A model predictive approach for community battery energy storage system optimization
© 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 a...
| Main Authors: | , , |
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| Format: | Conference Paper |
| Published: |
2014
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| Online Access: | http://hdl.handle.net/20.500.11937/56016 |
| _version_ | 1848759764924760064 |
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| author | Pezeshki, H. Wolfs, Peter Ledwich, G. |
| author_facet | Pezeshki, H. Wolfs, Peter Ledwich, G. |
| author_sort | Pezeshki, H. |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | © 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. |
| first_indexed | 2025-11-14T10:05:04Z |
| format | Conference Paper |
| id | curtin-20.500.11937-56016 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T10:05:04Z |
| publishDate | 2014 |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-560162017-09-13T16:11:02Z A model predictive approach for community battery energy storage system optimization Pezeshki, H. Wolfs, Peter Ledwich, G. © 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. 2014 Conference Paper http://hdl.handle.net/20.500.11937/56016 10.1109/PESGM.2014.6938788 restricted |
| spellingShingle | Pezeshki, H. Wolfs, Peter Ledwich, G. A model predictive approach for community battery energy storage system optimization |
| title | A model predictive approach for community battery energy storage system optimization |
| title_full | A model predictive approach for community battery energy storage system optimization |
| title_fullStr | A model predictive approach for community battery energy storage system optimization |
| title_full_unstemmed | A model predictive approach for community battery energy storage system optimization |
| title_short | A model predictive approach for community battery energy storage system optimization |
| title_sort | model predictive approach for community battery energy storage system optimization |
| url | http://hdl.handle.net/20.500.11937/56016 |