With the increasing share of renewable energy sources, the electrical power system is becoming more volatile, resulting in significant price fluctuations in the electricity market. Such conditions encourage the development of demand-side flexibility methods, particularly for large residential consumers such as heat pumps. This master's thesis presents a comprehensive approach to optimize the operation of a heat pump-based heating system in response to day-ahead electricity market prices. An innovative method was developed to identify system parameters solely from the historical operational data, eliminating the need for additional on-site measurements. Unlike black-box models, the employed model is based on a physical representation of the system, offering interpretability and insights into system behavior. Using this model, the operating cost of the heat pump was optimized while maintaining living comfort. By leveraging the building’s thermal capacity, electricity consumption was shifted to periods with lower prices, effectively turning the heating system into a flexible load within a smart grid. Validation with data from an entire heating season showed energy cost savings of between 15 and 25 %. The proposed solution is cost-effective, user-friendly, and well-suited for integration into existing control systems.
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