Reactive Power Control of a Converter in a Hardware-Based Environment Using Deep Reinforcement Learning

Abstract

Due to the increasing penetration of the power grid with renewable, distributed energy resources, new strategies for voltage stabilization in low voltage distribution grids must be developed. One approach to autonomous voltage control is to apply reinforcement learning (RL) for reactive power injection by converters. In this work, to implement a secure test environment including real hardware influences for such intelligent algorithms, a power hardware-in-the-loop (PHIL) approach is used to combine a virtually simulated grid with real hardware devices to emulate as realistic grid states as possible. The PHIL environment is validated through the identification of system limits and analysis of deviations to a software model of the test grid. Finally, an adaptive volt–var control algorithm using RL is implemented to control reactive power injection of a real converter within the test environment. Despite facing more difficult conditions in the hardware than in the software environment, the algorithm is successfully integrated to control the voltage at a grid connection point in a low voltage grid. Thus, the proposed study underlines the potential to use RL in the voltage stabilization of future power grids.