Since the electrical grid is a safety-critical system, the consequence of outages can be great, including risk of injury or death (such as during heat waves) and economic loss (as many businesses depend on electricity to operate). However, the need to rapidly de-carbonize the energy system is leading to an increased penetration of intermittent renewables, and insufficient storage to compensate for them. Consequently, the challenge of operating the grid to supply power to end users under all circumstances has grown more difficult. In this research project this challenge is addressed in part through data-driven methods, such as Deep Reinforcement Learning (DRL), which can make use of measurements from an increasingly digitized grid to formulate effective control strategies that improve grid reliability and resilience. Specifically, we wish to continue work on both power system integrity protection schemes and microgrid controllers using DRL. These would act as decision support tools for control room operators.