The Water, Energy, and Carbon Sequestration Simulation Model (WECSsim) is a national dynamic simulation model that calculates and assesses capturing, transporting, and storing CO2 in deep saline formations from all coal and natural gas-fired power plants in the U.S. An overarching capability of WECSsim is to also account for simultaneous CO2 injection and water extraction within the same geological saline formation. Extracting, treating, and using these saline waters to cool the power plant is one way to develop more value from using saline formations as CO2 storage locations. WECSsim allows for both one-to-one comparisons of a single power plant to a single saline formation along with the ability to develop a national CO2 storage supply curve and related national assessments for these formations. This report summarizes the scope, structure, and methodology of WECSsim along with a few key results. Developing WECSsim from a small scoping study to the full national-scale modeling effort took approximately 5 years. This report represents the culmination of that effort. The key findings from the WECSsim model indicate the U.S. has several decades' worth of storage for CO2 in saline formations when managed appropriately. Competition for subsurface storage capacity, intrastate flows of CO2 and water, and a supportive regulatory environment all play a key role as to the performance and cost profile across the range from a single power plant to all coal and natural gas-based plants' ability to store CO2. The overall system's cost to capture, transport, and store CO2 for the national assessment range from $74 to $208 / tonne stored ($96 to 272 / tonne avoided) for the first 25 to 50% of the 1126 power plants to between $1,585 to well beyond $2,000 / tonne stored ($2,040 to well beyond $2,000 / tonne avoided) for the remaining 75 to 100% of the plants. The latter range, while extremely large, includes all natural gas power plants in the U.S., many of which have an extremely low capacity factor and therefore relatively high system's cost to capture and store CO2.
People save for retirement throughout their career because it is virtually impossible to save all youll need in retirement the year before you retire. Similarly, without installing incremental amounts of clean fossil, renewable or transformative energy technologies throughout the coming decades, a radical and immediate change will be near impossible the year before a policy goal is set to be in place. Therefore, our research question is, To meet our desired technical and policy goals, what are the factors that affect the rate we must install technology to achieve these goals in the coming decades? Existing models do not include full regulatory constraints due to their often complex, and inflexible approaches to solve for optimal engineering instead of robust and multidisciplinary solutions. This project outlines the theory and then develops an applied software tool to model the laboratory-to-market transition using the traditional technology readiness level (TRL) framework, but develops subsequent and a novel regulatory readiness level (RRL) and market readiness level (MRL). This tool uses the ideally-suited system dynamics framework to incorporate feedbacks and time delays. Future energy-economic-environment models, regardless of their programming platform, may adapt this software model component framework or module to further vet the likelihood of new or innovative technology moving through the laboratory, regulatory and market space. The prototype analytical framework and tool, called the Technology, Regulatory and Market Readiness Level simulation model (TRMsim) illustrates the interaction between technology research, application, policy and market dynamics as they relate to a new or innovative technology moving from the theoretical stage to full market deployment. The initial results that illustrate the models capabilities indicate for a hypothetical technology, that increasing the key driver behind each of the TRL, RRL and MRL components individually decreases the time required for the technology to progress through each component by 63, 68 and 64%, respectively. Therefore, under the current working assumptions, to decrease the time it may take for a technology to move from the conceptual stage to full scale market adoption one might consider expending additional effort to secure regulatory approval and reducing the uncertainty of the technologys demand in the marketplace.
Carbon capture and sequestration (CCS) has important implications relative to future thermoelectric water use. A bounding analysis is performed using past greenhouse gas emission policy proposals and assumes either all effected capacity retires (lower water use bound) or is retrofitted (upper bound). The analysis is performed in the context of recent trends in electric power generation expansion, namely high penetration of natural gas and renewables along with constrained cooling system options. Results indicate thermoelectric freshwater withdrawals nationwide could increase by roughly 1% or decrease by up to 60% relative to 2009 levels, while consumption could increase as much as 21% or decrease as much as 28%. To identify where changes in freshwater use might be problematic at a regional level, electric power production has been mapped onto watersheds with limited water availability (where consumption exceeds 70% of gauged streamflow). Results suggest that between 0.44 and 0.96 Mm3/d of new thermoelectric freshwater consumption could occur in watersheds with limited water availability, while power plant retirements in these watersheds could yield 0.90 to 1.0 Mm3/d of water savings.
This report presents a system dynamics based model of the supply-demand interactions between the US light-duty vehicle (LDV) fleet, its fuels, and the corresponding primary energy sources through the year 2050. An important capability of our model is the ability to conduct parametric analyses. Others have relied upon scenario-based analysis, where one discrete set of values is assigned to the input variables and used to generate one possible realization of the future. While these scenarios can be illustrative of dominant trends and tradeoffs under certain circumstances, changes in input values or assumptions can have a significant impact on results, especially when output metrics are associated with projections far into the future. This type of uncertainty can be addressed by using a parametric study to examine a range of values for the input variables, offering a richer source of data to an analyst.The parametric analysis featured here focuses on a trade space exploration, with emphasis on factors that influence the adoption rates of electric vehicles (EVs), the reduction of GHG emissions, and the reduction of petroleum consumption within the US LDV fleet. The underlying model emphasizes competition between 13 different types of powertrains, including conventional internal combustion engine (ICE) vehicles, flex-fuel vehicles (FFVs), conventional hybrids(HEVs), plug-in hybrids (PHEVs), and battery electric vehicles(BEVs).We find that many factors contribute to the adoption rates of EVs. These include the pace of technological development for the electric powertrain, battery performance, as well as the efficiency improvements in conventional vehicles. Policy initiatives can also have a dramatic impact on the degree of EV adoption. The consumer effective payback period, in particular, can significantly increase the market penetration rates if extended towards the vehicle lifetime.Widespread EV adoption can have noticeable impact on petroleum consumption and greenhouse gas(GHG) emission by the LDV fleet. However, EVs alone cannot drive compliance with the most aggressive GHG emission reduction targets, even as the current electricity source mix shifts away from coal and towards natural gas. Since ICEs will comprise the majority of the LDV fleet for up to forty years, conventional vehicle efficiency improvements have the greatest potential for reductions in LDV GHG emissions over this time.These findings seem robust even if global oil prices rise to two to three times current projections. Thus,investment in improving the internal combustion engine might be the cheapest, lowest risk avenue towards meeting ambitious GHG emission and petroleum consumption reduction targets out to 2050.3 Acknowledgment The authors would like to thank Dr. Andrew Lutz, Dr. Benjamin Wu, Prof. Joan Ogden and Dr. Christopher Yang for their suggestions over the course of this project. This work was funded by the Laboratory Directed Research and Development program at Sandia National Laboratories.
This guide addresses software quality in the construction of Powersim{reg_sign} Studio 8 system dynamics simulation models. It is the result of almost ten years of experience with the Powersim suite of system dynamics modeling tools (Constructor and earlier Studio versions). It is a guide that proposes a common look and feel for the construction of Powersim Studio system dynamics models.
Sandia National Laboratories and General Motors Global Energy Systems team conducted a joint biofuels systems analysis project from March to November 2008. The purpose of this study was to assess the feasibility, implications, limitations, and enablers of large-scale production of biofuels. 90 billion gallons of ethanol (the energy equivalent of approximately 60 billion gallons of gasoline) per year by 2030 was chosen as the book-end target to understand an aggressive deployment. Since previous studies have addressed the potential of biomass but not the supply chain rollout needed to achieve large production targets, the focus of this study was on a comprehensive systems understanding the evolution of the full supply chain and key interdependencies over time. The supply chain components examined in this study included agricultural land use changes, production of biomass feedstocks, storage and transportation of these feedstocks, construction of conversion plants, conversion of feedstocks to ethanol at these plants, transportation of ethanol and blending with gasoline, and distribution to retail outlets. To support this analysis, we developed a 'Seed to Station' system dynamics model (Biofuels Deployment Model - BDM) to explore the feasibility of meeting specified ethanol production targets. The focus of this report is water and its linkage to broad scale biofuel deployment.
There is growing evidence of human induced climate change. Various legislation has been introduced to cap carbon emissions. Fossil powered electric generation is responsible for over 30% of the U.S. emissions. Carbon Capture and Sequestration (CCS) technology is water and energy intensive. The project's objectives are: (1) Explore water consumption implications associated with full deployment of a Carbon Capture and Storage (CCS) future; (2) Identify vulnerable areas in which water resources may be too limited to enable full deployment of CCS technology; and (3) Implement project with the cooperation of the National Energy Technology Laboratory (NETL) and DOE Office of Policy and International Affairs. Thermoelectric consumption projected to increase by 3.7 BGD due to CCS by 2035, a doubling over 2004. This increase is equivalent to projected growth in consumption by all other sectors. Demand is not equally distributed across the U.S. 18.5% of this future demand is located in watershed prone to surface and groundwater stress. 30% of current and future demand is located in watersheds prone to drought stress.
Recently, Sandia National Laboratories and General Motors cooperated on the development of the Biofuels Deployment Model (BDM) to assess the feasibility, implications, limitations, and enablers of producing 90 billion gallons of ethanol per year by 2030. Leveraging the past investment, a decision support model based on the BDM is being developed to assist investors, entrepreneurs, and decision makers in evaluating the costs and benefits associated with biofuels development in the U.S.-Mexico border region. Specifically, the model is designed to assist investors and entrepreneurs in assessing the risks and opportunities associated with alternative biofuels development strategies along the U.S.-Mexico border, as well as, assist local and regional decision makers in understanding the tradeoffs such development poses to their communities. The decision support model is developed in a system dynamics framework utilizing a modular architecture that integrates the key systems of feedstock production, transportation, and conversion. The model adopts a 30-year planning horizon, operating on an annual time step. Spatially the model is disaggregated at the county level on the U.S. side of the border and at the municipos level on the Mexican side. The model extent includes Luna, Hildalgo, Dona Anna, and Otero counties in New Mexico, El Paso and Hudspeth counties in Texas, and the four munipos along the U.S. border in Chihuahua. The model considers a variety of feedstocks; specifically, algae, gitropha, castor oil, and agricultural waste products from chili and pecans - identifying suitable lands for these feedstocks, possible yields, and required water use. The model also evaluates the carbon balance for each crop and provides insight into production costs including labor demands. Finally, the model is fitted with an interactive user interface comprised of a variety of controls (e.g., slider bars, radio buttons), descriptive text, and output graphics allowing stakeholders to directly explore the tradeoffs between alternative biofuels development scenarios.