The identification, quantification, and control of demand flexibility is the major challenge for future grid operations and requires innovative methods and new control strategies. Optimal control strategies such as economic model predictive control have gained attention in building energy management systems. The present experimental case study demonstrates the application of an economic model predictive controller under realtime pricing, including day-ahead prices and imbalance prices. For real-time prices in balancing and spot markets, we introduce a method that presents a flexibility service to provide demand flexibility for a notification time of 1h < t < tday end in advance. The flexibility service can be used as an ancillary service for innovative flexibility markets. The flexibility service includes a dynamic modification of the day-ahead prices to enable the adaption of energy consumption to errors in forecasting of renewable energy generation. The developed method was tested under real-life conditions, which also included the stochastic behaviour of occupants and the dynamic behaviour of the building and heating system. During the test periods, the controller managed the total operational costs of the heat pump’s electricity consumption and achieved a prediction performance of Root Mean Square Error between 0.17 and 0.22 kWh. To show the provision of demand flexibility, key performance indicators were quantified according to the categories 1) energy and power, 2) energy efficiency, and 3) energy costs. We introduce this categorization to present the benefits of using flexibility indicators along with conventional performance indicators in real-life applications.
- Energy flexibility
- Economic model predictive control
- Thermal energy storage
- Heat pump
- Flexibility service