Abstract
The trend towards renewable and decentralized generation in power systems results in an increased volatility of energy supply. Energy storages offer the possibility to locally balance supply and demand. Existing infrastructure of thermal systems for heating and cooling consists of various types of energy storages, which have the potential to provide flexibility on the demand side. Model predictive control (MPC) is used in smart grids to control supply and demand and is applied in this paper to control a residential heating system. An appropriate MPC design is presented based on a heat pump with a floor heating system and the potential of demand-side management in the residential sector demonstrated. The controller takes into account climatic conditions, internal gains of occupants and, if desired, electricity prices. The design of the controller incorporates an objective function which regards both discomfort and energy costs. Effectiveness of the MPC is demonstrated in a simulation of seven days, resulting in either more comfort or lower costs due to the exploitation of price differences. Finally, the trade-off between discomfort and cost is investigated.
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