Distributed Energy Resource Portfolio Sizing For Extreme Event Mitigation Using Deep Reinforcement Learning

Whether due to climate change, cyber attacks, or physical attacks, outages in the electrical grid can cause economic and physical harm. In this work we investigate how Distributed Energy Resources (DERs) could be leveraged to enhance resilience by using demand response and energy storage to mitigate line outage events. In order to provide rapid decision support, in this work continuous off-policy Deep Reinforcement Learning (DRL) is applied on a portfolio of DERs after an extreme event has impacted.With the help of distributed training, hyperparameter optimisation, and a safety shield the resulting agents were able to reduce Energy Not Supplied (ENS) by up to 52.9%, on average, in unseen scenarios. By varying the composition of the DER portfolio, a weak linear relationship between portfolio size and scenario performance was found, alongside a strong impact of where demand response was applied. Nevertheless, based on this sensitivity analysis, a DSO could temporarily contract DERs based on the expected reduction in the cost of ENS, when compared to the cost of acquiring the DER.