Forecasting based energy management of flywheel energy storage system connected to a wind power plant

Abstract

The active power output of a wind power system needs regulation due to the stochastic nature of wind speed. A flywheel energy storage system (FESS) is a viable option for active power regulation in a wind power plant. An efficient energy management system (EMS) for FESS is required for healthy operation of the overall connected system. A wind speed forecasting based EMS has been proposed in this paper. It utilizes the repeated wavelet transform based ARIMA model for very short-term wind speed forecasting, which has been proven to be better than the methods that exist in the literature. An artificial neural network based model is used to translate the forecasted wind speed to instantaneous wind power output. Considering the initial energy of FESS, an optimization technique has been used to calculate the speed command to be given to FESS for a given energy exchanged with the grid. The proposed control approach extends FESS usability as an energy exchange system for a period of large change in wind speed where the normal control approach saturates the FESS speed. The feasibility of the proposed EMS algorithm has been tested in MATLAB/Simulink.