This paper presents a supervisory control unit (SCU) combined with short-term ahead wind speed prediction for proper and effective management of the stored energy in a small capacity flywheel energy storage system (FESS) which is used to mitigate the output power fluctuations of an aggregated wind farm. Wind speed prediction is critical for a wind energy conversion system since it may greatly influence the issues related to effective energy management, dynamic control of wind turbine, and improvement of the overall efficiency of the power generation system. In this study, a wind speed prediction model is developed by artificial neural network (ANN) which has advantages over the conventional prediction schemes including data error tolerance and ease in adaptability. The proposed SCU-based control would help to reduce the size of the energy storage system for minimizing wind power fluctuation taking the advantage of prediction scheme. The model for prediction using ANN is developed in MATLAB/Simulink and interfaced with PSCAD/EMTDC. Effectiveness of the proposed control system is illustrated using real wind speed data in various operating conditions.

Abstract-With the increased integration of wind energy into power networks it has become more important to have a reliable system for mitigating the fluctuations of output power supplied to the grid. This paper proposes a new control scheme to smooth the output power fluctuations of an aggregated wind firm using Flywheel energy storage system (FESS) and shortterm ahead prediction of wind speed. In the proposed system, the kinetic energy of the FEES is utilized to smooth the output power fluctuations of wind farm. In addition, the stored energy of FESS is utilized in most efficient way by correcting the power reference using prediction base control. This helps to reduce the overall system cost by keeping the size of the energy storage system at minimum. The effectiveness of the proposed control is verified by using PSCAD/EMTDC.

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