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Generation Expansion Planning
Markov Process
Monte Carlo Method
Portfolio Diversification
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Variance Estimation
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Reliability evaluation of power systems
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Variance estimates of wind plant capacity credit
Variance estimates of wind plant capacity credit,10.2172/254973,Michael R. Milligan
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Variance estimates of wind plant capacity credit
(
Citations: 12
)
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Michael R. Milligan
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 rateofreturn 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 longterm 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 multiyear 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.
DOI:
10.2172/254973
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Citation Context
(6)
...Other related wind synthesis models are presented in [7] and [
8
]...
...1) Mean–VarianceNormalized ARMA Models: First [6], [
8
], timeseries 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 windspeed 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 Pudaruth
,
et 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. Pudaruth
,
et al.
Capacity credit variation in distribution systems
...A number of techniques can be used for this, and examples include the autoregressive 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 loadduration curve production cost model...
...This paper illustrates a technique that can be applied to any productioncost/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. Milligan
,
et al.
An enumerative technique for modeling wind power variations in product...
References
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A sequential simulation technique for adequacy evaluation of generating systems including wind energy
(
Citations: 69
)
R. Billinton
,
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,
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Journal:
IEEE Transactions on Energy Conversion  IEEE TRANS ENERGY CONVERS
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Reliability evaluation of power systems
(
Citations: 1021
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R. Billinton
,
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(
Citations: 8
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Michael Milligan
Published in 1996.
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Journal:
Nature
, vol. 180, no. 4595, pp. 10871088, 1957
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Citations
(12)
Sequential Reliability Forecasting for Wind Energy: Temperature Dependence and Probability Distributions
(
Citations: 4
)
Duncan S. Callaway
Journal:
IEEE Transactions on Energy Conversion  IEEE TRANS ENERGY CONVERS
, vol. 25, no. 2, pp. 577585, 2010
Wind integration into various generation mixtures
(
Citations: 9
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Jesse D. Maddaloni
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,
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Renewable Energy
, vol. 34, no. 3, pp. 807814, 2009
Locational Capacity Credit Evaluation
(
Citations: 2
)
Girish Rai Pudaruth
,
Furong Li
Journal:
IEEE Transactions on Power Systems  IEEE TRANS POWER SYST
, vol. 24, no. 2, pp. 10721079, 2009
Capacity credit variation in distribution systems
G. R. Pudaruth
,
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, pp. 17, 2008
Renewable energy as a natural gas price hedge: the case of wind
(
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Energy Policy  ENERG POLICY
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