling. Understand how these methods can be applied to various types of infrastructure, from residential plumbing to large-scale municipal systems.
Ideal for homeowners, contractors, engineers, and anyone interested in modern plumbing solutions, this guide provides valuable insights into why trenchless pipe repair is becoming the preferred choice for pipe rehabilitation. Stay informed about the latest advancements and best practices in the field.
Hybrid optimization of pumped hydro system and solar- Engr. Abdul-Azeez.pdffxintegritypublishin
Advancements in technology unveil a myriad of electrical and electronic breakthroughs geared towards efficiently harnessing limited resources to meet human energy demands. The optimization of hybrid solar PV panels and pumped hydro energy supply systems plays a pivotal role in utilizing natural resources effectively. This initiative not only benefits humanity but also fosters environmental sustainability. The study investigated the design optimization of these hybrid systems, focusing on understanding solar radiation patterns, identifying geographical influences on solar radiation, formulating a mathematical model for system optimization, and determining the optimal configuration of PV panels and pumped hydro storage. Through a comparative analysis approach and eight weeks of data collection, the study addressed key research questions related to solar radiation patterns and optimal system design. The findings highlighted regions with heightened solar radiation levels, showcasing substantial potential for power generation and emphasizing the system's efficiency. Optimizing system design significantly boosted power generation, promoted renewable energy utilization, and enhanced energy storage capacity. The study underscored the benefits of optimizing hybrid solar PV panels and pumped hydro energy supply systems for sustainable energy usage. Optimizing the design of solar PV panels and pumped hydro energy supply systems as examined across diverse climatic conditions in a developing country, not only enhances power generation but also improves the integration of renewable energy sources and boosts energy storage capacities, particularly beneficial for less economically prosperous regions. Additionally, the study provides valuable insights for advancing energy research in economically viable areas. Recommendations included conducting site-specific assessments, utilizing advanced modeling tools, implementing regular maintenance protocols, and enhancing communication among system components.
Student information management system project report ii.pdfKamal Acharya
Our project explains about the student management. This project mainly explains the various actions related to student details. This project shows some ease in adding, editing and deleting the student details. It also provides a less time consuming process for viewing, adding, editing and deleting the marks of the students.
Forklift Classes Overview by Intella PartsIntella Parts
Discover the different forklift classes and their specific applications. Learn how to choose the right forklift for your needs to ensure safety, efficiency, and compliance in your operations.
For more technical information, visit our website https://intellaparts.com
Automobile Management System Project Report.pdfKamal Acharya
The proposed project is developed to manage the automobile in the automobile dealer company. The main module in this project is login, automobile management, customer management, sales, complaints and reports. The first module is the login. The automobile showroom owner should login to the project for usage. The username and password are verified and if it is correct, next form opens. If the username and password are not correct, it shows the error message.
When a customer search for a automobile, if the automobile is available, they will be taken to a page that shows the details of the automobile including automobile name, automobile ID, quantity, price etc. “Automobile Management System” is useful for maintaining automobiles, customers effectively and hence helps for establishing good relation between customer and automobile organization. It contains various customized modules for effectively maintaining automobiles and stock information accurately and safely.
When the automobile is sold to the customer, stock will be reduced automatically. When a new purchase is made, stock will be increased automatically. While selecting automobiles for sale, the proposed software will automatically check for total number of available stock of that particular item, if the total stock of that particular item is less than 5, software will notify the user to purchase the particular item.
Also when the user tries to sale items which are not in stock, the system will prompt the user that the stock is not enough. Customers of this system can search for a automobile; can purchase a automobile easily by selecting fast. On the other hand the stock of automobiles can be maintained perfectly by the automobile shop manager overcoming the drawbacks of existing system.
Immunizing Image Classifiers Against Localized Adversary Attacksgerogepatton
This paper addresses the vulnerability of deep learning models, particularly convolutional neural networks
(CNN)s, to adversarial attacks and presents a proactive training technique designed to counter them. We
introduce a novel volumization algorithm, which transforms 2D images into 3D volumetric representations.
When combined with 3D convolution and deep curriculum learning optimization (CLO), itsignificantly improves
the immunity of models against localized universal attacks by up to 40%. We evaluate our proposed approach
using contemporary CNN architectures and the modified Canadian Institute for Advanced Research (CIFAR-10
and CIFAR-100) and ImageNet Large Scale Visual Recognition Challenge (ILSVRC12) datasets, showcasing
accuracy improvements over previous techniques. The results indicate that the combination of the volumetric
input and curriculum learning holds significant promise for mitigating adversarial attacks without necessitating
adversary training.
1. NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Contract No. DE-AC36-08GO28308
System Advisor Model, SAM
2014.1.14: General Description
Nate Blair, Aron P. Dobos, Janine Freeman,
Ty Neises, and Michael Wagner
National Renewable Energy Laboratory
Tom Ferguson, Paul Gilman, and Steven
Janzou
Independent Consultants
Technical Report
NREL/TP-6A20-61019
February 2014
2. NREL is a national laboratory of the U.S. Department of Energy
Office of Energy Efficiency & Renewable Energy
Operated by the Alliance for Sustainable Energy, LLC
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Contract No. DE-AC36-08GO28308
National Renewable Energy Laboratory
15013 Denver West Parkway
Golden, CO 80401
303-275-3000 • www.nrel.gov
System Advisor Model, SAM
2014.1.14: General Description
Nate Blair, Aron P. Dobos, Janine Freeman,
Ty Neises, and Michael Wagner
National Renewable Energy Laboratory
Tom Ferguson, Paul Gilman, and Steven
Janzou
Independent Consultants
Prepared under Task No. SS13.5020
Technical Report
NREL/TP-6A20-61019
February 2014
3. NOTICE
This report was prepared as an account of work sponsored by an agency of the United States government.
Neither the United States government nor any agency thereof, nor any of their employees, makes any warranty,
express or implied, or assumes any legal liability or responsibility for the accuracy, completeness, or usefulness of
any information, apparatus, product, or process disclosed, or represents that its use would not infringe privately
owned rights. Reference herein to any specific commercial product, process, or service by trade name,
trademark, manufacturer, or otherwise does not necessarily constitute or imply its endorsement, recommendation,
or favoring by the United States government or any agency thereof. The views and opinions of authors
expressed herein do not necessarily state or reflect those of the United States government or any agency thereof.
This report is available at no cost from the National Renewable Energy
Laboratory (NREL) at www.nrel.gov/publications.
Available electronically at http://www.osti.gov/bridge
Available for a processing fee to U.S. Department of Energy
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Cover Photos: (left to right) photo by Pat Corkery, NREL 16416, photo from SunEdison, NREL 17423, photo by Pat Corkery, NREL
16560, photo by Dennis Schroeder, NREL 17613, photo by Dean Armstrong, NREL 17436, photo by Pat Corkery, NREL 17721.
Printed on paper containing at least 50% wastepaper, including 10% post consumer waste.
4. iii
Executive Summary
This document describes the capabilities of the U.S. Department of Energy and National
Renewable Energy Laboratory's System Advisor Model (SAM), Version 2014.1.14, released on
January 14, 2014, for potential users or readers wanting to learn about the model's capabilities.
SAM is a computer model that calculates performance and financial metrics of renewable energy
systems. Project developers, policymakers, equipment manufacturers, and researchers use SAM
results to evaluate financial, technology, and incentive options for renewable energy projects.
SAM simulates the performance of photovoltaic, concentrating solar power, solar water heating,
wind, geothermal, biomass, and conventional power systems. The financial model can represent
financial structures for projects that either buy and sell electricity at retail rates (residential and
commercial) or sell electricity at a price determined in a power purchase agreement (utility).
SAM's advanced simulation options facilitate parametric and sensitivity analyses, and statistical
analysis capabilities are available for Monte Carlo simulation and weather variability (P50/P90)
studies. SAM can also read input variables from Microsoft Excel worksheets. For software
developers, the SAM software development kit (SDK) makes it possible to use SAM simulation
modules in their applications written in C/C++, C#, Java, Python, and MATLAB. NREL
provides both SAM and the SDK as free downloads at http://sam.nrel.gov. Technical support and
more information about the software are available on the website.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
5. iv
Table of Contents
SAM Overview ............................................................................................................................................. 1
Model Structure........................................................................................................................................... 3
SAM Software Development Kit ........................................................................................................... 3
SAM Models and Databases .................................................................................................................. 3
Results: Tables, Graphs, and Reports...................................................................................................... 5
Performance Models................................................................................................................................... 6
Financial Models ......................................................................................................................................... 8
Residential and Commercial Projects..................................................................................................... 8
Power Purchase Agreement (PPA) Projects........................................................................................... 8
Levelized Cost of Energy and Cash Flow .............................................................................................. 9
Incentives.............................................................................................................................................. 10
Analysis Options....................................................................................................................................... 11
Software Development History and Users ............................................................................................. 12
Downloading SAM and User Support ..................................................................................................... 13
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
6. v
List of Figures
Figure 1. The SAM main window showing monthly electricity generation and the annual cash
flow for a photovoltaic system...................................................................................................2
Figure 2. The Results page showing the energy loss diagram for a photovoltaic system ...............5
Figure 3. The Time Series graph on the Results page showing hourly electricity generation for
a 100 MW parabolic trough system with 6 hours of storage.....................................................6
Figure 4. The first several rows of the cash flow table for a utility-scale project ...........................9
Figure 5. The report generator exports a PDF showing key assumptions and results from a
SAM analysis case...................................................................................................................12
Figure 6. SAM's help system includes detailed descriptions of the user interface, modeling
options, and results...................................................................................................................13
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
7. 1
SAM Overview
This document describes the U.S. Department of Energy and National Renewable Energy
Laboratory's System Advisor Model (SAM), Version 2014.1.14, released on January 14, 2014. It
is intended to both help potential users determine whether the model meets their modeling needs,
and provide information for readers who do not plan to use the model but want to learn about
its capabilities.
SAM is a performance and financial model designed to facilitate decision making for people
involved in the renewable energy industry:
• Project managers and engineers
• Financial and policy analysts
• Technology developers
• Researchers.
SAM makes performance predictions and cost of energy estimates for grid-connected power
projects based on installation and operating costs and system design parameters that you specify
as inputs to the model. Projects can be either on the customer side of the utility meter, where they
buy and sell electricity at retail rates, or on the utility side of the meter, where they sell electricity
at a price negotiated through a power purchase agreement (PPA).
SAM is an electric power generation model and assumes that the renewable energy system
delivers power either to an electric grid, or to a grid-connected building or facility to meet
electric load. It does not model thermal energy systems that meet a thermal process load. SAM
also does not model isolated or off-grid power systems, and does not model systems with
electricity storage batteries.
Creating a SAM file involves choosing both a performance model and a financial model to
represent your project. SAM automatically populates input variables with a set of default values
based on your choices. After you create the file, you modify the inputs to provide information
about the project's location, the type of equipment in the system, the cost of installing and
operating the system, and financial and incentives assumptions. It is your responsibility as an
analyst to review and modify all of the input data as appropriate for each analysis. Once you are
satisfied with the input variable values, you run simulations, and then examine results. A typical
analysis involves running simulations, examining results, revising inputs, and repeating that
process until you understand and have confidence in the results.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
8. 2
Figure 1. The SAM main window showing monthly electricity generation and the annual cash flow
for a photovoltaic system
SAM is available as a desktop application for Windows and OS X. The National Renewable
Energy Laboratory (NREL) distributes SAM for free at https://sam.nrel.gov. You may use SAM
for any purpose, but you must acknowledge your use of SAM in any documentation of analysis
involving the software, and credit NREL for developing the software, and the U.S. Department
of Energy for supporting its development. Guidelines for citing SAM in publications are
available at https://sam.nrel.gov/content/sam-publications.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
9. 3
Model Structure
SAM consists of a user interface, calculation engine, and programming interface. The user
interface is the part of SAM that you see. It provides access to input variables and simulation
controls and displays tables and graphs of results.
The user interface performs three basic functions:
• Provide access to input variables, which are organized into input pages. The input
variables describe the physical characteristics of a system, and the cost and financial
assumptions for a project. SAM's input variables are populated with default values to
help you get started with your analysis.
• Allow you to control how SAM runs simulations. You can run a basic simulation, or
more advanced simulations for optimization and sensitivity studies.
• Provide access to output variables in tables and graphs on the Results page, and in files
that you can open in a spreadsheet application or other software.
SAM's calculation engine, called the SAM Simulation Core (SSC), performs a time-step-by-
time-step simulation of a power system's performance, and calculates project cash flow and
financial metrics annually. The programming interface allows external programs to interact with
SAM, and a public version is available in the SAM software development kit.
SAM Software Development Kit
The SAM Simulation Core (SSC) software development kit (SDK) is a package of tools for
software developers for creating renewable energy system and project models using the SSC
library. The SSC library is the collection of simulation and computation modules that SAM uses
to simulate renewable energy projects: SAM is a desktop application that provides a user-
friendly front end for the SSC library. The SDK allows you to use modules from the SSC library
in Windows, OS X, or Linux applications that you write in C++, C#, Java, MATLAB, or Python.
NREL distributes the SAM Simulation Core SDK for free at https://sam.nrel.gov.
SAM Models and Databases
SAM represents the cost and performance of renewable energy projects using computer models
developed at NREL, Sandia National Laboratories, the University of Wisconsin, and other
organizations. Each performance model represents a part of the system, and each financial model
represents a project's financial structure.
Source code for the SAM simulation and calculation modules is not available to the public.
However, reference manuals describing the algorithms in each of the performance model
modules are available for download from the SAM website.
The models require input data to describe the performance characteristics of physical equipment
in the system, and project costs and financial assumptions. SAM's user interface makes it
possible for people with no experience developing computer models to build a model of a
renewable energy project, and to make cost and performance projections based on model results.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
10. 4
To describe the renewable energy resource and weather conditions at a project location, SAM
requires a weather data file. Depending on the kind of system you are modeling, you either
choose a weather data file from a list, download one from the Internet, or create the file using
your own data.
SAM includes several libraries of performance data and coefficients that describe the
characteristics of system components such as photovoltaic modules and inverters, parabolic
trough receivers and collectors, wind turbines, and biopower combustion systems. For those
components, you simply choose an option from a list, and SAM applies values from the library
to the input variables.
SAM can automatically download data and populate input variable values from the following
online databases:
• DSIRE for U.S. incentives.
• OpenEI Utility Rate Database for retail electricity rate structures for U.S. utilities.
• NREL Solar Prospector for solar resource data and ambient weather conditions.
• NREL Wind Integration Datasets for wind resource data.
• NREL Biofuels Atlas and DOE Billion Ton Update for biomass resource data.
• NREL Geothermal Resource database for temperature and depth data.
For the remaining input variables, you either use the default value or change its value. Some
examples of input variables are:
• Installation costs including equipment purchases, labor, engineering and other project
costs, land costs, and operation and maintenance costs.
• Numbers of modules and inverters, tracking type, and derating factors for photovoltaic
systems.
• Collector and receiver type, solar multiple, storage capacity, and power block capacity
for parabolic trough systems.
• Analysis period, real discount rate, inflation rate, tax rates, internal rate of return target or
power purchase price for utility financing models.
• Building load and time-of-use retail rates for commercial and residential financing
models.
• Tax and cash incentive amounts and rates.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
11. 5
Results: Tables, Graphs, and Reports
SAM displays modeling results in tables and graphs, ranging from the metrics table that displays
levelized cost of energy, first year annual production, and other single-value metrics, to tables
and graphs that show detailed annual cash flows and hourly performance data.
Figure 2. The Results page showing the energy loss diagram for a photovoltaic system
After you run simulations, SAM displays a set of default graphs. You can customize the graphs
by changing titles, colors, and font sizes, or create new graphs that show different data. You can
also export images of all graphs and the data they display to include in reports and presentations,
or to analyze further with other software.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
12. 6
Performance Models
SAM's performance models make hour-by-hour calculations of a power system's electric output,
generating a set of 8,760 hourly values that represent the system's electricity production over a
single year. You can explore the system's performance characteristics in detail by viewing tables
and graphs of the hourly and monthly performance data, or use performance metrics such as the
system's total annual output and capacity factor for more general performance evaluations.
Figure 3. The Time Series graph on the Results page showing hourly electricity generation for a
100 MW parabolic trough system with 6 hours of storage
The current version of SAM includes performance models for the following technologies:
• Photovoltaic (PV) systems (flat-plate and concentrating photovoltaic)
• CSP parabolic trough
• CSP power tower (molten salt and direct steam)
• CSP linear Fresnel
• CSP dish-Stirling
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
13. 7
• Conventional thermal (a simple heat rate model)
• Solar water heating for residential or commercial buildings
• Large and small wind power
• Geothermal power and geothermal co-production
• Biomass power.
You can compare different kinds of projects by creating more than one case in a file. For
example, you could compare the savings of a residential rooftop solar water heater to those of a
photovoltaic system. Or, for a large utility-scale project, you could compare the power purchase
price that would be required to make a wind, a photovoltaic, or a concentrating solar power
project profitable at a given location. SAM does not model hybrid power systems. For example,
you cannot model a single project that combines wind turbines and photovoltaic modules.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
14. 8
Financial Models
SAM's financial models calculate financial metrics for various kinds of power projects based on
a project's cash flows over an analysis period that you specify. The financial model uses the
system's electrical output calculated by the performance model to calculate the series of annual
cash flows.
SAM includes financial models for the following kinds of projects:
• Residential (retail electricity rates)
• Commercial (retail rates or power purchase agreement)
• Utility-scale (power purchase agreement):
o Single owner
o Leveraged partnership flip
o All equity partnership flip
o Sale leaseback.
Residential and Commercial Projects
Residential and commercial projects are financed through either a loan or cash payment, and
recover investment costs through savings from reduced electricity purchases from the electricity
service provider. For electricity pricing, SAM can model simple flat buy and sell rates, monthly
net metering, or complex rate structures with tiered time-of-use pricing. For these projects, SAM
reports the following metrics:
• Levelized cost of energy
• Electricity cost with and without renewable energy system
• Electricity savings
• After-tax net present value
• Payback period.
Power Purchase Agreement (PPA) Projects
Utility and commercial PPA projects are assumed to sell electricity through a power purchase
agreement at a fixed price with optional annual escalation and time-of-delivery (TOD) factors.
For these projects, SAM calculates:
• Levelized cost of energy
• PPA price (electricity sales price)
• Internal rate of return
• Net present value
• Debt fraction or debt service coverage ratio.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
15. 9
SAM can either calculate the internal rate of return based on a power price you specify, or
calculate the power price based on the rate of return you specify.
Levelized Cost of Energy and Cash Flow
For projects using retail electricity rates (residential and commercial), SAM calculates the
levelized cost of energy (LCOE) from after-tax cash flows, so that the LCOE represents the cost
of generating electricity over the project life, accounting for taxes and incentives. For projects
selling electricity under a power purchase agreement (utility), SAM calculates the LCOE from
the revenue cash flow, so that the LCOE also includes the developer's margin defined by the
project's internal rate of return.
Figure 4. The first several rows of the cash flow table for a utility-scale project
The project annual cash flows include:
• Revenues from electricity sales and incentive payments
• Installation costs
• Operating, maintenance, and replacement costs
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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• Loan principal and interest payments
• Tax benefits and liabilities (accounting for any tax credits for which the project
is eligible)
• Incentive payments
• Project and partner's internal rate of return requirements (for PPA projects).
Incentives
The financial model can account for a wide range of incentive payments and tax credits:
• Investment based incentives
• Capacity-based incentives
• Production-based incentives
• Investment tax credits
• Production tax credits
• Depreciation (MACRS, Straight-line, custom, bonus, etc.).
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
17. 11
Analysis Options
In addition to simulating a system's performance over a single year and calculating a project cash
flow over a multi-year period, SAM's analysis options make it possible to conduct studies
involving multiple simulations, linking SAM inputs to a Microsoft Excel workbook, and working
with custom simulation modules. The following options are for analyses that investigate the
impacts on model results of variations and uncertainty in assumptions about weather,
performance, cost, and financial parameters:
• Parametric Analysis: Assign multiple values to input variables to create graphs and tables
showing the value of output metrics for each value of the input variable. Useful for
optimization and exploring relationships between input variables and results.
• Sensitivity Analysis: Create tornado graphs by specifying a range of values for input
variables as a percentage of a base value.
• Statistical Analysis: Create histograms showing the sensitivity of output metrics to
variations in input values.
• Probability of Exceedance Analysis (P50/P90): For locations with weather data available
for many years, calculate the probability that the system's total annual output will exceed
a certain value.
SAM also makes it possible to work with external models developed in Excel or the TRNSYS
simulation platform:
• Excel Exchange: Use Excel to calculate the value of input variables, and automatically
pass values of input variables between SAM and Excel.
• Exchange Variables: Create your own input variables for use with Excel Exchange or a
custom TRNSYS deck.
• Simulator Options: Change the simulation time step, or run SAM with your own
simulation modules developed in the TRNSYS modeling platform.
Finally, SAM's scripting language SamUL allows you to write your own programs within the
SAM user interface to control simulations, change values of input variables, and write data to
text files.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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Software Development History and Users
SAM, originally called the "Solar Advisor Model," was first developed by the National
Renewable Energy Laboratory in collaboration with Sandia National Laboratories in 2005 for
internal use by the U.S. Department of Energy's Solar Energy Technologies Program in its
systems-based analysis of solar technology improvement opportunities within the program.
NREL released the first public version in August 2007 as Version 1, making it possible for solar
energy professionals to analyze photovoltaic systems and concentrating solar power parabolic
trough systems in the same modeling platform using consistent financial assumptions. Between
2007 and 2013, two new versions were released each year, adding new technologies and
financing options. In 2010, the name changed to "System Advisor Model" to reflect the addition
of non-solar technologies. Beginning in 2014, NREL will release one new version of the
software each year, with periodic maintenance updates as needed.
Figure 5. The report generator exports a PDF showing key assumptions and results from a SAM
analysis case
The DOE and DOE laboratories continue to use the model for program planning and grant
programs. Since the first public release, over 35,000 people have downloaded the software,
representing manufacturers, project developers, academic researchers, and policymakers.
Academic professionals use SAM for graduate-level classroom instruction and for research.
Project developers use SAM to evaluate different system configurations to maximize earnings
from electricity sales. Policymakers and designers use the model to experiment with different
incentive structures.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
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Downloading SAM and User Support
SAM runs on both Windows and OS X. It requires about 500 MB of storage space on your
computer. SAM is available for free download. To download the software, you must register for
an account on the website. After registering, you will receive an email with your account
information. SAM's website includes software descriptions, links to publications about SAM and
other resources.
Figure 6. SAM's help system includes detailed descriptions of the user interface, modeling
options, and results
The following resources are available for learning to use SAM and for getting help with
your analyses:
• Help system: Press the F1 key in Windows or Command-? in Mac OS from any input or
results page in SAM to view the Help topic for that page.
• User support forum.
• Documentation, videos, and training schedule on the SAM website Learning page.
You can contact the SAM support team by submitting a message on the Contact Us page.
This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.
