  With the increased amount of volatile renewable energy sources connected to
the electricity grid, and the phase-out of fossil fuel based power plants,
there is an increased need for frequency regulation. On the demand side,
frequency regulation services can be offered by buildings or districts that are
equipped with electric heating or cooling systems, by exploiting their thermal
inertia. Existing approaches for tapping into this potential typically rely on
dynamic building models, which in practice can be challenging to obtain and
maintain. As a result, practical implementations of such systems are scarce.
Moreover, actively controlling buildings requires extensive control
infrastructure and may cause privacy concerns in district energy systems.
Motivated by this, we exploit the thermal inertia of buffer storage for
reserves, reducing the building models to demand forecasts here. By combining a
control scheme based on Robust Model Predictive Control, with affine policies,
and heating demand forecasting based on Artificial Neural Networks with online
correction methods, we offer frequency regulation reserves and maintain user
comfort with a system comprising a heat pump and buffer storage. While the
robust approach ensures occupant comfort, the use of affine policies reduces
the effect of disturbance uncertainty on the system state. In a
first-of-its-kind experiment with a real district-like building energy system,
we demonstrate that the scheme is able to offer reserves in a variety of
conditions and track a regulation signal while meeting the heating demand of
the connected buildings. 13.4% of the consumed electricity is flexible. In
additional numerical studies, we demonstrate that using affine policies
significantly decreases the cost function and increases the amount of offered
reserves and we investigate the suboptimality in comparison to an omniscient
control system.

Systems and Control;Systems and Control