Biomass Technology Analysis Models and Tools
The following is a list of models and tools that can assist in learning more about the listed technologies and uses. Most of these tools can be applied on a global, regional, local, or project basis.
Conducting full life-cycle assessments for biomass products, including electricity, biodiesel, and ethanol, is important for determining environmental benefits. NREL analysts use a life-cycle inventory modeling package and supporting databases to conduct life-cycle assessments. These tools can be applied on a global, regional, local, or project basis. Additional information is found on the Biomass Research Data and Resources page.
Biomass Feedstock Composition and Property Database is the resulting data from analysis of more than 150 (as of 10/01) samples of potential biofuels feedstocks including corn stover, wheat straw, bagasse, switchgrass and other grasses, and poplars and other fast-growing trees.
Under the renewable fuels standard provisions of the Energy Independence and Security Act of 2007, U.S. policy targets 36 billion gallons per year of biofuels utilization by 2022. Achieving such large scale biofuels adoption may require the substantial development of new infrastructure, markets, and related systems. The National Renewable Energy Laboratory has developed a system dynamics model, the Biomass Scenario Model (BSM), to represent the primary system effects and dependencies in the biomass-to-biofuels supply chain and to provide a framework for developing scenarios and conducting biofuels policy analysis. This approach was designed to inform analysis and discussion by determining which supply chain changes would have the greatest potential to accelerate the deployment of biofuels. The model currently attempts to integrate all aspects of the cellulosic biofuels supply chain, from growing the feedstock through harvest, collection, transport, conversion, distribution of fuel, and, finally, consumption of the fuel in available, applicable vehicles. For more information, see OpenEnergyInfo (OpenEI) Wiki.
The Job and Economic Development Impact (JEDI) Models are easy-to-use, spreadsheet based tools that analyze the economic impacts of constructing and operating power generation and biofuel plants at the local and state level. First developed to model wind energy development impacts, JEDI has expanded to offer models that analyze the job and economic impacts of biofuel plants and concentrating solar power, coal and natural gas power plants.
These NREL and analogous ASTM laboratory procedures provide tested and accepted methods for performing analyses commonly used in biofuels research.
The Theoretical Ethanol Yield Calculator allows you to calculate the theoretical ethanol yield of a particular biomass feedstock, based on its sugar content.
Thermodynamic Data for Biomass Conversion and Waste Incineration
This National Bureau of Standards/NREL report provides heat of combustion and other useful data for biopower and biofuels research on a wide range of biomass and nonbiomass materials.
Crosscutting Analytical Tools
The following is a list of models and tools that can assist in learning more about our main renewable energy technologies and their uses. Most of these tools can be applied on a global, regional, local, or project basis.
Visit the Modeling and Tools for Project Engineering website for more information.
2011 Renewable Energy Data Book
The 2011 Renewable Energy Data Book provides facts and figures on energy in general, renewable electricity in the United States, and global renewable energy development and investments. Rich graphics and depth and breadth of data make the Data Book series among the most popular items on NREL.gov.
Cost of Renewable Energy Spreadsheet Tool (CREST)
The Cost of Renewable Energy Spreadsheet Tool (CREST) is an economic cash flow model designed to enable PUCs and the renewable energy community assess projects, design cost-based incentives (e.g., feed-in tariffs), and evaluate the impact of tax incentives or other support structures. CREST is a suite of three analytic tools, for solar (photovoltaic and solar thermal), wind, and geothermal technologies, respectively.
Distributed Generation Technology Costs and Performance Data
Recent cost estimates for distributed generation (DG) renewable energy technologies are available across capital costs, operations and maintenance (O&M) costs, capacity factor, and levelized cost of energy (LCOE). Where available, links to utility-scale and DG data are available under the tab headings. The LCOE tab provides a simple calculator for both utility-scale and DG technologies that compares the combination of capital costs, O&M, performance, and fuel costs.
Geographic Information System
This site provides dynamically generated maps of renewable energy resources that determine which energy technologies are viable solutions in the United States. The National Renewable Energy Laboratory analyzes the resources and inputs the data into the GIS—Geographic Information Systems.
Green Power Network
The Green Power Network (GPN) provides news and information on green power markets and related activities. The site provides up-to-date information on green power providers, product offerings, consumer protection issues, and policies affecting green power markets. It also includes a reference library of relevant papers, articles and reports. The Green Power Network is operated and maintained by the National Renewable Energy Laboratory for the U.S. Department of Energy.
HOMER®, the micropower optimization model, simplifies the task of evaluating design options for both off-grid and grid-connected power systems. When you design a power system, you must make many decisions about the configuration of the system: What components does it make sense to include in the system design? How many and what size of each component should you use? How do the costs and environmental impacts of different system designs compare? The large number of technology options, range of technology costs, and variable availability of energy resources make these decisions difficult to make. The HOMER® model's optimization and sensitivity analysis algorithms make it easier to evaluate the many possible system configurations. For more information, visit the HOMER Energy website. You also can access a fact sheet about this unique tool. Contact developer for more information.
The Hybrid2 code is a user-friendly tool to conduct detailed long-term performance and economic analysis on a wide variety of hybrid power systems.
Hydrogen Deployment System (HyDS)
The Hydrogen Deployment System (HyDS) model analyzes the transition to a hydrogen economy. It costs out numerous pathways — from production to distribution — finding the most economic mode for hydrogen to be delivered in a user-defined region. It integrates an intercity optimization algorithm, which considers economy-of-scale of production, transportation, and delivery — as well as the trade-offs between centralized and forecourt hydrogen production. Given price projections for gasoline, natural gas, and other feedstocks, the HyDS ME produces a supply curve reflecting the most economic pathway for hydrogen to be delivered. Contact Nate Blair of the Strategic Energy Analysis Center (SEAC) for more information.
Open EI's Transparent Cost Database
The Transparent Cost Database collects program cost and performance estimates for EERE technologies in a public forum where they can be viewed and compared to other published estimates. The database includes literature on technology cost and performance estimates (both current and future projections) for vehicles, biofuels, and electricity generation. All data are downloadable for full transparency.
REFlex is a reduced form dispatch model that evaluates the limits of variable renewable generation as a function of system flexibility. It can also evaluate the role of enabling technologies such as demand response and energy storage. It is an updated version of the PVFlex model described in the following articles: "Evaluating the Limits of Solar Photovoltaics (PV) in Traditional Electric Power Systems," by Paul Denholm and Robert Margolis, NREL Report No. JA-640-41459; doi:10.1016/j.enpol.2006.10.014 and "Evaluating the Limits of Solar Photovoltaics (PV) in Electric Power Systems Utilizing Energy Storage and Other Enabling Technologies," by Paul Denholm and Robert Margolis, NREL Report No. JA-6A2-45315. doi:10.1016/j.enpol.2007.03.004
RETFinance is a levelized cost-of-energy model, which simulates a detailed 20-year nominal dollar cash flow for renewable energy projects power projects including project earnings, cash flows, and debt payment to calculate a project's levelized cost-of-electricity, after-tax nominal Internal Rate of Return, and annual Debt-Service-Coverage-Ratios.
Regional Energy Deployment System (ReEDS)
Regional Energy Deployment System (ReEDS) is a multiregional, multitimeperiod, Geographic Information System (GIS), and linear programming model of capacity expansion in the electric sector of the United States. The model, developed by NREL's Strategic Energy Analysis Center (SEAC), is designed to conduct analysis of the critical energy issues in today's electric sector with detailed treatment of the full potential of conventional and renewable electricity generating technologies as well as electricity storage. The principal issues addressed include access to and cost of transmission, access to and quality of renewable resources, the variability of wind and solar power, and the influence of variability on the reliability of the grid. ReEDS addresses these issues through a highly discretized regional structure, explicit accounting for the variability in wind and solar output over time, and consideration of ancillary services requirements and costs. See the ReEDS Web site for more information.
Renewable Energy Technology Characterizations (1997)
The Renewable Energy Technology Characterizations describe the technical and economic status of the major emerging renewable energy options for electricity supply. These technology characterizations represent the best estimates of the U.S. Department of Energy (DOE) and the Electric Power Research Institute (EPRI) regarding the future performance and cost improvements expected for these technologies as a result of continuing research and development (R&D) and development of markets for renewable energy through the year 2030. The Renewable Energy Technology Characterizations are copyrighted, but permission is granted for unlimited copying for noncommercial use.
SERA (Scenario Evaluation, Regionalization & Analysis)
The Scenario Evaluation, Regionalization and Analysis (SERA) model is a geospatially and temporally oriented infrastructure analysis model that determines the optimal production and delivery scenarios for hydrogen, given resource availability and technology cost. Given annual H2 demands on a city-by-city basis, forecasts of feedstock costs, and a catalog of available hydrogen production and transportation technologies, the model generates "blueprints" for hydrogen infrastructure build-out that minimize the overall net-present-value of capital, operating, and feedstock costs for infrastructure networks that meet the specified demand profiles. The model represents production facilities and pipelines at the level of individually geolocated components, while it treats truck and rail transportation at an aggregate level. Intra-urban locations of dispensing stations and of hydrogen production for stationary applications are generated using a geospatial statistical model that matches empirical distributions of such facilities. Prior to October 2009, SERA was know as the Hydrogen Deployment System Modeling Environment (HyDS-ME).
Stochastic Energy Deployment System (SEDS)
The Stochastic Energy Deployment System (SEDS) model is a capacity-expansion model of the U.S. energy market. The model uses five-year time periods from 2005 to 2050. SEDS can be operated either deterministically or stochastically. When operated deterministically, SEDS uses a single value instead of the input-probability distributions for the uncertain parameters. In this mode, the results are immediate and informative, in terms of how the model responds to different inputs and assumptions. When operated stochastically, SEDS uses Monte Carlo simulations to make a number of sweeps through the time period. In each sweep, the random variables are sampled using a Latin Hypercube approach that improves on a standard Monte Carlo simulation. SEDS is being developed with a commercially available software package, Analytica, designed to facilitate the development of stochastic models (for more information on Analytica, visit Lumina). Contact Emily Newes of the Strategic Energy Analysis Center (SEAC) for more information.