Kernel-based online learning for real-time voltage control in distribution networks

Abstract

This paper presents a new data-driven voltage control approach for distribution networks based on kernel methods. Voltage control becomes more and more challenging due to the increased penetration of Distributed Generation (DG), bidirectional power flow and faster voltage dynamics. State-of-art strategies for voltage control rely on physics model-based Optimal Power Flow (OPF) solutions, which can be implemented in a centralized or distributed manner. Nevertheless, such strategies require a detailed model of the network, and often lack scalability due to the large number of nodes and the limited communication infrastructure in distribution networks. In order to achieve real-time voltage control in distribution networks of meshed and radial topology, this paper presents a data-driven approach, which relies on local or regional measurements and does not require accurate models of the grid or an advanced communication infrastructure. Specifically, the proposed data-driven approach uses functional stochastic gradient descent in Reproducing Kernel Hilbert Spaces (RKHSs), to learn the control strategies for Distributed Generation (DG) units in real-time that lead to near-optimal operation costs, while maintaining adequate voltage profiles in the network and alleviating congestions for time-varying load and generation conditions.