Challenges of implementing economic model predictive control strategy for buildings interacting with smart energy systems

Abstract

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

“…the building mass and TES, the EMPC algorithm which is shown in Figure 3 solves two sequential optimization problems for every sampling time within the prediction horizon. The PFH3 algorithm determines the radiators' output to supply the building with the required heat; it is the same as developed in [4]. The second algorithm is to optimize the ASHP electric power consumption to set the TES temperature to the optimal temperature within the operating boundaries.…”

“…The TES model is developed based on the concept illustrated in [9]. The heat balance can be given by Equation (4). Where m is the mass of the hot water; C p is the water thermal capacity, and T w,i is the water temperature in the i th layer.…”

“…The building model was developed in [4], what-so-called T i T e model as a state space model that considers the building envelope temperature as well as the indoor air temperature.…”

“…An MPC-based scheduling algorithms developed in [3] for the building energy management system to exploit the thermal mass of the building as well as the non-thermal appliances flexibility to incur energy savings. A pilot study in [4] showed that energy savings and load shifting can be achieved by applying EMPC for electric heaters consumption utilizing a residential building mass as heat storage.…”

“…The building model used in this paper is discussed in [4], and the stratification phenomenon is considered in the TES model. Based on the dynamic power price signals and the weather forecast, EMPC optimizes both the ASHP electricity consumption and the building heating consumption; the system's energy consumption is compared with some reference cases.…”