Concentrating Solar Deployment Systems (CSDS)
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Nate Blair, Walter Short, Mark Mehos, Donna Heimiller National Renewable Energy Laboratory - PowerPoint PPT Presentation

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Concentrating Solar Deployment Systems (CSDS) A New Model for Estimating U.S. Concentrating Solar Power Market Potential. Nate Blair, Walter Short, Mark Mehos, Donna Heimiller National Renewable Energy Laboratory. Goal of Analysis.

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Concentrating Solar Deployment Systems (CSDS)A New Model for Estimating U.S. Concentrating Solar Power Market Potential

Nate Blair, Walter Short, Mark Mehos, Donna Heimiller

National Renewable Energy Laboratory

Goal of analysis
Goal of Analysis

  • Build a new capability to examine future market penetration for concentrating solar power

    • Extend capabilities of Wind Deployment System (WinDS)

  • Attempting to answer the following questions

    • When will concentrating solar power strongly enter the market under business-as-usual conditions?

    • What regions of the southwestern U.S. are most likely to see significant CSP market penetration?

    • Is an extension of the current investment tax credit (ITC) or a wind-type production tax credit (PTC) provide greater acceleration of market penetration?

    • What impact do the expected, improved costs due to research and development have on market penetration?

    • What is the sensitivity of deployment to general cost reductions?

Csds model c oncentrating s olar d eployment s ystem
CSDS Model(Concentrating Solar Deployment System)

A multi-regional, multi-time-period model of capacity expansion in the electric sector of the U.S. focused on renewables.

Designed to estimate market potential of solar energy in the U.S. for the next 20 – 50 years under different technology development and policy scenarios

General characteristics of csds
General Characteristics of CSDS

  • Linear program cost minimization for each of 26 two-year periods from 2000 to 2050

  • Sixteen time slices in each year: 4 daily and 4 seasons

    • Capacity factors for each timeslice determined by hourly simulation

  • 4 levels of regions – solar supply/demand, power control areas, NERC areas, Interconnection areas

  • Existing and new transmission lines

  • 5 wind classes (3-7), onshore and offshore shallow and deep

  • 5 solar classes (6.75 kW/m2/day to 8 kw/m2/day)

  • All major power technologies – hydro, gas CT, gas CC, 4 coal technologies, nuclear, gas/oil steam

  • Conventional costs and fuel prices from EIA’s Annual Energy Outlook 2005

Current csp input assumptions
Current CSP Input Assumptions

  • SEGS Type Trough Plant

    • Typical 100 MW plant sizing

    • 6 hours of thermal storage

    • Prescribed capacity factor based on plant as modeled in Excelergy (NREL CSP specific model) for various solar resource levels

    • Costs (capital, fixed O&M, Variable O&M) from Excelergy for different locations

    • Assume cost reductions in line with DOE goals

    • 8% learning rate

    • Independent Power Producer (IPP) financing


  • A tool was created for modeling CSP capacity growth and examine various scenarios while accounting for transmission needs.

  • CSP will contribute a share of future electric generation in our Base Case scenario and increase that share with various policy enhancements.

  • Increased R&D leading to further reductions in cost are vital to CSP market penetration.

  • CSP deployment is very cost sensitive because the resource is geographically focused and relatively close to load centers.

  • Appropriate incentives are necessary to help assure a more sustained technology expansion.

    • Extending the Investment Tax Credit past 2007 will dramatically increase the generation from CSP.

    • Implementing a Production Tax Credit for CSP similar to the PTC for wind has a minimal or negative impact on CSP deployment until costs drop significantly.

  • Disclaimer and Government License

  • This work has been authored by Midwest Research Institute (MRI) under Contract No. DE-AC36-99GO10337 with the U.S. Department of Energy (the “DOE”). The United States Government (the “Government”) retains and the publisher, by accepting the work for publication, acknowledges that the Government retains a non-exclusive, paid-up, irrevocable, worldwide license to publish or reproduce the published form of this work, or allow others to do so, for Government purposes.

  • Neither MRI, the DOE, the Government, nor any other agency thereof, nor any of their employees, makes any warranty, express or implied, or assumes any 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 any privately owned rights. Reference herein to any specific commercial product, process, or service by trade name, trademark, manufacturer, or otherwise does not constitute or imply its endorsement, recommendation, or favoring by the Government or any agency thereof. The views and opinions of the authors and/or presenters expressed herein do not necessarily state or reflect those of MRI, the DOE, the Government, or any agency thereof.