Setting the maximum import net transfer capacity under extreme RES integration scenarios

Abstract

In order to reduce the curtailment of renewable generation in periods of low load, operators can limit the import net transfer capacity (NTC) of interconnections. This paper presents a probabilistic approach to support the operator in setting the maximum import NTC value in a way that the risk of curtailment remains below a pre-specified threshold. Main inputs are the probabilistic forecasts of wind power and solar PV generation, and special care is taken regarding the tails of the global margin distribution (all generation-all loads and pumping), since the accepted thresholds are generally very low. Two techniques are used for this purpose: interpolation with exponential functions and nonparametric estimation of extreme conditional quantiles using extreme value theory. The methodology is applied to five representative days, where situations ranging from high maximum NTC values to NTC=0 are addressed. Comparison of the two techniques for modeling tails is also comprised.

Extreme Quantiles Dynamic Line Rating Forecasts and Application on Network Operation

“…In [1], the framework for calculating day ahead probabilistic forecast and the framework for its impact evaluation on network operation has been established. In [2,3] the improvement of extreme quantiles forecasting for wind power production has been explored. To help the reader, descriptions of the models developed in [1,2] are presented in Sections 3.1 and 3.2, respectively.…”

“…In [2,3] the improvement of extreme quantiles forecasting for wind power production has been explored. To help the reader, descriptions of the models developed in [1,2] are presented in Sections 3.1 and 3.2, respectively.…”

“…Considering a tail parameter , the authors consider the same parameter for all of the considered forecasts. In [2], where forecasts are carried out to determine the net transfer capacity between Portugal and Spain, it is also proposed to model forecasts for quantiles lower than 5% using exponential interpolation, but the values of are equal to the average power depending on the position belonging of the median forecast class. In this paper, the authors propose to use a similar approach for RTLR forecasting and conditional to a set of explanatory variables.…”

“…The basic methodology proposed in [2] is enhanced with a clustering approach to provide a conditional estimation of extreme quantiles associated to DLR and applied to the bi-level stochastic optimization described in [1] to evaluate the benefit of highly accurate DLR probabilistic forecasts for quantiles below 5%. This result is not automatic nor known in advance: in fact, wind power is a bounded variable limited between 0 and the rated power of the wind turbine or power plant, whilst RTLR is unbounded in the region of observation.…”

Towards Improved Understanding of the Applicability of Uncertainty Forecasts in the Electric Power Industry

“…When considering the tails of the predictive distribution, threshold predictors implemented with logistic regression can offer better convergence for extreme quantiles than with quantile regression [75]. Another alternative is to use conditional extreme value theory or exponential tails with conditional parameters [8].…”