Concentrating Solar Deployment Systems CSDS A New Model for Estimating U.S. Concentrating Solar Powe

1
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

2
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?

3
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

4
CSDS Regions
5
Solar Resources in CSDS
6
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 EIAs
  Annual Energy Outlook 2005

7
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 OM, Variable OM) from
  Excelergy for different locations
• Assume cost reductions in line with DOE goals
• 8 learning rate
• Independent Power Producer (IPP) financing

8
Base Case Capacity by Generator Type
9
CSP Capacity deployment in 2050
10
Base Case Capacity by Solar Class
11
Base Case CSP by Transmission Type
12
Base Case Generation Fractions
13
Impact of CSP RD Improvements
14
Impact of Reduced Cost Scenario
15
Extension of Investment Tax Credit (ITC)
16
Extension of Production Tax Credit (ITC)
17
Conclusions

• 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 RD 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.

18

• 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.
•   
•  
•