GenSim

Drennen, T., A. Baker, and W. Kamery (2003). Electricity Generation Cost Simulation Model (GenSim) Version 2.0. Sandia National Laboratories. 

These are projected production costs for a new, state of the art, electricity unit (2005). Production costs calculated using Sandia s Electricity Generation Simulation Model (GenSim). 

Table 6.3. Projected 2020 electricity costs from GenSim...

GenSim is written in Powersim Studio Enterprise 2005, a dynamic simulation-modeling software package. The model s easy-to-use policy screens allow the user to explore "what if " questions, such as... 

GenSim calculates projected levelized cost of energy (LCOE) for a wide variety of electricity generation technologies advanced coal, CC natural gas, natural gas combustion, nuclear, wind, geothermal, solar thermal, and solar PV. All values are for new plants equipped with the best available pollution-control technologies (BACT). 

GenSim considers externality costs for emissions of sulfur dioxide (SO2), nitrogen oxides (NO ), carbon dioxide (COj), and mercury (Hg). Externality costs are initially set to zero in the model. 

Figure Al. Representative GenSim main busbar screen (nudear) showing busbar...

In the current version of GenSim, the variable and fixed O M estimates are based on the default capacity factors in Table Al, as O M data for other capacity factors were not available. These O M estimates may not be valid at different rates of capacity utilization. The actual O M costs for gas CT facilities might be quite different operating at 50 or 60% capacity utilization on a sustained basis, rather than at 30%. 

GenSim s structure makes sensitivity analysis easy. A representative screen (solar PV) is shown in Figure A3. This screen allows the user to compare LCOE costs at either comparable capacity factors (i.e., all at 50%), or at default or user defined capacity factors (i.e., solar PV at 20% with nuclear at 90%). LCOE estimates are displayed at the top of the graph. These estimates change as the user changes key assumptions using either the sliders or number boxes on the bottom half of the screen. For example, changing the assumed capital costs for solar PV from 3868 to 1500 /kW reduces the LCOE from 26.0 to 10.4 cents/kWh. 

The following three sections provide a more detailed sensitivity analysis, derived from GenSim. In the first section, production costs for various technologies are plotted against specific fuel prices. This type of analysis is useful for determining fuel price break-even costs, such as the coal price at which nuclear is cost competitive. The next section determines capital cost break-even points, such as at what capital costs nuclear becomes competitive with coal, gas, or wind. The third section discusses the results of a sensitivity analysis for nuclear plant construction time. All examples use the DOE s data set comparable analysis using the Platt s data set is included in Appendix A.l. 

Figure A6 illustrates a similar analysis for advanced coal technology. This analysis shows that fuel prices of (in /MBtu) 2.18,2.13, and 2.41, respectively, make coal competitive with nuclear, wind, and gas CC technologies. The default DOE coal price in GenSim is 1.29 /MBtu. As with the previous example, these results indicate that coal s competitiveness is very dependent on assumed fuel prices. These results are not very sensitive to changes in capital costs a 10% difference in capital costs changes these results by 0.26 /MBtu.

GenSim includes an extensive externality component that allows the user to consider the costs of externalities on LCOE estimates. Initially, GenSim assumes that the prices for all four externalities, COj, NO, SOj, and mercury (Hg) are set at zero. The capital costs for each generating option include those associated with the best available control technologies for both SO2 and NO. CO2 and mercury emission technology costs are not included in the default capital costs. Using this externality component, the user can explore the effect of externality costs and/or different pollution-control technologies on the estimates of LCOE. 

GenSim also permits the user to consider the overall costs of pollution control. Without pollution-control technologies included in the analysis, LCOE estimates for coal and natural gas decrease 0.70 and 0.06 cents/kWh, respectively. These are the implied costs of the required pollution-control devices. 

In addition to the type of externality analysis illustrated here, GenSim allows users to conduct a wide range of more detailed externality analyses. The various options by technology and pollutant are summarized in Appendix A.2. 

We take IjK = Bo = 978.8 kbar for Si, to be consistent with our previous argument that the Ge QDs are constrained by the Si host. We obtain the deformation potential of -2.6 0.5 eV as compared to the bulk Ge value (—4.5 0.4 eV) in Ref. [39]. Other contributions to the smaller a value may originate from the nonlinear dependence of the peak energy on pressure inasmuch as any sublinear dependence will tend to decrease the pressure coefficient. It is noteworthy that a pressure coefficient of 4 1 meV/kbar has been obtained in GenSim multiple quantum wells [40]. 

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