Accommodating High Levels of Variable Generation - NERC

Transmission Planning & Resource Adequacy
ELCC approach is also very much dependent on system characteristics (e.g. interconnection,
storage, fuel availability, and hydro-dominated systems pose complications).
Given that the hourly variable generation output will be different in any year based on
availability of the primary fuel, planners must attempt to ensure that they have an accurate
representation of the capacity values of the variable generation. Presently, the best approach is
to explicitly represent the variable generation output as the historical 8,760 hourly variable
generation output from measurements or NWP models that is time synchronized to the system
demand 8,760 time series. Because the variable generation output varies from year to year,
multiple years of 8,760 variable generation data must be used to generate the aggregate LOLE
results across the multiple simulations considered. The concern with this approach is
determining how many years of variable generation output data are adequate to accurately reflect
the behavior of variable generation as a capacity resource. Future analysis techniques and tools
may allow for a truer probabilistic representation of the variable generation output at each hour,
but the inherent correlations between demand and variable generation output levels must be
retained. Thus, any probabilistic approach must not decouple the specific weather-driven
correlation of variable generation output and demand that characterizes the absolute system peak
hours.
The simplified Capacity Factor (CF) approach attempts to approximate the more rigorous ELCC
approach by assuming that the demonstrated output of the variable generation (calculated using a
regression method from historical or synthesized data) is available during time periods which
typically reflect high-risk reliability hours. 54 The selection of specific time periods for the CF
method will likely differ across the continent and would depend on the specific characteristics of
the region and the demand shape. Several entities in the U.S. use peak period definitions to
calculate an approximate wind capacity value (sometimes referred to as “Net Qualifying
Capacity”), as illustrated in Figure 3.3. 55 As the number of hours included in the time period
increases, the results from the CF and ELCC approaches tend to converge. The ELCC method is
always considered the more accurate method to calculate the capacity value of a variable
generator, but requires much more data and computational resources than the CF approach.
The correlation between variable generation technologies and demand is an important factor in
determining a capacity value. For example, wind and solar technologies typically have patterns
that are driven by seasonal and diurnal cycles. Wind tends to be correlated across a region and
the capacity value of wind in relative terms decreases as the penetration of wind increases. This
phenomenon is consistently observed in capacity value studies. As incremental amounts of wind
54 For example, see http://www.nwcouncil.org/library/2008/2008-07.pdf
55 M. Milligan and K. Porter, “Determining the Capacity Value of Wind: An Updated Survey of Methods and
Implementation,” Presented at Wind Power 2008, June 2008, Houston, Texas
Accommodating High Levels of Variable Generation 39