Risk-aware Scheduling and Dispatch of Flexibility Events in Buildings.

Residential and commercial buildings, equipped with systems such as heat pumps, hot water tanks, or stationary energy storage, have a large potential to offer their consumption flexibility as grid services. In this work, we leverage this flexibility to react to consumption requests related to maximizing self-consumption and reducing peak loads. We present a general characterization of consumption flexibility in the form of flexibility envelopes and discuss a data-driven battery model formulation for modeling individual buildings. These models are used to predict the available consumption flexibility while incorporating a description of uncertainty and being risk-aware with a pre-defined risk level. A Mixed-integer Linear Program (MILP) is formulated to schedule the activation of the buildings in order to best respond to an external consumption request. An aggregated consumption request is dispatched to the active individual buildings by an algorithm, based on the previously determined schedule. The effectiveness of the approach is demonstrated by coordinating up to 500 simulated buildings using the Energym Python library and observing about 1.5 times peak power reduction in comparison with a baseline approach while maintaining comfort more robustly. We demonstrate the scalability of the approach, with solving times being approximately linear in the number of considered assets in the scheduling problem.