A model predictive approach for community battery energy storage system optimization

Pezeshki, H.; Wolfs, Peter; Ledwich, G.

doi:10.1109/PESGM.2014.6938788

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Authors

Pezeshki, H.

Wolfs, Peter

Ledwich, G.

Date

2014

Type

Conference Paper

Metadata

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Citation

Pezeshki, H. and Wolfs, P. and Ledwich, G. 2014. A model predictive approach for community battery energy storage system optimization.

Source Title

IEEE Power and Energy Society General Meeting

DOI

10.1109/PESGM.2014.6938788

ISSN

1944-9925

School

Department of Electrical and Computer Engineering

URI

http://hdl.handle.net/20.500.11937/56016

Collection

Curtin Research Publications

Abstract

© 2014 IEEE. This paper presents an efficient algorithm for optimizing the operation of battery storage in a low voltage distribution network with a high penetration of PV generation. A predictive control solution is presented that uses wavelet neural networks to predict the load and PV generation at hourly intervals for twelve hours into the future. The load and generation forecast, and the previous twelve hours of load and generation history, is used to assemble load profile. A diurnal charging profile can be compactly represented by a vector of Fourier coefficients allowing a direct search optimization algorithm to be applied. The optimal profile is updated hourly allowing the state of charge profile to respond to changing forecasts in load.