Weather-Driven Predictive Control of a Battery Storage for Improved Microgrid Resilience

This paper aims to introduce a predictive weather-based control policy for the microgrid energy management to improve the resilience of the microgrid. This policy relies on the application of machine learning models for the prediction of microgrid load demand and solar production and supply interruption in the upstream distribution network. The predictions serve as an input to multiobjective chance constraint optimization that balances the microgrid resilience and economic objectives based on the probability of a supply interruption. The interruption predictions are made with a decision-tree-based model that can predict an upcoming interruption in the distribution network with 78% of the maximum accuracy. The case study microgrid consisting of several customers, solar photovoltaic generation, and battery storage is applied to cluster areas located in Finland. Overall, the developed control policy shows an improvement in the daily resilience of the microgrid in regard to an interruption in the main grid when compared with economic dispatch only.