## Abstract

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.

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.

Original language | English |
---|---|

Publication status | Unpublished - 2014 |

## Keywords

- power system planning
- power system reliability
- Risk analysis
- wind power