Power Profiling and Inherent Lag Prediction of a Wind Power Generating System for Its Integration to an Energy Storage System

A key challenge within the power sector is to address the issue of intermittency. It is the unavailability of energy at all times in order to meet the demand requirements. Intermittency is responsible for reducing the efficiency of the national infrastructure and can compromise energy security. Increasing use of renewable energy can cause the increasing intermittency. This is an important issue that needs to be dealt with. Predictive mechanisms based on historical data have been used previously to try and address energy security with renewables. However, the effectiveness of the predictive mechanisms are low. Going forward, energy storage systems will play a key role in securing the energy supply provided by renewables. Efficient use of energy storage relies on information about the generator system that it is coupled with. This paper aims to show that despite the inherent characteristics of renewable energy generation, the nature of mechanical generation of renewable systems can be equated and modelled. The model can provide the information required for energy storage coupling. The model equates the inherent lag using the torque values of the generator, as well as the generator´s velocity. The model is part of a larger framework that predicts the output power profile of the renewables, using an Artificial Neural Network (ANN). The predictive information can further improve the performance of the coupled energy storage system and address intermittency.