How much back up does wind power need? 

Winds are notoriously footloose and hard to predict, but for grid operators keen to keep their customers’ lights on and hold down costs, agreeing on a method to measure the reliability of the fast-growing electricity source is vital.

Policymakers and operators around the world have come up with a range of ways to estimate wind power and a bunch of terms to measure wind power reliability, including capacity credit, capacity value and availability factor.

Capacity value is the proportion of a power plant’s installed capacity that can be absolutely relied upon for security of supply.

For a coal plant that figure is around 85 percent but for wind can range from zero to 30 percent, depending on which methodology you choose.

The range partly reflects actual differences across regions – for example in wind speed, interconnection and the share of wind power in wider generation.

But it also reflects different methods of calculation, and highlights the need for more consistency to avoid blackouts if wind power is over-estimated or spending too much on reserve capacity if it is under-estimated.

Specific measurements are vital to gauge the proportion of installed capacity that can be reasonably relied upon at any one time, whether during predictable demand surges or unexpected events such as an unplanned nuclear outage.

HERE’S THE MATH

Estimating capacity value, whether for variable renewable or conventional coal and gas-fired capacity, provides a systematic way to measure security of supply.

The risk of not meeting demand – called “loss of load” – can be expressed in various ways.

One standard term among grid planners is “Loss of Load Expectation” (LOLE).

According to a recent report by the British energy regulator, Ofgem, LOLE “represents the number of hours per year in which supply is expected to be lower than demand under normal operation of the system. Importantly, this is before any intervention by the System Operator, so does not represent the likelihood of customer disconnections.”

Capacity values are key in estimating the impact of new power plants on LOLE, pinpointing how much of the installed capacity can be relied upon.

In the case of wind, it is estimated to lie in the range of 0-30 percent of installed capacity, compared with more than 80 percent for baseload conventional, gas, coal, nuclear and hydro power.

BRITISH EXAMPLE

Ofgem last month calculated the reliability of wind power capacity in its six-year outlook for security of supply.

Britain in the near-term faces a greater risk of limited blackouts than historically, as the country shuts down polluting coal and ageing gas plants.

As a result, Ofgem is interested in capacity values during periods of peak demand, for example in January when heating and lighting needs are higher.

The regulator obtained local wind speeds from a re-analysis of NASA satellite weather data – a standard academic procedure to work around a lack of direct observations of wind power generation.

It converted wind speeds into power generation using observations from a sample of actual wind farms.

It then generated probability distributions for wind power output during peak demand annually through 2019, taking into account a doubling of wind power capacity over the period.

It added expected conventional generation and electricity demand to calculate what it terms the “Equivalent Firm Capacity” of wind power, which it found to be in a range from 17-24 percent of installed capacity.

The range refers to different assumptions for the amount of installed wind power.

Perhaps paradoxically, the greater the share of wind power in the generation mix the smaller the proportion which can be relied upon, because on still days with no wind there will be a greater risk of a loss of load.

UNCERTAINTY

Uncertainty about how to measure wind power capacity values is a concern.

Even within Britain, Ofgem calculated a very different value over the next five years (17-24 percent) than the country’s transmission operator National Grid for the winter of 2011/12 (8 percent).

Ofgem says the National Grid’s calculation method – direct observation of wind power generation – contrasts with its statistical modelling of all supply and demand, and is inappropriate for assessing security of supply.

“The large difference in these numbers reflects two very different approaches. The (National Grid’s) Winter Outlook approach is based on observations of the output of wind at peak times. By its nature this is a small number of observations, and it is therefore possible that the wind output at the time of observation could have been very different.

“We do not consider this approach appropriate for a capacity adequacy analysis, as it represents a pessimistic estimate of the availability of wind in isolation from the rest of the system,” Ofgem said last month.

In Europe, estimates among transmission system operators for wind power capacity value during peak demand vary from zero (in Austria, Cyprus and Estonia) to up to 30 percent (in France and Portugal), according to the European Network of Transmission System Operators for Electricity.

In the United States, grid planners projected wind capacity values during peak demand in 2019, ranging from 8 percent in the Midwest coordinating region, to 18.5 percent in the western United States and Canada region, called WECC (Western Electricity Coordinating Council). (Chart 2)

The U.S. Energy Information Administration (EIA) illustrated the importance of getting capacity values right, with the example of a region projecting 20 gigawatts of wind capacity by 2019.

“If it decreased its capacity value by one percentage point from 12 percent to 11 percent, and had to replace that lost wind capacity in order to meet its target reserve margin, it would require an additional 200 megawatts of capacity resources.”

If gas-fired power supplied the difference, that would cost $195 million in upfront capital, EIA estimated in 2011.

But the range is up to 30 – not one – percentage points, underlining how important it is to agree on a consistent modelling approach for a more accurate balancing of demand and supply, to save costs and better ensure grid reliability.