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Residential and commercial buildings, equipped with systems such as heatpumps, hot water tanks, or stationary energy storage, have a large potential tooffer their consumption flexibility as grid services. In this work, we leveragethis flexibility to react to consumption requests related to maximizingself-consumption and reducing peak loads. We present a general characterizationof consumption flexibility in the form of flexibility envelopes and discuss adata-driven battery model formulation for modeling individual buildings. Thesemodels are used to predict the available consumption flexibility whileincorporating a description of uncertainty and being risk-aware with apre-defined risk level. A Mixed-integer Linear Program (MILP) is formulated toschedule the activation of the buildings in order to best respond to anexternal consumption request. An aggregated consumption request is dispatchedto the active individual buildings by an algorithm, based on the previouslydetermined schedule. The effectiveness of the approach is demonstrated bycoordinating up to 500 simulated buildings using the Energym Python library andobserving about 1.5 times peak power reduction in comparison with a baselineapproach while maintaining comfort more robustly. We demonstrate thescalability of the approach, with solving times being approximately linear inthe number of considered assets in the scheduling problem.

Risk-aware Scheduling and Dispatch of Flexibility Events in Buildings