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Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage Public Deposited

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  • This paper demonstrates the use of model-based predictive control for energy storage systems to improve the dispatchability of wind power plants. Large-scale wind penetration increases the variability of power flow on the grid, thus increasing reserve requirements. Large energy storage systems collocated with wind farms can improve dispatchability of the wind plant by storing energy during generation over-the-schedule and sourcing energy during generation under-the-schedule, essentially providing on-site reserves. Model predictive control (MPC) provides a natural framework for this application. By utilizing an accurate energy storage system model, control actions can be planned in the context of system power and state-of-charge limitations. MPC also enables the inclusion of predicted wind farm performance over a near-term horizon that allows control actions to be planned in anticipation of fast changes, such as wind ramps. This paper demonstrates that model-based predictive control can improve system performance compared with a standard non-predictive, non-model-based control approach. It is also demonstrated that secondary objectives, such as reducing the rate of change of the wind plant output (i.e., ramps), can be considered and successfully implemented within the MPC framework. Specifically, it is shown that scheduling error can be reduced by 81%, reserve requirements can be improved by up to 37%, and the number of ramp events can be reduced by 74%.
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  • Halamay, D., Antonishen, M., Lajoie, K., Bostrom, A., & Brekken, T. K. A. (2014). Improving Wind Farm Dispatchability Using Model Predictive Control for Optimal Operation of Grid-Scale Energy Storage. Energies, 7(9), 5847-5862. doi:10.3390/en7095847
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  • This work was supported in part by the BPA, Central Lincoln People’s Utility District (PUD), Pacific Power, Portland General Electric (PGE), and Oregon Built Environment and Sustainable Technologies (BEST). Aspects of this work were also supported by the National Science Foundation under Grant No. 0846533.
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  • description.provenance : Made available in DSpace on 2014-11-14T17:42:43Z (GMT). No. of bitstreams: 2 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) HalamayDouglasEECSImprovingWindFarm.pdf: 2962371 bytes, checksum: 75af992fc92559fdfcba8d0dea2b09e5 (MD5) Previous issue date: 2014-09-05
  • description.provenance : Approved for entry into archive by Erin Clark(erin.clark@oregonstate.edu) on 2014-11-14T17:42:43Z (GMT) No. of bitstreams: 2 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) HalamayDouglasEECSImprovingWindFarm.pdf: 2962371 bytes, checksum: 75af992fc92559fdfcba8d0dea2b09e5 (MD5)
  • description.provenance : Submitted by Erin Clark (erin.clark@oregonstate.edu) on 2014-11-14T17:42:28Z No. of bitstreams: 2 license_rdf: 1370 bytes, checksum: cd1af5ab51bcc7a5280cf305303530e9 (MD5) HalamayDouglasEECSImprovingWindFarm.pdf: 2962371 bytes, checksum: 75af992fc92559fdfcba8d0dea2b09e5 (MD5)

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