The ever-growing higher penetration of distributed
energy resources (DERs) in low-voltage (LV) distribution systems
brings both opportunities and challenges to voltage support and
regulation. This paper proposes a deep reinforcement learning
(DRL)-based scheduling scheme of energy storage systems
(ESSs) to mitigate system voltage deviations in unbalanced LV
distribution networks. The ESS-based voltage regulation problem
is formulated as a multi-stage quadratic stochastic program,
with the objective of minimizing the expected total daily voltage
regulation cost while satisfying operational constraints. While
existing voltage regulation methods are mostly focused on onetime-
step control, this paper explores a day-horizon systemwide
voltage regulation problem. In other words, the size of
action and state spaces are extremely high-dimensional and need
to be delicately handled. Furthermore, in order to overcome
the difficulty of modeling uncertainties and develop a realtime
solution, a learn-to-schedule feedback control framework is
proposed by adapting the problem to a model-free DRL setting.
The proposed algorithm is tested on a customized 6-bus system
and a modified IEEE 34-bus system. Simulation results validate
the effectiveness and near-optimality of voltage regulation by ESS
in comparison with a deterministic quadratic program solution.
Published: September 25, 2021
Citation
Wang S., L. Du, X. Fan, and Q. Huang. 2021.Deep Reinforcement Scheduling of Energy Storage Systems for Real-time Voltage Regulation in Unbalanced LV Networks with High PV Penetration.IEEE Transactions on Sustainable Energy 12, no. 4:2342-2352.PNNL-SA-163829.doi:10.1109/TSTE.2021.3092961