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A model predictive approach for community battery energy storage system optimization

conference contribution
posted on 22.10.2018, 00:00 by H Pezeshki, Peter WolfsPeter Wolfs, G Ledwich
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. © 2014 IEEE.

Funding

Category 2 - Other Public Sector Grants Category

History

Start Page

1

End Page

5

Number of Pages

5

Start Date

27/07/2014

Finish Date

31/07/2014

eISSN

1944-9933

ISSN

1944-9925

ISBN-13

9781479964154

Location

Harbor, MD, USA

Publisher

IEEE

Place of Publication

Piscataway, NJ.

Peer Reviewed

Yes

Open Access

No

External Author Affiliations

Queensland University of Technology

Era Eligible

Yes

Name of Conference

IEEE Power & Energy Society General Meeting