Scheduling framework using dynamic optimal power flow for battery energy storage systems

Fulin Fan, Ivana Kockar, Han Xu, Jingsi Li

Research output: Contribution to journalArticlepeer-review

13 Citations (Scopus)
41 Downloads (Pure)


Battery energy storage systems (BESS) are instrumental in the transition to a low carbon electrical network with enhanced flexibility, however, the set objective can be accomplished only through suitable scheduling of their operation. This paper develops a dynamic optimal power flow (DOPF)-based scheduling framework to optimize the day(s)-ahead operation of a grid-scale BESS aiming to mitigate the predicted limits on the renewable energy generation as well as smooth out the network demand to be supplied by conventional generators. In DOPF, all the generating units, including the ones that model the exports and imports of the BESS, across the entire network and the complete time horizon are integrated on to a single network. Subsequently, an AC-OPF is applied to dispatch their power outputs to minimize the total generation cost, while satisfying the power balance equations, and handling the unit and network constraints at each time step coupled with intertemporal constraints associated with the state of charge (SOC). Furthermore, the DOPF developed here entails the frequently applied constant current-constant voltage charging profile, which is represented in the SOC domain. Considering the practical application of a 1 MW BESS on a particular 33 kV network, the scheduling framework is designed to meet the pragmatic requirements of the optimum utilization of the available energy capacity of BESS in each cycle, while completing up to one cycle per day.
Original languageEnglish
Pages (from-to)271-280
Number of pages10
JournalCSEE Journal of Power and Energy Systems
Issue number1
Early online date30 Dec 2021
Publication statusPublished - 25 Jan 2022


  • battery energy storage
  • day(s)-ahead scheduling
  • dynamic optimal power flow
  • load smoothing
  • renewable energy


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