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

Due to the increasing penetration of the power grid with renewable, distributed energy re-sources, new strategies for voltage stabilization in low voltage distribution grids must be devel-oped. 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 in-cluding 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 em-ulate as realistic grid states as possible. The PHIL environment is validated through the identifica-tion 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