Model predictive approaches for cost-efficient building climate control with seasonal energy storage

Abstract

Due to the increasing share of fluctuating renewable energy, a seasonal energy storage with a high capacity is needed, which stores the summer surplus electricity to satisfy the winter heating demand.It is assumed that the control integration of a novel thermochemical seasonal energy storage concept into a building energy system can meet this requirement and save operating costs. For this purpose, a model-predictive control concept with a prediction horizon beyond that of public weather forecasts is crucial.A state-based model is developed consisting of a building, a water buffer and a heat supply. To assess the effects of seasonal storage, different heat supply configurations are considered. On this basis, two Model Predictive Control (MPC) concepts are designed to efficiently operate the system over one year. Since public weather forecasts are reliable in the time range of several days, test reference year data are used to approximate the weather forecast beyond the public forecast period. Additionally, the control hierarchy is comprised of a superordinate optimal generation scheduling (OGS) and a subordinate MPC. The concepts follow the scheduled long-term lime storage trajectory and realise possible short-term yields based on the current public forecast. The trajectory tracking is formulated either in the objective function or the constraints.The integration of the novel lime storage module into the heat supply of a building allows a reduction of operating costs of 18% in the realistic scenario and up to 80% in case of highly fluctuating electricity prices. This reduction potential is fully exploited by the developed control approaches, but it is very sensitive to the change of the controller parameters, the fluctuation of the electricity price and the weather data. Moreover, by applying the objective-based reference tracking approach, the higher-level scheduling hierarchy could be avoided.Through the results of this work, it is confirmed that the integration of a seasonal energy storage system can greatly reduce annual operating costs and that it is not crucial to apply a prediction horizon beyond that of public forecasts.