Determination of characteristic parameters of battery energy storage system for wind farm. Issue 1 (1st January 2014)

• Record Type: Journal Article
• Authors: Zhang, Kun; Mao, Chengxiong; Xie, Junwen; Lu, Jiming; Wang, Dan; Zeng, Jie; Chen, Xun; Zhang, Junfeng
• Is Part Of: IET renewable power generation. Volume 8:Issue 1(2014)
• Page Start: 22
• Page End: 32
• Publication Date: 2014-01-01
• Subjects: battery storage plants -- genetic algorithms -- neural nets -- power engineering computing -- power generation economics -- power grids -- wind power plants; battery energy storage system -- wind farm -- BESS -- power fluctuation -- battery charge‐discharge power -- time constant -- level of smoothing -- power grid -- economic cost -- artificial neural network‐based long‐term model -- EC -- LOS -- genetic algorithm -- mathematical model; Renewable energy sources -- Periodicals; 333.79405
• DOI: 10.1049/iet-rpg.2012.0385
• Languages: English
• ISSNs: 1752-1416

Abstract

Abstract : Integrating a battery energy storage system (BESS) with a wind farm can smooth power fluctuations from the wind farm. Battery storage capacity ( C ), maximum charge/discharge power of battery ( P ) and smoothing time constant ( T ) for the control system are three most important parameters that influence the level of smoothing (LOS) of output power transmitted to the grid. The economic cost (EC) of a BESS should also be taken into consideration when determining the characteristic parameters of BESS ( C, P ). In this study, an artificial neural network‐based long‐term model of evaluated BESS technical performance and EC is established to reflect the relationship between the three parameters ( C, P, T ) and LOS of output power transmitted to the grid, the EC of BESS. After that, genetic algorithm is used to find optimal parameter combination of C, P and T by optimising the objective function derived from the mathematical model constructed. The simulation results of the example indicate that the parameter combination of C, P and T obtained by the proposed method can better not only meet the technical demand but also achieve maximum economic profit.