In recent years, the world has been actively promoting the development of wind power, photovoltaic, and other new energy. The inherent randomness and intermittency of wind power output have led to the reduction of supply-side controllability and stability, and the power system is facing severe challenges. Aiming at the irregular fluctuation of wind power output and the restriction between the charge and discharge depth and service life of hybrid energy storage equipment, a hierarchical control strategy for a hybrid energy storage system based on improved GA-SVR wind power prediction is proposed. First of all, the short-term prediction of wind power output is carried out using Support Vector Regression (SVR), and the improved genetic algorithm is used for optimization. Then, the result obtained from the prediction calculation is used as the wind power output, and the internal initial power of each energy storage element is obtained through the hybrid energy storage capacity configuration method and further controlled through hierarchical control regulation. Finally, a simulation experiment is carried out on the proposed control strategy. The simulation algorithm shows that the proposed method can not only enhance the effective output of new energy but also extend the service life of energy storage and ensure the safe and stable operation of the power system.

Research on Hierarchical Control Strategy of ESS in Distribution Based on GA-SVR Wind Power Forecasting

Pau, Giovanni;
2023-01-01

Abstract

In recent years, the world has been actively promoting the development of wind power, photovoltaic, and other new energy. The inherent randomness and intermittency of wind power output have led to the reduction of supply-side controllability and stability, and the power system is facing severe challenges. Aiming at the irregular fluctuation of wind power output and the restriction between the charge and discharge depth and service life of hybrid energy storage equipment, a hierarchical control strategy for a hybrid energy storage system based on improved GA-SVR wind power prediction is proposed. First of all, the short-term prediction of wind power output is carried out using Support Vector Regression (SVR), and the improved genetic algorithm is used for optimization. Then, the result obtained from the prediction calculation is used as the wind power output, and the internal initial power of each energy storage element is obtained through the hybrid energy storage capacity configuration method and further controlled through hierarchical control regulation. Finally, a simulation experiment is carried out on the proposed control strategy. The simulation algorithm shows that the proposed method can not only enhance the effective output of new energy but also extend the service life of energy storage and ensure the safe and stable operation of the power system.
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11387/155784
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