Operation optimization in large-scale heat pump systems: A scheduling framework integrating digital twin modelling, demand forecasting, and MILP

The integration of large-scale heat pumps and thermal energy storage can facilitate sector coupling, potentially lowering heating and cooling costs in industries and buildings. This cost reduction can be extended by optimizing the utilization of the available thermal energy storage capacity in accordance to fluctuating electricity prices. Although the literature offers methods for optimizing the operation of these integrated systems, they often overlook the impact of heat pump performance degradation over time, such as from fouling. This oversight can lead to suboptimal system performance and inaccurate operational cost estimates. The present study addresses this gap by introducing a novel operational scheduling framework that aimed to reduce the operational costs of a commercial large-scale heat pump system. The system comprised an open cooling tower, a thermal storage tank and two heat pumps affected by fouling. The framework incorporated a mixed-integer linear programming (MILP) model, thermal demand forecasting, and heat pump performance maps that account for varying fouling levels. These maps were obtained from online calibrated simulation models used as digital twins of the heat pumps. The results demonstrated that the proposed framework enhanced the thermal energy storage utilization in response to variable electricity prices and adjusted the heat pump operation based on the influence of fouling. This resulted in a reduction of operational costs of up to 5% compared to the conventional operation of the system. These savings were observed to vary depending on the forecasting accuracy and the prevailing fouling levels. Overall, this study demonstrates the potential of using the proposed framework for cost reduction in largescale heat pump systems.