This is one of a series of reports produced as a result of the Transportation Energy Futures (TEF) project, a U.S. Department of Energy (DOE)-sponsored multi-agency project initiated to identify underexplored strategies for abating greenhouse gases and reducing petroleum dependence related to transportation. The project was designed to consolidate existing transportation energy knowledge, advance analytic capacity-building, and uncover opportunities for sound strategic action. Transportation currently accounts for 71% of total U.S. petroleum use and 33% of the nation's total carbon emissions. The TEF project explores how combining multiple strategies could reduce GHG emissions and petroleum use by 80%. Researchers examined four key areas-lightduty vehicles, non-light-duty vehicles, fuels, and transportation demand-in the context of the marketplace, consumer behavior, industry capabilities, technology and the energy and transportation infrastructure. The TEF reports support DOE long-term planning. The reports provide analysis to inform decisions about transportation energy research investments, as well as the role of advanced transportation energy technologies and systems in the development of new physical, strategic, and policy alternatives. In addition to the DOE and its Office of Energy Efficiency and Renewable Energy, TEF benefitted from the collaboration of experts from the National Renewable Energy Laboratory and Argonne National Laboratory, along with steering committee members from the Environmental Protection Agency, the Department of Transportation, academic institutions and industry associations. More detail on the project, as well as the full series of reports, can be found at http://www.eere.energy.gov/analysis/transportationenergyfutures.
REopt is an energy planning platform offering concurrent, multiple technology integration and optimization capabilities to help clients meet their cost savings and energy performance goals. The REopt platform provides techno-economic decision support analysis throughout the energy planning process, from agency-level screening and macro planning to project development to energy asset operation. REopt employs an integrated approach to optimizing the energy costs of a site by considering electricity and thermal consumption, resource availability, complex tariff structures including time-of-use, demand and export rates, incentives, net metering, and interconnection limits. Formulated as a mixed integer linear program, REopt recommends an optimally sized mix of conventional and renewable energy, and energy storage technologies; estimates the net present value associated with implementing those technologies; and provides the cost-optimal dispatch strategy for operating them at maximum economic efficiency. The REopt platform can be customized to address a variety of energy optimization scenarios including policy, microgrid, and operational energy applications. This paper presents the REopt techno-economic model along with two examples of recently completed analysis projects.
PrefaceChanges are occurring throughout the U.S. economy, especially in regards to how energy is generated and used in the electricity, buildings, industrial, and transportation sectors. These changes are being driven by environmental and energy security concerns and by economics. The electric-sector market share of natural gas and variable renewable generation, such as wind and solar photovoltaics (PV), continues to grow. The buildings sector is evolving to meet efficiency standards, the transportation sector is evolving to meet efficiency and renewable fuels standards, and the industrial sector is evolving to reduce emissions. Those changes are driving investment and utilization strategies for generation and other assets.Nuclear and renewable energy sources are important to consider in the energy sector's evolution because both are considered to be clean and non-carbon emitting energy sources. The Idaho National Laboratory (INL) and National Renewable Energy Laboratory (NREL) are jointly investigating potential synergies between technologies exploiting nuclear and renewable energy sources. The two laboratories have held several joint workshops since 2011. Those workshops brought together experts in both areas to identify synergies and potential opportunities to work together. Workshop participants identified nuclear-renewable hybrid energy systems (N-R HESs) as one of the opportunities and recommended investigating whether N-R HESs could both generate dispatchable electricity without carbon emissions and provide clean energy to industrial processes. They also recommended analyzing the potential for N-R HESs to provide dispatchable capacity to a grid with high penetrations of non-dispatchable resources and to investigate whether real inertia provided by thermal power cycles within N-R HESs provides value to the grid. This report is one of a series of reports INL and NREL are producing to investigate the technical and economic aspects of N-R HESs. It provides results of an analysis of two N-R HES scenarios. The first is a Texas-synthetic gasoline scenario that includes four subsystems including a nuclear reactor, thermal power cycle, wind power plant, and synthetic gasoline production technology. The second is an Arizona-desalination scenario with its four subsystems a nuclear reactor, thermal power cycle, PV, and a desalination plant. INL analyzed the technical performance of the same two N-R HESs in a previous report. Future analyses are planned for other N-R HES options.
and BlocPower were instrumental in providing data for the analysis and hosting site visits. We would like to acknowledge Dylan Cutler and Dan Olis of NREL for their role in developing the stochastic outage modeling capability in REopt. We would also like to thank Bob Butt and Dan Olis of NREL,
NOTICEThis 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. Executive SummaryThe Biomass Scenario Model (BSM) is a system dynamics model developed by the U.S. Department of Energy (DOE) as a tool to better understand the interaction of complex policies and their potential effects on the biofuels industry in the United States. However, it does not currently have the capability to account for allocation of biomass resources among the various end uses, which limits its utilization in analysis of policies that target biomass uses outside the biofuels industry. This report provides a more holistic understanding of the dynamics surrounding the allocation of biomass among uses that include traditional use, wood pellet exports, bio-based products and bioproducts, biopower, and biofuels by (1) highlighting the methods used in existing models' treatments of competition for biomass resources; (2) identifying coverage and gaps in industry data regarding the competing end uses; and (3) exploring options for developing models of biomass allocation that could be integrated with the BSM to actively exchange and incorporate relevant information.The review of existing models (Section 2) provides an overview on how competing demands for biomass are currently represented; these models do not include the dynamic interaction among biomass end users as a primary focus. There are models that specifically deal with the competition for biomass, but they are predominantly optimization models either with a limited time horizon or an incomplete pool of competitors for biomass. In terms of the current industries that consume biomass resources (Section 3), it is clear that federal and state regulations are a critical driver of growth, as has been the case in the biopower and biofuels industries. It is conceivable that the wood pellet and bioproducts industries could consume a larger portion of the biomass resource pool in the future, but to what extent that occurs depends largely on domestic and foreign policies' promotion of these technologies. In order to more fully explore the competition for biomass resources, complementary modeling pathways are developed in Section 4. A basic, standalone system dynamics model that explicitly models ...
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