With the increased amount of volatile renewable energy sources connected to
the electricity grid, there is an increased need for frequency regulation. On
the demand side, frequency regulation services can be offered by buildings that
are equipped with electric heating or cooling systems, by exploiting the
thermal inertia of the building. Existing approaches for tapping into this
potential typically rely on a first-principles building model, which in
practice can be expensive to obtain and maintain. Here, we use the thermal
inertia of a buffer storage instead, reducing the model of the building to a
demand forecast. By combining a control scheme based on robust Model Predictive
Control, with heating demand forecasting based on Artificial Neural Networks
and online correction methods, we offer frequency regulation reserves and
maintain user comfort with a system comprising a heat pump and a storage tank.
We improve the exploitation of the small thermal capacity of buffer storage by
using affine policies on uncertain variables. These are chosen optimally in
advance, and modify the planned control sequence as the values of uncertain
variables are discovered. In a three day experiment with a real multi-use
building we show that the scheme is able to offer reserves and track a
regulation signal while meeting the heating demand of the building. 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.Comment: 13 pages, 12 figures, 1 table, submitted to IEEE Transactions on
Control Systems Technolog
