Title :
Operating power reserve quantification through PV generation uncertainty analysis of a microgrid
Author :
Xingyu Yan ; Francois, Bruno ; Abbes, Dhaker
Author_Institution :
L2EP, EC de Lille, Villeneuve-d´Ascq, France
fDate :
June 29 2015-July 2 2015
Abstract :
Due to renewable energy sources (RES) variable nature and their wide integration into power systems, setting an adequate operating power reserve is important to compensate unpredictable imbalance between generation and consumption. However, this power reserve should be ideally minimized to reduce system cost with a satisfying security level. Although many forecasting methodologies have been developed for forecasting energy generation and load demand, management tools for decision making of operating reserve are still needed. This paper deals with power reserve quantification through uncertainty analysis with a photovoltaic (PV) generator. Indeed, using an artificial neural network based predictor (ANNs), PV power and load have been forecasted 24 hours ahead, and also forecasting errors have been predicted. Through forecasting uncertainty analysis, the power reserve quantification is calculated according to various risk indexes.
Keywords :
distributed power generation; neural nets; photovoltaic power systems; power engineering computing; ANN; PV generation uncertainty analysis; PV generator; RES; artificial neural network based predictor; microgrid; operating power reserve quantification; photovoltaic generator; power reserve quantification; renewable energy sources; uncertainty analysis; Artificial neural networks; Forecasting; Indexes; Load modeling; Probabilistic logic; Reliability; Uncertainty; Artificial Neural Networks; microgrid uncertainty; power reserve quantification; variability;
Conference_Titel :
PowerTech, 2015 IEEE Eindhoven
Conference_Location :
Eindhoven
DOI :
10.1109/PTC.2015.7232577