The strong growth that is felt at the level of photovoltaic (PV) power generation craves for more sophisticated and accurate forecasting methods that could be able to support its proper integration into the energy distribution network. Through the combination of the vector autoregression model (VAR) with the least absolute shrinkage and selection operator (LASSO) framework, a set of sparse VAR structures can be obtained in order to capture the dynamic of the underlying system. The robust and efficient alternating direction method of multipliers (ADMM), well known for its great ability dealing with high-dimensional data (scalability and fast convergence), is applied to fit the resulting LASSO-VAR variants. This spatial-temporal forecasting methodology has been tested, using 1-hour and 15-minutes resolution, for 44 microgeneration units time-series located in a city in Portugal. A comparison with the conventional autoregressive (AR) model is performed leading to an improvement up to 11%.

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.

This paper presents a study on dynamic line rating (DLR) forecasting procedure aimed at developing a new methodology able to forecast future ampacity values for rare and extreme events. This is motivated by the belief that to apply DLR network operators must be able to forecast their values and this must be based on conservative approaches able to guarantee the safe operation of the network. The proposed methodology can be summarised as follows: firstly, probabilistic forecasts of conductors’ ampacity are calculated with a non-parametric model, secondly, the lower part of the distribution is replaced with a new distribution calculated with a parametric model. The paper presents also an evaluation of the proposed methodology in network operation, suggesting an application method and highlighting the advantages. The proposed forecasting methodology delivers a high improvement of the lowest quantiles’ reliability, allowing perfect reliability for the 1% quantile and a reduction of roughly 75% in overconfidence for the 0.1% quantile.