Data-Driven Fast Voltage Control in Non-DPMU Distribution Networks With Microgrids

Abstract

Traditional voltage control methods for distribution networks assume perfect knowledge of the power system model. Nevertheless, the extensive scale of future distribution networks makes it unrealistic to acquire the overall operation state monitoring. Moreover, with the deregulation of distribution networks, partial controllable resources belong to independent systems, such as microgrids, causing distribution system operators unable to force them to provide voltage support directly. To cope with the previously mentioned problems, a data-driven fast voltage control method for distribution networks with MGs is proposed in this article. First, voltage sensitivity matrices are estimated indirectly by identifying line parameters in a regression approach, without using measurement data of distribution phasor measurement units (DPMUs) in distribution networks. Then, an incomplete information game model is proposed to motivate MGs to provide ancillary services of voltage control. To guarantee privacy, only a little key information is shared among MGs and distribution system operators. Moreover, MGs make voltage control strategies autonomously based on the data-driven deep reinforcement learning algorithms, while maximizing their own profits. Finally, we test the method on the modified IEEE 33-node networks and IEEE 123-node networks. The results demonstrate that the proposed method can provide an accurate voltage estimation in electricity markets with non-DPMU measurement data and increase energy and asset utilization.