Economic Model Predictive Control for Hot Water Based Heating Systems in Smart Buildings

Abstract

This paper presents a study to optimize the heating energy costs in a residential building with varying electricity price signals based on an Economic Model Predictive Controller (EMPC). The investigated heating system consists of an air source heat pump (ASHP) incorporated with a hot water tank as active Thermal Energy Storage (TES), where two optimization problems are integrated together to optimize both the ASHP electricity consumption and the building heating consumption utilizing a heat dynamic model of the building. The results show that the proposed EMPC can save the energy cost by load shifting compared with some reference cases.

Sustainability matchmaking: Linking renewable sources to electric water heating through machine learning

“…Knudsen and Petersen [79] used the Economic Model Predictive Controller (EMPC) to control the water heater system that is used to increase the supply temperature in ultra-low temperature district heating since it can easily manage time-varying tariffs. Awadelrahman et al [80] also proposed an EMPC to maximize heating energy costs with varying electricity price signals in a residential building. It was shown that while the temperatures in the systems were kept within limits, EMPC shifted the electricity demand based on the price.…”

Enhancing Wind Power Integration through Optimal Use of Flexibility in Multi-Carrier Energy Systems from the Danish Perspective

“…P el is the HP electric power consumption which is the manipulated input within the operational limits P min and P max . g (P el ) is a nonlinear function which represents the temperature of the stored water for the each layer in the TES, and it is calculated using the stratified tank model [18]. T s,min is the minimum stored temperature, in this study the boundary is optimized to meet Figure 3.…”

“…Flowchart of the MPC-based control strategy. World Journal of Engineering and Technology the demand at a every certain time; this minimum value and it is calculated by utilizing the hot water radiator model in reference [18] to calculate the optimal T s,min and it should be sufficient to meet the maximum required load in the house. T s,max is the maximum boundary and set to 100 º C temperature to keep the water from being evaporated.…”

A mixed-methods case study on resident thermal comfort and attitude towards peak shifting of space heating

“…Simulation-based research on enabling temporal DSM of residential heating systems, by using Economic Model Predictive Control (EMPC) to exploit the building fabric for short-term energy storage (see e.g. [3][4][5][6][7][8] to mention a few), has indicated that realizing the potential is an asset to operational challenges in e.g. urban district heating systems [9][10][11][12].…”