Extensions of the dynamic programming framework: Battery scheduling, demand charges, and renewable integration

Morgan Jones, Matthew M. Peet

Research output: Contribution to journalArticlepeer-review

Abstract

We consider a general class of dynamic programming (DP) problems with nonseparable objective functions. We show that for any problem in this class, there exists an augmented-state DP problem that satisfies the principle of optimality and the solutions to which yield solutions to the original problem. Furthermore, we identify a subclass of DP problems with naturally forward separable objective functions for which this state-augmentation scheme is tractable. We extend this framework to stochastic DP problems, proposing a suitable definition of the principle of optimality. We then apply the resulting algorithms to the problem of optimal battery scheduling with demand charges using a data-based stochastic model for electricity usage and solar generation by the consumer.

Original languageEnglish (US)
Article number9116976
Pages (from-to)1602-1617
Number of pages16
JournalIEEE Transactions on Automatic Control
Volume66
Issue number4
DOIs
StatePublished - Apr 2021
Externally publishedYes

Keywords

  • Battery management systems
  • Dynamic programming
  • Optimal scheduling

ASJC Scopus subject areas

  • Control and Systems Engineering
  • Computer Science Applications
  • Electrical and Electronic Engineering

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