The Alternative Liquid Fuels Simulation Model (AltSim) is a high-level dynamic simulation model which calculates and compares the production costs, carbon dioxide emissions, and energy balances of several alternative liquid transportation fuels. These fuels include: corn ethanol, cellulosic ethanol, biodiesel, and diesels derived from natural gas (gas to liquid, or GTL) and coal (coal to liquid, or CTL). AltSim allows for comprehensive sensitivity analyses on capital costs, operation and maintenance costs, renewable and fossil fuel feedstock costs, feedstock conversion efficiency, financial assumptions, tax credits, CO{sub 2} taxes, and plant capacity factor. This paper summarizes the preliminary results from the model. For the base cases, CTL and cellulosic ethanol are the least cost fuel options, at $1.60 and $1.71 per gallon, respectively. Base case assumptions do not include tax or other credits. This compares to a $2.35/gallon production cost of gasoline at September, 2007 crude oil prices ($80.57/barrel). On an energy content basis, the CTL is the low cost alternative, at $12.90/MMBtu, compared to $22.47/MMBtu for cellulosic ethanol. In terms of carbon dioxide emissions, a typical vehicle fueled with cellulosic ethanol will release 0.48 tons CO{sub 2} per year, compared to 13.23 tons per year for coal to liquid.
Before this LDRD research, no single tool could simulate a very high temperature reactor (VHTR) that is coupled to a secondary system and the sulfur iodine (SI) thermochemistry. Furthermore, the SI chemistry could only be modeled in steady state, typically via flow sheets. Additionally, the MELCOR nuclear reactor analysis code was suitable only for the modeling of light water reactors, not gas-cooled reactors. We extended MELCOR in order to address the above deficiencies. In particular, we developed three VHTR input models, added generalized, modular secondary system components, developed reactor point kinetics, included transient thermochemistry for the most important cycles [SI and the Westinghouse hybrid sulfur], and developed an interactive graphical user interface for full plant visualization. The new tool is called MELCOR-H2, and it allows users to maximize hydrogen and electrical production, as well as enhance overall plant safety. We conducted validation and verification studies on the key models, and showed that the MELCOR-H2 results typically compared to within less than 5% from experimental data, code-to-code comparisons, and/or analytical solutions.
This report began with a Laboratory-Directed Research and Development (LDRD) project to improve Sandia National Laboratories multidisciplinary capabilities in energy systems analysis. The aim is to understand how various electricity generating options can best serve needs in the United States. The initial product is documented in a series of white papers that span a broad range of topics, including the successes and failures of past modeling studies, sustainability, oil dependence, energy security, and nuclear power. Summaries of these projects are included here. These projects have provided a background and discussion framework for the Energy Systems Analysis LDRD team to carry out an inter-comparison of many of the commonly available electric power sources in present use, comparisons of those options, and efforts needed to realize progress towards those options. A computer aid has been developed to compare various options based on cost and other attributes such as technological, social, and policy constraints. The Energy Systems Analysis team has developed a multi-criteria framework that will allow comparison of energy options with a set of metrics that can be used across all technologies. This report discusses several evaluation techniques and introduces the set of criteria developed for this LDRD.
Energy planning represents an investment-decision problem. Investors commonly evaluate such problems using portfolio theory to manage risk and maximize portfolio performance under a variety of unpredictable economic outcomes. Energy planners need to similarly abandon their reliance on traditional, ''least-cost'' stand-alone technology cost estimates and instead evaluate conventional and renewable energy sources on the basis of their portfolio cost--their cost contribution relative to their risk contribution to a mix of generating assets. This report describes essential portfolio-theory ideas and discusses their application in the Western US region. The memo illustrates how electricity-generating mixes can benefit from additional shares of geothermal and other renewables. Compared to fossil-dominated mixes, efficient portfolios reduce generating cost while including greater renewables shares in the mix. This enhances energy security. Though counter-intuitive, the idea that adding more costly geothermal can actually reduce portfolio-generating cost is consistent with basic finance theory. An important implication is that in dynamic and uncertain environments, the relative value of generating technologies must be determined not by evaluating alternative resources, but by evaluating alternative resource portfolios. The optimal results for the Western US Region indicate that compared to the EIA target mixes, there exist generating mixes with larger geothermal shares at equal-or-lower expected cost and risk.
Stakeholders often have competing interests when selecting or planning new power plants. The purpose of developing this preliminary Electricity Portfolio Simulation Model (EPSim) is to provide a first cut, dynamic methodology and approach to this problem, that can subsequently be refined and validated, that may help energy planners, policy makers, and energy students better understand the tradeoffs associated with competing electricity portfolios. EPSim allows the user to explore competing electricity portfolios annually from 2002 to 2025 in terms of five different criteria: cost, environmental impacts, energy dependence, health and safety, and sustainability. Four additional criteria (infrastructure vulnerability, service limitations, policy needs and science and technology needs) may be added in future versions of the model. Using an analytic hierarchy process (AHP) approach, users or groups of users apply weights to each of the criteria. The default energy assumptions of the model mimic Department of Energy's (DOE) electricity portfolio to 2025 (EIA, 2005). At any time, the user can compare alternative portfolios to this reference case portfolio.
The Hydrogen Futures Simulation Model (H{sub 2}Sim) is a high level, internally consistent, strategic tool for exploring the options of a hydrogen economy. Once the user understands how to use the basic functions, H{sub 2}Sim can be used to examine a wide variety of scenarios, such as testing different options for the hydrogen pathway, altering key assumptions regarding hydrogen production, storage, transportation, and end use costs, and determining the effectiveness of various options on carbon mitigation. This User's Guide explains how to run the model for the first time user.
Hydrogen has the potential to become an integral part of our energy transportation and heat and power sectors in the coming decades and offers a possible solution to many of the problems associated with a heavy reliance on oil and other fossil fuels. The Hydrogen Futures Simulation Model (H2Sim) was developed to provide a high level, internally consistent, strategic tool for evaluating the economic and environmental trade offs of alternative hydrogen production, storage, transport and end use options in the year 2020. Based on the model's default assumptions, estimated hydrogen production costs range from 0.68 $/kg for coal gasification to as high as 5.64 $/kg for centralized electrolysis using solar PV. Coal gasification remains the least cost option if carbon capture and sequestration costs ($0.16/kg) are added. This result is fairly robust; for example, assumed coal prices would have to more than triple or the assumed capital cost would have to increase by more than 2.5 times for natural gas reformation to become the cheaper option. Alternatively, assumed natural gas prices would have to fall below $2/MBtu to compete with coal gasification. The electrolysis results are highly sensitive to electricity costs, but electrolysis only becomes cost competitive with other options when electricity drops below 1 cent/kWhr. Delivered 2020 hydrogen costs are likely to be double the estimated production costs due to the inherent difficulties associated with storing, transporting, and dispensing hydrogen due to its low volumetric density. H2Sim estimates distribution costs ranging from 1.37 $/kg (low distance, low production) to 3.23 $/kg (long distance, high production volumes, carbon sequestration). Distributed hydrogen production options, such as on site natural gas, would avoid some of these costs. H2Sim compares the expected 2020 per mile driving costs (fuel, capital, maintenance, license, and registration) of current technology internal combustion engine (ICE) vehicles (0.55$/mile), hybrids (0.56 $/mile), and electric vehicles (0.82-0.84 $/mile) with 2020 fuel cell vehicles (FCVs) (0.64-0.66 $/mile), fuel cell vehicles with onboard gasoline reformation (FCVOB) (0.70 $/mile), and direct combustion hydrogen hybrid vehicles (H2Hybrid) (0.55-0.59 $/mile). The results suggests that while the H2Hybrid vehicle may be competitive with ICE vehicles, it will be difficult for the FCV to compete without significant increases in gasoline prices, reduced predicted vehicle costs, stringent carbon policies, or unless they can offer the consumer something existing vehicles can not, such as on demand power, lower emissions, or better performance.
This paper analyzes the relationship between current renewable energy technology costs and cumulative production, research, development and demonstration expenditures, and other institutional influences. Combining the theoretical framework of 'learning by doing' and developments in 'learning by searching' with the fields of organizational learning and institutional economics offers a complete methodological framework to examine the underlying capital cost trajectory when developing electricity cost estimates used in energy policy planning models. Sensitivities of the learning rates for global wind and solar photovoltaic technologies to changes in the model parameters are tested. The implications of the results indicate that institutional policy instruments play an important role for these technologies to achieve cost reductions and further market adoption.
The Electricity Generation Cost Simulation Model (GenSim) is a user-friendly, high-level dynamic simulation model that calculates electricity production costs for variety of electricity generation technologies, including: pulverized coal, gas combustion turbine, gas combined cycle, nuclear, solar (PV and thermal), and wind. The model allows the user to quickly conduct sensitivity analysis on key variables, including: capital, O&M, and fuel costs; interest rates; construction time; heat rates; and capacity factors. The model also includes consideration of a wide range of externality costs and pollution control options for carbon dioxide, nitrogen oxides, sulfur dioxide, and mercuty. Two different data sets are included in the model; one from the US. Department of Energy (DOE) and the other from Platt's Research Group. Likely users of this model include executives and staff in the Congress, the Administration and private industry (power plant builders, industrial electricity users and electric utilities). The model seeks to improve understanding of the economic viability of various generating technologies and their emissions trade-offs. The base case results, using the DOE data, indicate that in the absence of externality costs, or renewable tax credits, pulverized coal and gas combined cycle plants are the least cost alternatives at 3.7 and 3.5 cents/kwhr, respectively. A complete sensitivity analysis on fuel, capital, and construction time shows that these results coal and gas are much more sensitive to assumption about fuel prices than they are to capital costs or construction times. The results also show that making nuclear competitive with coal or gas requires significant reductions in capital costs, to the $1000/kW level, if no other changes are made. For renewables, the results indicate that wind is now competitive with the nuclear option and is only competitive with coal and gas for grid connected applications if one includes the federal production tax credit of 1.8cents/kwhr.
The Electricity Generation Cost Simulation Model (GenSim) is a user-friendly, high-level dynamic simulation model that calculates electricity production costs for variety of electricity generation technologies, including: pulverized coal, gas combustion turbine, gas combined cycle, nuclear, solar (PV and thermal), and wind. The model allows the user to quickly conduct sensitivity analysis on key variables, including: capital, O&M, and fuel costs; interest rates; construction time; heat rates; and capacity factors. The model also includes consideration of a wide range of externality costs and pollution control options for carbon dioxide, nitrogen oxides, sulfur dioxide, and mercury. Two different data sets are included in the model; one from the US. Department of Energy (DOE) and the other from Platt's Research Group. Likely users of this model include executives and staff in the Congress, the Administration and private industry (power plant builders, industrial electricity users and electric utilities). The model seeks to improve understanding of the economic viability of various generating technologies and their emissions trade-offs. The base case results, using the DOE data, indicate that in the absence of externality costs, or renewable tax credits, pulverized coal and gas combined cycle plants are the least cost alternatives at 3.7 and 3.5 cents/kwhr, respectively. A complete sensitivity analysis on fuel, capital, and construction time shows that these results coal and gas are much more sensitive to assumption about fuel prices than they are to capital costs or construction times. The results also show that making nuclear competitive with coal or gas requires significant reductions in capital costs, to the $1000/kW level, if no other changes are made. For renewables, the results indicate that wind is now competitive with the nuclear option and is only competitive with coal and gas for grid connected applications if one includes the federal production tax credit of 1.8cents/kwhr.