►Concentrating Solar Power (CSP) - Basics and Introduction
Site selection includes numerous factors, but a top priority is a good solar resource. For site selection, a representative annual solar resource is required to make comparisons with alternative sites and estimate plant output. Because site selection is always based on historical solar resource data and changes in weather patterns from year to year, more years of data are better for determining a representative annual dataset. Defining a typical meteorological year (TMY) dataset is not a trivial exercise.
This is a summary of the tools and techniques for evaluating specific CSP sites based on all available information, as well as guidance on steps to improve the on-site determination of the solar resource relevant to the type of CSP technology that is being considered. The overall goal is to help the project developer and investor obtain the best estimates of the solar resource and weather information to address four stages of a CSP project evaluation and operation.
Ideally, a potential CSP site will have several years of high-quality on-site data, using the measurement and metrology procedures, in formats directly relevant to the type of technology being considered. However, in the current CSP market, such data are not usually available, and project developers must rely on a number of techniques to provide the most accurate determination of site resource characteristics based on any available information sources. In the United States, these data sources might include some limited on-site measurements of varying quality, access to nearby measurements that may or may not be precisely applicable to the site because of spatial and temporal variability, access to satellitederived DNI estimates, or access to nearby modeled ground stations.
We assume that during the site-screening and prefeasibility stages, no high-quality on-site data are available, and that annual energy estimates must be derived from historical datasets such as the Perez SUNY satellite data and the NSRDB. During feasibility assessments, including engineering analysis and due diligence, some periods of high-quality measurements are assumed to be available at the site; however, these relative short-term measurements must be extrapolated to long-term records that capture seasonal trends and the interannual variability of solar resources for the site. During the system acceptance and site operation stages, reliance should be on high-quality ground-based measurements, perhaps supplemented to some extent by ongoing satellite-derived measurements for the region. The project developer should consult the following table when evaluating sites through the various stages of project development.
| Evaluation Step
| Solutions and Insights|
| What proposed site location(s)
need to be evaluated?
| Has a single site been chosen?
| If not, is the developer making a choice among two|
or more sites, or “prospecting” over a wider area? If
choosing among multiple sites, the developer will
benefit from using maps and graphical techniques
to evaluate both the estimated resource and the
uncertainty of those resource estimates. See examples
| How can short-term datasets
that provide projections
over the next few years be
extended to long-term (30-
year) projections so cash flow
projections through the life of
the project can be made?
| Different locations may have different interannual|
variability, e.g., locations more subject to a monsoon
effect will have higher interannual variability in the
summer months. Typically, on-site data cover at
most a few years, so we will discuss procedures for
extrapolating these datasets to long-term projections
using longer term (up to 45 years) modeled DNI data
from the NSRDB as well as how to relate the nearest
NSRDB stations to site-specific data.
| How important are seasonal
and diurnal patterns for DNI?
| Most CSP projects will produce electricity for the public|
utility grid. If time-of-day pricing has been implemented
for the consumer, an understanding of the diurnal
patterns and monthly mean values during those
months when time-of-day pricing is in place may be
more important than the estimate of the annual
average. If the CSP project includes thermal storage, the
need to analyze when the system will build up storage
versus when the system provides power to the grid
during daylight hours also emphasizes the importance
of understanding the diurnal patterns. Thermal storage
greatly mitigates the effect of system intermittency,
but accurate or realistic daily, hourly, or subhourly solar
radiation data may still be needed.
| Are data needed that most
closely match actual concurrent
utility load data to conduct
and system intermittency?
| In this case, daily, hourly, or even subhourly data may|
be needed for a specific time period, which cannot be
provided by TMY data.
| What are the temporal and
spatial characteristics of
the data sources available
to the developer, and how
do these characteristics
influence the evaluation
of system performance?
Satellite data usually represent
snapshots in time due to the
scanning characteristics of the
on-board radiometers and are
typically considered to range
from nearly instantaneous to
about 5-minute averages. For
SUNY satellite data used in the
NSRDB, individual pixel size
is 1 km, and the pixel is at the
center of the 10-km grid cell.
Newer satellite-based methodologies
now average the 1-km
pixel to 3- or 5-km grid cells
| Example: Measured solar data apply to a specific|
location, and are usually recorded at short time intervals
(6 minutes or less), then averaged to the desired time
interval (often hourly).
Example: Surface modeled data (e.g., NSRDB/METSTAT)
are somewhat smoothed, because they are based on
cloud cover observations that can be seen from a point
location, typically a circle 40 km in radius, averaged over
roughly a 30-minute period.