Energy Storage Sizing Taking into Account Forecast Uncertainties and Receding Horizon Operation

Kyri Baker, Gabriela Hug, Xin Li

Research output: Contribution to journalArticlepeer-review

150 Scopus Citations

Abstract

Energy storage systems (ESS) have the potential to be very beneficial for applications such as reducing the ramping of generators, peak shaving, and balancing not only the variability introduced by renewable energy sources, but also the uncertainty introduced by errors in their forecasts. Optimal usage of storage may result in reduced generation costs and an increased use of renewable energy. However, optimally sizing these devices is a challenging problem. This paper aims to provide the tools to optimally size an ESS under the assumption that it will be operated under a model predictive control scheme and that the forecast of the renewable energy resources include prediction errors. A two-stage stochastic model predictive control is formulated and solved, where the optimal usage of the storage is simultaneously determined along with the optimal generation outputs and size of the storage. Wind forecast errors are taken into account in the optimization problem via probabilistic constraints for which an analytical form is derived. This allows for the stochastic optimization problem to be solved directly, without using sampling-based approaches, and sizing the storage to account not only for a wide range of potential scenarios, but also for a wide range of potential forecast errors. In the proposed formulation, we account for the fact that errors in the forecast affect how the device is operated later in the horizon and that a receding horizon scheme is used in operation to optimally use the available storage.
Original languageAmerican English
Pages (from-to)331-340
Number of pages10
JournalIEEE Transactions on Sustainable Energy
Volume8
Issue number1
DOIs
StatePublished - 2017

NREL Publication Number

  • NREL/JA-5D00-67113

Keywords

  • batteries
  • energy storage
  • optimal scheduling
  • power system planning
  • wind energy

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