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Mathematics 2022, 10, 3167 2 of 27 model predictive controller using a dynamic exergy-based objective function to maximize exergetic efficiency of a basic refrigeration cycle [3]. Concerning HVAC systems in build- ings, Espejel-Blanco et al. have recently presented an HVAC temperature control system based on the Predicted Mean Vote (PMV) index calculation, which combines the values of humidity and temperature to define comfort zones and achieves energy savings ranging from 33% to 44% against the built-in control of the HVAC equipment [4]. Kou et al. develop both model-based and data-driven HVAC control strategies to determine the optimal con- trol actions for HVAC systems, highlighting a significant trade-off between electricity costs and computational speed between both strategies [5]. Furthermore, Macieira et al. have also presented an energy management model for HVAC control based on reinforcement learning, to manage the HVAC units in smart buildings according to the prediction of users, current environmental context, and current energy prices [6]. In recent years, a novel line of research regarding cold-energy management has been introduced which involves the addition of thermal energy storage (TES) systems to the standard refrigeration cycle. Accordingly, cooling production and demand can be decoupled, since excess cold energy may be stored in the TES system, being released when required, as common storage strategies applied to solar plants [7]. This setup makes it possible to use lower capacity systems, since they need not be oversized to face peak- demand periods [8], and thus the investment cost may be reduced. Moreover, the cycle may be operated in more favourable terms, which improves efficiency and reduces energy usage. Furthermore, it is possible to schedule the cooling production according to the variable cost of electricity given by the energy market. Thus, an optimal strategy (peak-shifting) would be to produce as much cold energy as possible during the low-priced periods, while storing the excess in the TES system, and to produce as little as possible during the high-priced periods, releasing the cold energy previously stored [9]. Concerning the design of the storage system, most commercial and developing systems rely on phase change materials (PCM) [10]. The main reason is that their thermodynamic properties are more suitable for energy storage than those of sensible-heat materials [11]. PCM are able to store larger amount of energy per unit volume and their temperature remains approximately constant in latent state, which improves heat transfer efficiency. Concerning domestic refrigerators, Bista et al. published a thorough review [12], regarding PCM selection and placement inside the refrigerator, where the use of PCM is shown to increase the Coefficient of Performance (COP) with a relatively significant margin. Among the PCM-based systems, there are several factors defining the storage configuration, such as the PCM encapsulation and the heat exchanger layout where the PCM and the heat transfer fluid (HTF) meet, the packed bed technology being one of the most used configurations [13]. Regarding the latter, Berdja et al. proposed a novel approach to optimize the dimensions of the packed bed heat exchanger, according to the technical features and operating conditions of a standard refrigerator [14]. Many works in the literature have addressed cold-energy management of refriger- ation systems with energy storage. For instance, three works by Wang et al. cover the design [15], modelling [16], and control [17] of a large-scale HVAC plant, where a ring of PCM-based TES tanks acts as a cold-energy reservoir. Control policies are proposed to activate/deactivate the TES tanks, seeking high performance. Other works implement management strategies based on exergy analysis, where a combined use of the different PCM modules is applied [18]. Furthermore, model predictive control (MPC) also represents a successful strategy. Indeed, it has been applied for HVAC without energy storage [19,20], but the potential of predictive strategies for TES-backed-up systems is much higher.For instance, Shafiei et al. [21] propose a MPC framework to minimize the divergence in electric energy usage with respect to a given reference, in a large-scale refrigeration plant. For that purpose, energy collected by and retrieved from the TES is estimated, and an optimization problem is formulated by introducing the reference of the evaporation tem- perature as a virtual control variable. Shafiei et al. [22] also apply predictive control to TES-backed-up refrigerated freight transport, where the TES tank is embedded in parallelPDF Image | Refrigeration Systems with Thermal Energy Storage
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