Wind Variability

One of the most critical features of wind generation is the variability of wind. Wind speeds vary with time of day, time of year, height above ground, and location on the earth’s surface. This makes wind generators into what might be called energy producers rather than power producers.

That is, it is easier to estimate the energy production for the next month or year than it is to estimate the power that will be produced at 4:00 PM next Tuesday. Wind power is not dispatchable in the same manner as a gas turbine. A gas turbine can be scheduled to come on at a given time and to be turned off at a later time, with full power production in between. A wind turbine produces only when the wind is available.

At a good site, the power output will be zero (or very small) for perhaps 10% of the time, rated for perhaps another 10% of the time, and at some intermediate value the remaining 80% of the time.

This variability means that some sort of storage is necessary for a utility to meet the demands of its customers, when wind turbines are supplying part of the energy. This is not a problem for penetrations of wind turbines less than a few percent of the utility peak demand. In small concentrations, wind turbines act like negative load. That is, an increase in wind speed is no different in its effect than a customer turning off load. The control systems on the other utility generation sense that generation is greater than load, and decrease the fuel supply to bring generation into equilibrium with load. In this case, storage is in the form of coal in the pile or natural gas in the well.

 

An excellent form of storage is water in a hydroelectric lake. Most hydroelectric plants are sized large enough to not be able to operate full-time at peak power. They therefore must cut back part of the time because of the lack of water.

A combination hydro and wind plant can conserve water when the wind is blowing, and use the water later, when the wind is not blowing. When high-temperature superconductors become a little less expensive, energy storage in a magnetic field will be an exciting possibility. Each wind turbine can have its own superconducting coil storage unit.

This immediately converts the wind generator from an energy producer to a peak power producer, fully dis-patchable. Dis-patchable peak power is always worth more than the fuel cost savings of an energy producer. Utilities with adequate base load generation (at low fuel costs) would become more interested in wind power if it were a dis-patchable peak power generator.

The variation of wind speed with time of day is called the diurnal cycle. Near the earth’s surface, winds are usually greater during the middle of the day and decrease at night. This is due to solar heating, which causes “bubbles” of warm air to rise. The rising air is replaced by cooler air from above. This thermal mixing causes wind speeds to have only a slight increase with height for the first hundred meters or so above the earth. At night, however, the mixing stops, the air near the earth slows to a stop, and the winds above some height (usually 30 to 100 m) actually increase over the daytime value. A turbine on a short tower will produce a greater proportion of its energy during daylight hours, while a turbine on a very tall tower will produce a greater proportion at night.

As tower height is increased, a given generator will produce substantially more energy.

However, most of the extra energy will be produced at night, when it is not worth very much. Standard heights have been increasing in recent years, from 50 to 65 m or even more. A taller tower gets the blades into less turbulent air, a definite advantage.

The disadvantages are extra cost and more danger from overturning in high winds. A very careful look should be given the economics before buying a tower that is significantly taller than whatever is sold as a standard height for a given turbine.

Wind speeds also vary strongly with time of year. In the southern Great Plains (Kansas, Oklahoma, and Texas), the winds are strongest in the spring (March and April) and weakest in the summer (July and August). Utilities here are summer peaking, and hence need the most power when winds are the lowest and the least power when winds are highest. The diurnal variation of wind power is thus a fairly good match to utility needs, while the yearly variation is not.

TABLE 1
Monthly Average Wind Speed in MPH and Projected Energy Production at 65 m, at a Good Site in Southern Kansas

	10 m	60 m	Energy		10 m	60 m	Energy
Month	Speed	Speed	(MWh)	Month	Speed	Speed	(MWh)
1/96	14.9	20.3	256	1/97	15.8	21.2	269
2/96	16.2	22.4	290	2/97	14.7	19.0	207
3/96	17.6	22.3	281	3/97	17.4	22.8	291
4/96	19.8	25.2	322	4/97	15.9	20.4	242
5/96	18.4	23.1	297	5/97	15.2	19.8	236
6/96	13.5	18.2	203	6/97	11.9	16.3	167
7/96	12.5	16.5	169	7/97	13.3	18.5	212
8/96	11.6	16.0	156	8/97	11.7	16.9	176
9/96	12.4	17.2	182	9/97	13.6	19.0	211
10/96	17.1	23.3	320	10/97	15.0	21.1	265
11/96	15.3	20.0	235	11/97	14.3	19.7	239
12/96	15.1	20.1	247	12/97	13.6	19.5	235

The variability of wind with month of year and height above ground is illustrated in Table 1. These are actual wind speed data for a good site in Kansas, and projected electrical generation of a Vestas turbine (V47-660) at that site. Anemometers were located at 10, 40, and 60 m above ground. Wind speeds at 40 and 60 m were used to estimate the wind speed at 65 m (the nominal tower height of the V47-660) and to calculate the expected energy production from this turbine at this height. Data have been normalized for a 30-day month. There can be a factor of two between a poor month and an excellent month (156 MWh in 8/96 to 322 MWh in 4/96). There will not be as much variation from one year to the next, perhaps 10 to 20%.

A wind power plant developer would like to have as long a data set as possible, with an absolute minimum of one year. If the one year of data happens to be for the best year in the decade, followed by several below average years, a developer could easily get into financial trouble. The risk gets smaller if the data set is at least two years long.

One would think that long-term airport data could be used to predict whether a given data set was collected in a high or low wind period for a given part of the country, but this is not always true. One study showed that the correlation between average annual wind speeds at Russell, Kansas, and Dodge City, Kansas, was 0.596 while the correlation between Russell and Wichita was 0.115.

The terrain around Russell is very similar to that around Wichita, and there is no obvious reason why wind speeds should be high at one site and low at the other for one year, and then swap roles the next year.

There is also concern about long-term variation in wind speeds. There appears to be an increase in global temperatures over the past decade or so, which would probably have an impact on wind speeds. It also appears that wind speeds have been somewhat lower as temperatures have risen, at least in Kansas. It appears that wind speeds can vary significantly over relatively short distances. A good data set at one location may under-predict or over-predict the winds at a site a few miles away by as much as 10 to 20%. Airport data collected on a 7-m tower in a flat river valley may underestimate the true surrounding hilltop winds by a factor of two.

If economics are critical, a wind power plant developer needs to acquire rights to a site and collect wind speed data for at least one or two years before committing to actually constructing turbines there.