  In recent years there has been a resurgence of interest in generation
adequacy risk assessment, due to the need to include variable generation
renewables within such calculations. This paper will describe new statistical
approaches to estimating the joint distribution of demand and available VG
capacity; this is required for the LOLE calculations used in many statutory
adequacy studies, for example those of GB and PJM. The most popular estimation
technique in the VG-integration literature is `hindcast', in which the historic
joint distribution of demand and available VG is used as a predictive
distribution. Through the use of bootstrap statistical analysis, this paper
will show that due to extreme sparsity of data on times of high demand and low
VG, hindcast results can suffer from sampling uncertainty to the extent that
they have little practical meaning. An alternative estimation approach, in
which a marginal distribution of available VG is rescaled according to demand
level, is thus proposed. This reduces sampling uncertainty at the expense of
the additional model structure assumption, and further provides a means of
assessing the sensitivity of model outputs to the VG-demand relationship by
varying the function of demand by which the marginal VG distribution is
rescaled.
