This paper investigates the use of additional meteorological (temperature, wind) data, to obtain more reliable estimates of risk metrics for capacity adequacy in systems with significant wind generation. The advantage of this approach, which requires the careful verification of a number of statistical conditions (well satisfied in practice), is that a considerably longer dataset may be used to estimate the distribution of wind generation, and so also that of demand-net-of-wind, thereby overcoming the otherwise serious problem of a lack of data in the critical region of high demand and low wind. A further advantage is that a block-bootstrapping procedure may be used to assess the remaining uncertainty associated with wind generation, and the effect of that uncertainty on desired risk metrics. This is in contrast to the hindcast approach, where no such uncertainty estimation is possible and where there may be serious concerns about the calculated values of the risk metrics.
|Title of host publication||2018 IEEE International Conference on Probabilistic Methods Applied to Power Systems (PMAPS)|
|Publisher||Institute of Electrical and Electronics Engineers (IEEE)|
|Number of pages||6|
|Publication status||Published - 20 Aug 2018|