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Publisher's copyright statement: c 2011 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. Additional information:Use policyThe full-text may be used and/or reproduced, and given to third parties in any format or medium, without prior permission or charge, for personal research or study, educational, or not-for-prot purposes provided that:• a full bibliographic reference is made to the original source • a link is made to the metadata record in DRO • the full-text is not changed in any way The full-text must not be sold in any format or medium without the formal permission of the copyright holders.Please consult the full DRO policy for further details. The capacity value of a generator is the contribution that a given generator makes to generation system adequacy. The variable and stochastic nature of wind sets it apart from conventional energy sources. As a result, the modeling of wind generation in the same manner as conventional generation for capacity value calculations is inappropriate. In this paper a preferred method for calculation of the capacity value of wind is described and a discussion of the pertinent issues surrounding it is given. Approximate methods for the calculation are also described with their limitations highlighted. The outcome of recent wind capacity value analyses in Europe and North America, along with some new analysis are highlighted with a discussion of relevant issues also given.Index Terms--Wind power, capacity value, effective load carrying capability, power system operation and planning

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Wind forecasting is an important consideration in integrating large amounts of wind power into the electricity grid. The wind power forecast error distribution assumed can have a large impact on the confidence intervals produced in wind power forecasting. In this work we examine the shape of the persistence model error distribution for ten different wind plants in the ERCOT system over multiple timescales. Comparisons are made between the experimental distribution shape and that of the normal distribution. The shape of the distribution is found to change significantly with the length of the forecasting timescale. The Cauchy distribution is proposed as a model distribution for the forecast errors and model parameters are fitted. Finally, the differences in confidence intervals obtained using the Cauchy distribution and the normal distribution are compared.

Greater penetrations of variable renewable generation on electric grids have resulted in increased levels of curtailment in recent years. Studies of renewable energy grid integration have found that curtailment levels may grow as the penetration of wind and solar energy generation increases. This paper reviews international experience with curtailment of wind and solar energy on bulk power systems in recent years, with a focus on eleven countries in Europe, North America, and Asia. It examines levels of curtailment, the causes of curtailment, curtailment methods and use of market-based dispatch, as well as operational, institutional, and other changes that are being made to reduce renewable energy curtailment.

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