Variance estimates of wind plant capacity credit
As the utility industry adapts to meet the changing regulatory and business climate, it is becoming increasingly important for utilities to identify and quantify the risks in various aspects of doing business. To reduce the risk of depending too heavily on one specific type of generation or fuel, generation expansion planning techniques are incorporating methods of portfolio diversification theory. Financial option theory is also used to evaluate the relative costs of building now or building later. Applying these theories to utility planning helps utilities assess risks in the emerging competitive environment. Risk is typically measured as a variance. For example, the risk associated with an investment can be characterized by the rate-of-return variance. Many studies that calculate the capacity credit of a wind plant do not calculate its variance, and therefore ignore risk. A capacity credit that is calculated in this way can be far different than the long-term average value. This problem is compounded by the usual method of calculating capacity credit, which depends very heavily on the level of wind generation during the system peak hours. A small change in wind power during the peak can have a dramatic effect on the capacity credit. This problem is further compounded by the limited availability of multi-year wind data sets that can be used in utility production cost modeling. For example, a study that uses a single year of data and finds a 30% capacity credit may be based on a wind generation pattern that is not at all typical. Although the preferred approach would be to use many years of wind data to obtain a range of capacity credit estimates, this is not always possible. This paper describes a technique that can help generation planners evaluate the variance of the capacity credit for wind power plants when there is limited wind data, and also shows some results of these calculations.
|Year of publication:||
|Subject:||wind energy | energy planning, policy and economy | WIND POWER PLANTS | COMPUTERIZED SIMULATION | OPERATION | CAPACITY | MARKOV PROCESS | DIAGRAMS | NUMERICAL DATA | MONTE CARLO METHOD|
|Type of publication:||Other|
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