This work explores a Principal Component Analysis (PCA) in combination with two post-processing techniques for the prediction of wind power produced over Sicily, and of solar irradiance produced over the Oklahoma Mesonet. For wind power, the study is conducted over a 2-year long period, with hourly data of the aggregated wind power output of the island. The 0-72 hour wind predictions are generated with the limited-area Regional Atmospheric Model System (RAMS), with boundary conditions provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) deterministic forecast. For solar irradiance, we consider daily data of the aggregated solar radiation energy output (based on the Kaggle competition dataset) over an 8-year long period. Numerical Weather Prediction data for the contest come from the National Oceanic & Atmospheric Administration -Earth System Research Laboratory (NOAA/ESRL) Global Ensemble Forecast System (GEFS) Reforecast Version 2. The PCA is applied to reduce the datasets dimension. A Neural Network (NN) and an Analog Ensemble (AnEn) post-processing are then applied on the PCA output to obtain the final forecasts. The study shows that combining PCA with these post-processing techniques leads to better results when compared to the implementation without the PCA reduction.
We propose a stochastic model for the daily operation scheduling of a generation system including pumped storage hydro plants and wind power plants, where the uncertainty is represented by the hourly wind power production. In order to assess the value of the stochastic modeling, we discuss two case studies: in the former the scenario tree is built so as to include both low and high wind power production scenarios, in the latter the scenario tree is built on historical wind speed data covering a time span of one and a half year. The Value of the Stochastic Solution, computed by a modified new procedure, shows that in scenarios with low wind power production the stochastic solution allows the producer to obtain a profit which is greater than the one associated to the deterministic solution. In-sample stability of the optimal function values for increasing number of scenarios is reported.
The electricity system is subject to continuous evolution stimulated by the integration of renewable energy sources. This transformation is having deep impacts on the planning and operation of networks and new potential roles of system operators are currently investigated. In particular, having assumed a future in which the energy demand is largely satisfied by distributed generation, it can be reasonably expected that distribution networks will be soon enabled to offer balancing and regulation services to the market. In this scenario, the distribution operator may aggregate all the local dispatchable resources and, from their combination, a single capability can be obtained to represent the flexibility limits of the entire network. This study presents few simple and intuitive methods for a fast and accurate construction of this equivalent capability.
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