A MIP-informed machine learning method for the real time management of a multi-storage microgrid with renewable energy sources

Complex microgrids with the inclusion of renewable energy sources and multiple energy storage systems based on batteries and hydrogen storage require fast, reliable, and adaptable management approaches to achieve profitability. Uncertainty in power generation and demand levels renders the application of typical optimization methodologies impossible in real-time management scenarios, due to slow response times and the risk of power imbalances. In this paper, we introduce a new machine learning based approach to the real-time management of complex microgrids under uncertainty. A decision tree classifier is trained on solved-to-optimality mixed-integer linear programming problem instances. Then, a decision algorithm is proposed to dispatch the microgrid operation. The proposed machine learning approach shows promising performance on a real dataset simulation compared to the benchmark rule-based approach, resulting in a significant 22% yearly operational cost reduction tested on the real data.