Reinforcement Learning Approaches to Power Management in Grid-tied Microgrids: A Review

Abstract

Modern power systems are characterized by bidirectional power flows, smart metering schemes capable of net metering applications and several renewable energy-based distributed generators whose outputs are stochastic. Moreover, customers are producing their own energy using renewables and are involved in contractual energy cooperation with the main grid. This introduces more complexities in power management as the number of random variables to be considered in scheduling increases. Therefore, there is a need to develop more robust power management algorithms that are adaptive to the stochasticity in the new power systems. Recently, the employment of reinforcement learning (RL) techniques in power management and scheduling has received significant interest. This paper reviews recent RL methods used in solving grid-tied microgrid power control problems and highlights the potential benefits and disadvantages of RL algorithms in solving these problems. The various challenges associated with the control of power flow in microgrids being linked to the utility grids are first described using electric vehicle charging station that is powered by PV and the utility grid as an example. The application of various RL techniques in addressing those challenges is then critically reviewed.