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dispersion of the wind sites contributing to the load and the correlation in the output of those sites. When the ability to model solar-only CSP plants is added to CSDS, an approach similar to wind power’s method of estimating capacity value will be used. • Operating reserve constraint: Unlike wind, CSP is currently assumed to be fully dispatchable and, therefore, requires no additional operating reserve. 2. CURRENT CSP SYSTEM ASSUMPTIONS This paper reports on the first stage of, hopefully, several in the implementation of CSP within the WinDS/CSDS framework. Ideally, the model would compete solar-only, trough with storage, dishes, solar towers, etc. in an economically optimum portfolio. However, this first stage of development limits the situation to a single technology (parabolic trough Rankine cycle, similar to the SEGS plants installed in California) with a preselected thermal storage level (six hours of thermal storage). These factors, combined with an assumed scale of 100 MW plant size, determine the initial cost and performance characteristics. The NREL CSP analysis tool, Excelergy2, is a Microsoft Excel-based performance and financing tool for parabolic trough systems, with current costs and performance assumptions. The storage assumption greatly simplifies the treatment of resource variability. Because the plant is assumed to be dispatchable, the capacity value for the plant is assumed to be equal to the capacity factor during the summer peak period, which is essentially the nameplate capacity. Additionally, no operating reserve is necessary for this plant, and surplus is assumed to be negligible due to the alignment of the solar resource and load. TABLE 1: TIMESLICE PLANT CAPACITY FACTORS Excelergy was also used outside of CSDS to determine the performance of the assumed system for a variety of locations, representing all five solar classes. For each location, the hourly output of Excelergy was aggregated into the 16 timeslices within CSDS to determine the average capacity factor for each timeslice of the year, for each solar class (Table 1). For this analysis, it was conservatively assumed that these capacity factors (i.e. solar plant performance) were unchanged in the future. In reality, it is expected that these would improve through R&D and shared operational improvements. 3. BASE-CASE ASSUMPTIONS AND RESULTS In this analysis, the Base Case is a business-as-usual case that relies heavily on the Reference Case scenario of the U.S. Energy Information Agency Annual Energy Outlook (AEO) for 2005 for inputs that fall outside the scope of CSDS. The single deviation from the AEO 2005 is the price of natural gas going forward; which, for CSDS, is based instead on the California Market Price Reference (CA MPR).4 Although partly based on the EIA projections, the CA MPR reference is significantly higher. It was determined that this reference was more reflective of the market in the southwestern United States. As with the U.S. EIA’s AEO, this price trajectory will change each year. Based on the 2005 DOE Solar Program Multiyear Technology Plan,5 we assume that 54% of the cost improvements projected by DOE will occur through R&D (Table 2). In addition to the improvements over time shown in Table 2, CSDS also allows for “learning” improvements in the cost values. For each doubling of installed worldwide CSP capacity (a scenario of CSP installations outside the United States reaching 120 GW by 2040 is input), there is an 8% reduction in costs. TABLE 2: R & D-DRIVEN COST AND PERFORMANCE Timeslice solar class 1 solar class 2 solar class 3 solar class 4 solar class 5 Hours H1 0.384 0.367 0.381 0.395 0.446 1152 H2 0.642 0.769 0.821 0.872 0.855 462 H3 0.989 0.974 1.020 1.065 1.066 264 H4 0.831 0.732 0.794 0.856 0.920 330 H5 0.274 0.199 0.229 0.259 0.276 792 H6 0.535 0.661 0.677 0.694 0.740 315 H7 0.900 0.894 0.890 0.887 0.978 180 H8 0.405 0.402 0.401 0.399 0.558 225 H9 0.115 0.111 0.106 0.102 0.122 1496 H10 0.259 0.326 0.326 0.326 0.327 595 H11 0.502 0.581 0.560 0.540 0.542 340 H12 0.035 0.047 0.040 0.034 0.031 425 H13 0.330 0.313 0.328 0.343 0.327 1144 H14 0.640 0.716 0.730 0.745 0.708 455 H15 0.947 0.935 0.973 1.010 0.950 260 H16 0.742 0.684 0.703 0.722 0.736 325 Average 0.409 0.413 0.427 0.442 0.457 Capacity Fixed Solar class Year Factor Capital cost O&M Var O&M - 1000$/MW 1000$/MW-$/Mwh 1 2005 0.409 4865 5.83 2.54 1 2030 0.409 2832 5.83 0.55 1 2050 0.409 2554 5.83 0.50 2 2005 0.413 4865 5.83 2.52 2 2030 0.413 2832 5.83 0.55 2 2050 0.413 2554 5.83 0.50 3 2005 0.427 4865 5.84 2.46 3 2030 0.427 2832 5.84 0.53 3 2050 0.427 2554 5.84 0.48 4 2005 0.442 4865 5.84 2.39 4 2030 0.442 2832 5.84 0.52 4 2050 0.442 2554 5.84 0.47 5 2005 0.457 4865 5.84 2.33 5 2030 0.457 2832 5.84 0.51 5 2050 0.457 2554 5.84 0.46 3PDF Image | Concentrating Solar Deployment System (CSDS) A New Model for Estimating U.S. Concentrating Solar Power (CSP) Market Potential
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