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Model Predictive Control of a Microgrid With Plug-In Vehicles: Error Modeling and the Role of Prediction Horizon

[+] Author Affiliations
Diane L. Peters, Abigail R. Mechtenberg, John Whitefoot, Panos Y. Papalambros

University of Michigan, Ann Arbor, MI

Paper No. DSCC2011-6030, pp. 787-794; 8 pages
doi:10.1115/DSCC2011-6030
From:
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control
  • ASME 2011 Dynamic Systems and Control Conference and Bath/ASME Symposium on Fluid Power and Motion Control, Volume 1
  • Arlington, Virginia, USA, October 31–November 2, 2011
  • Conference Sponsors: Dynamic Systems and Control Division
  • ISBN: 978-0-7918-5475-4
  • Copyright © 2011 by ASME

abstract

We demonstrate the use of model predictive control (MPC) for a microgrid with plug-in vehicles. A predictive model is developed based on a hub model of the microgrid, and the control is optimized for minimum generator fuel usage. A variety of horizons and levels of prediction error are used in the optimization. A new method to model expected load and error is presented based on radial basis functions. Results show that for a given prediction horizon, as the level of prediction error increases, the amount of fuel used increases. Results also show that in some cases there is little benefit in extending the prediction horizon. While an extended prediction horizon does result in increased use of battery storage, this does not necessarily produce significant decreases in fuel usage. This result is analyzed and explained in terms of battery charging and discharging limitations.

Copyright © 2011 by ASME

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