Accommodating High Levels of Variable Generation - NERC

2.4. Principal Characteristics of Wind and Solar Generation
Characteristics of Power Systems & Variable Generation
It is vital to understand the specific attributes of variable generation, which correspond to the
type and variety of both their fuel source and environment. This section provides a high-level
view of the characteristics of the two variable resources which are undergoing rapid growth:
wind and solar.
2.4.1. Wind Resources
Many of the regions in North America that are well suited for wind generation development (i.e.
offering a high wind capacity factor) tend to be remote from demand and existing transmission
infrastructure. Some excellent areas for wind generation development in North American
include the province of Québec, the panhandle and western regions of Texas, the southern
regions of Alberta, many regions in British Columbia (particularly the North Coast and
Vancouver Island), coastal and high elevation sites in New Brunswick and New England, many
areas of Midwest especially in the Dakotas and Wyoming, and High Desert areas of California.
The degree to which wind matches demand may differ widely in different geographic areas and
at different times of the year. Therefore, it is not possible to generalize the pattern of wind
generation across the NERC region. However, one important characteristic shared by all types
of wind power is their diurnal and seasonal pattern (i.e. peak output can occur in the morning and
evening of the day and may have higher outputs in spring and fall). Some wind regimes are
driven by daily thermal cycles, whereas others are driven primarily by meteorological
atmospheric dynamics.
Supply surplus conditions can also result when wind energy is available during times of low
demand (quite typically due to daily thermal cycles) and these situations will generally be dealt
with through operating procedures and wind power management. Because the same variables
that impact demand can also impact the output of wind resources, it is critical to ensure wind
data comes from the same time period as demand data whenever demand and wind power are
compared. Because weather is a common driver for demand and wind, analysis should take into
account the complex correlation between them.
A key characteristic of wind power is its longer-term ramping attribute, which can be much
different than its variability in the shorter term. In the short-term variability, there is
considerable diversity in the output from wind turbines within a single wind plant, and an even
larger diversity among wind plants dispersed over a wider geographic area. Such spatial
variation in wind speed makes the combined output from many turbines significantly less
variable than that of a single turbine. In fact, the aggregate energy output from wind plants
spread over a reasonably large area tends to remain relatively constant on a minute-to-minute
time frame, with changes in output tending to occur gradually over an hour or more. These
longer term changes are associated with wind ramping characteristics, which can present
operating challenges. Figure 2.4 below shows an example of California wind generation from 5
Accommodating High Levels of Variable Generation 15