TY - JOUR
T1 - Scheduling framework using dynamic optimal power flow for battery energy storage systems
AU - Fan, Fulin
AU - Kockar, Ivana
AU - Xu, Han
AU - Li, Jingsi
N1 - © 2021 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting /republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
PY - 2022/1/25
Y1 - 2022/1/25
N2 - 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.
AB - 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.
KW - battery energy storage
KW - day(s)-ahead scheduling
KW - dynamic optimal power flow
KW - load smoothing
KW - renewable energy
U2 - 10.17775/CSEEJPES.2020.03710
DO - 10.17775/CSEEJPES.2020.03710
M3 - Article
VL - 8
SP - 271
EP - 280
IS - 1
ER -