# Presentation: 758360

## 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

- 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(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

## CSDS Regions

**Notes:**
Note the 4 regional levels: 
Wind regions built up from counties and typically laid out to isolate windy lands from load areas if possible. 
PCA regions composed of wind regions and attempt to match actual utility boundaries. All state boundaries obeyed as well for obtaining state-level results
NERC regions built up from PCA regions and match NEMS regions
The three interconnects have red boundaries.
Different aspects of the model use all four levels of regions.
Currently NREL is inserting RTO regions

## Solar Resources in CSDS

**Notes:**
One of the powerful features of WinDS is easy access to the best solar resource data via the NREL GIS team and Donna Heimiller
Additionally, there are a variety of exclusions applied to the solar resource areas. Areas are excluded if: 
They have less than 6.75 kWh/m2/day annual average direct normal resource (May 2003 Perez data)
The average slope is greater than 1% (derived from 90 m elevation data)
Major urban and water features are excluded.
Protected federal lands (wilderness, parks, monuments, etc.) are excluded.
If the remaining resource lands are less than 5 contiguous sq. km, they are excluded.

## 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

- 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

## Base Case Capacity by Generator Type

**Notes:**
Here the important thing to note is that some wind of each class is deployed by the model throughout the forecast. This depends on many factors but primarily transmission distance and wind quality. However, it is certainly not the case that all of class 7 wind is used before class 6.

Note also that Class 3 for which a new line must be built is the eventual big winner while class 4 on new lines is the second most dominant player.

## CSP Capacity deployment in 2050 

## Base Case Capacity by Solar Class

**Notes:**
Here the important thing to note is that minimal CSP is deployed until after 2024 in the base case. 

Note also the best resource areas come on first but that they are not completely depleted prior to the growth in less attractive solar areas. These areas are close to major loads and so the transmission costs are lower.

## Base Case CSP by Transmission Type 

**Notes:**
Note that this assumes that 20% of existing grid capacity can be used for transmitting new capacity (solar, wind or conventionals).

Note that it never gets cost-effective (in the base case) to build new transmission lines between regions specifically for concentrating solar power systems.  

Also, there is significant load quite close to regions with high-quality solar resource (such as Los Angeles and Las Vegas)

## Base Case Generation Fractions

## Impact of CSP R&D Improvements

## Impact of Reduced Cost Scenario

## Extension of Investment Tax Credit (ITC)

## Extension of Production Tax Credit (ITC)

## 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 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

**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.