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.
We use a three-level control scheme in this work. In Level 1, a robust optimization scheme with affine policies is used to optimally determine the offered electrical reserves, which are offered to the transmission system operator (TSO). The optimization contains a model of the heat pump and the buffer storage. Moreover, a heating demand forecast, which is done with online-corrected Artificial Neural Networks, is used. In Level 2, the base-load of the heat pump can be adjusted with the help of a Model Predictive Control Scheme. Level 3 is a PI controller, which tracks the regulation signal, which is broadcasted from the TSO.
This plot illustrates an excerp from the results. It can be seen that the storage temperature stays between two bounds at all times, which means that the heating demand of the building can always be met. Moreover, the heat pump regulates up and down, depending on the regulation signal and the offered reserves.
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