Robust opportunistic optimal energy management of a mixed microgrid under asymmetrical uncertainties

Energy management within microgrids under the presence of large number of renewables such as photovoltaics is complicated due to uncertainties involved. Randomness in energy production and consumption make both the prediction and optimality of exchanges challenging. In this paper, we evaluate the impact of uncertainties on optimality of different robust energy exchange strategies. To address the problem, we present AIROBE, a data-driven system that uses machine-learning-based predictions of energy supply and demand as input to calculate robust energy exchange schedules using a multiband robust optimization approach to protect from deviations. AIROBE allows the decision maker to tradeoff robustness with stability of the system and energy costs. Our evaluation shows, how AIROBE can deal effectively with asymmetric deviations and how better prediction methods can reduce both the operational cost while at the same time may lead to increased operational stability of the system.

Automated summary

This paper investigates the complexity of energy management within microgrids with a large number of renewable energy sources, such as photovoltaics, and proposes a data-driven system called AIROBE that utilizes machine learning and robust optimization to address uncertainties. The impact of uncertainties on the optimality of different robust energy exchange strategies is evaluated.