Model Predictive Control (MPC) is an optimal control algorithm based on a mathematical model of the plant (i.e. the building) and mathematical programming. The model is used to predict the system’s future state depending on the current state, future control inputs and measurable disturbances. According to these predictions, optimal control actions for a limited time horizon are calculated while only the first input is applied to the plant, see Figure below.
The increase in the renewable energy sources connected to the electricity grid has resulted in an increased need for frequency regulation. On the demand side, frequency regulation services can be provided by electrified heating/cooling systems exploiting the energy stored in thermal mass of buildings. To provide such services a first principles model of the building is needed, which is often difficult to obtain in practice. This issue can be overcome by using a buffer storage between the heating/cooling source and the building. Here, we present a solution based on robust optimization and heating demand forecasts with Artificial Neural Networks to offer frequency regulation reserves with such a system comprising a heat pump, a thermal storage in the form of a warm water buffer tank, and heating demand from a building that needs to be served. We mitigate the problem of limited thermal storage by introducing affine policies on uncertain variables in the optimization problem. In experiments at the NEST building, we are able to offer electrical reserves under real operation of the system.