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

fYear

2013

fDate

22-24 Sept. 2013

Firstpage

1

Lastpage

6

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;

fLanguage

English

Publisher

ieee

Conference_Titel

North American Power Symposium (NAPS), 2013

Conference_Location

Manhattan, KS

Type

conf

DOI

10.1109/NAPS.2013.6666901

Filename

6666901