State estimation of resistive domestic hot water heaters in arbitrary operation modes for demand side management

Abstract

Utilizing domestic hot water heaters (DHWH) for demand side management (DSM) requires physical models of DHWH dynamic behavior. An ideal dynamic DHWH model is simple as well as accurate. However, system identification and state estimation, which are necessary for real-life implementation, are usually not considered. This work reviews standard thermal models, such as the single-node model, the multi-node model, a two-mass composite model and a two-node hybrid model with thermocline tracking. A prediction error method (PEM), applicable to all four models, is developed. 16 weeks of user and switching data from a DSM field test are used to operate a laboratory DHWH realistically. The temperature distribution within the heater, as well as electrical power input, water volume flow, inlet and outlet water temperature, temperature of the surroundings, and thermal well temperature are recorded. System identification and state estimation routines are developed based on the data available for a retrofitted system. Using cross-validation on the available user and operation scenarios from the lab, the approaches are compared with respect to average and outlet temperature estimation errors, model robustness and computational costs. The results show that a single-node model, which incorporates information on stratification during heating intervals in assuming a linear temperature distribution in the lower part of the tank, performs nearly as well in predicting the average temperature (mean average percentage error of 6%) as the 50 times more computationally expensive multi-layer model (mean average percentage error of 4%) and better than all other single-layer model based approaches considered.