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Variance estimates of wind plant capacity credit

Variance estimates of wind plant capacity credit,10.2172/254973,Michael R. Milligan

Variance estimates of wind plant capacity credit   (Citations: 12)
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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.
Published in 1996.
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    • ...Other related wind synthesis models are presented in [7] and [8]...
    • ...1) Mean–Variance-Normalized ARMA Models: First [6], [8], time-series models are fit to the wind data, and those models are subsequently simulated...
    • ...The latter objective is notable because, although there are several commonly cited methods available to synthesize wind-speed distributions [6]–[8], the ability of these approaches to capture real variation in wind reliability has yet to be tested...

    Duncan S. Callaway. Sequential Reliability Forecasting for Wind Energy: Temperature Depend...

    • ...These probabilistic methods can be subdividedintothreemainmodels:numericalprobabilistic[16], simulation [20], and analytic probabilistic [21]...
    • ...Previous works have modeled wind power as negative load and hence substracted the available wind power from the load [14]‐[16], [36]...

    Girish Rai Pudaruthet al. Locational Capacity Credit Evaluation

    • ...Previous works have modeled wind power as negative load and hence substracted the available wind power from the load [8,9,20,21]...

    G. R. Pudaruthet al. Capacity credit variation in distribution systems

    • ...A number of techniques can be used for this, and examples include the auto-regressive integrated moving average approach applied by Billinton et. al [1] and the Markov modeling applied by Milligan [3] and Milligan and Graham [2]...

    M. R. Milligan. Sliding Window Technique for Calculating System LOLP Contributions of ...

    • ...The first method was introduced by [7], and is an enumerated probabilistic approach (EPA)...
    • ...[7] illustrates a Monte Carlo method that is external to the load-duration curve production cost model...
    • ...This paper illustrates a technique that can be applied to any production-cost/reliability model, and extends earlier work by [7]...
    • ...Although these results appear to be consistent with the annual results reported in [7], here we have a larger variance of capacity credit because of the larger variation in wind plant output over the month than would occur over a year...

    Michael R. Milliganet al. An enumerative technique for modeling wind power variations in product...

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