Title :
Lifetime cost optimized wind power control using hybrid energy storage system
Author :
Dongsheng Li ; Fenglong Lu ; Qin Lv ; Li Shang
Author_Institution :
Tongji Univ., Shanghai, China
Abstract :
This paper presents the use of hybrid energy storage, composed of ultracapacitor and Lithiumion battery, to improve wind power stability. A control algorithm based on artificial neural network is proposed to manage the run-time use of the hybrid energy storage system to (1) optimize wind power predictability hence power grid stability, and (2) minimize the overall lifetime cost of the energy storage system. Evaluations using wind farm data demonstrate that, compared with two recently proposed control methods, the proposed control algorithm can extend system lifetime by 62% and 143%, and reduce the overall lifetime energy storage system cost (20 years) by 41% and 59%, respectively.
Keywords :
battery storage plants; neural nets; power engineering computing; power generation control; power grids; power system stability; secondary cells; supercapacitors; wind power plants; Li; artificial neural network; hybrid energy storage system; lifetime cost optimized wind power control; lithium-ion battery; power grid stability; proposed control algorithm; system lifetime; ultracapacitor; wind farm data; wind power stability; Artificial neural networks; Batteries; Supercapacitors; Wind farms; Wind forecasting; Wind power generation;
Conference_Titel :
North American Power Symposium (NAPS), 2013
Conference_Location :
Manhattan, KS
DOI :
10.1109/NAPS.2013.6666901
