## 1 Introduction
[6], instead, performed and validated the dynamic model of a parabolic trough collector in Thermocycle Modelica against the experimental results obtained at the Plataforma Solar de Almeria, finding good agreement both under steady-state and transient operating conditions. [PERSON] et al. [7] mathematically investigated in TRNSYS latent heat TES for application to a solar power ORC system over a one-week period, obtaining a weekly average overall solar-to-electricity efficiency of 3.9%. Furthermore, the authors found that appropriate control logics are required to improve the performance of the system over a more extended period. Similarly, some of the authors of the present paper [8] numerically analyzed the performance of a novel solar ORC system based on an LFR, combined with an advanced latent heat TES unit, finding that the integrated plant had significant heat losses, which could be sensibly reduced by improving the control strategy of the plant. In subsequent work [9], the authors developed a hardware-in-the-loop simulator of the system to support the optimization of the control algorithms and to define the best control strategy to ensure proper operation of the ORC unit. Nevertheless, the work pointed out that further analyses were required to optimize the operation of the integrated system and maximize the energy production or the conversion efficiency. Indeed, the operation of medium-temperature solar energy systems is sensitive to varying ambient conditions and users' needs. In addition, the thermal inertia of the different components has an important impact on the dynamics of these plants. Therefore, the development of an effective control logic is of paramount importance to maximize the conversion efficiencies of these systems. Among all the possible control approaches, a fuzzy logic controller is considered suitable for similar integrated systems. Fuzzy logic, firstly introduced by [PERSON] in [10], has been, indeed, applied with success in several engineering domains and has proved to be useful in renewable energy systems in general. Fuzzy logic is used for modeling purposes, such as in [11], where a household electric quantitative and qualitative representation was obtained in order to evaluate, ex ante, the economic benefit of energy management (EM). It is also used for design purposes, as in [12], to compare different heat transfer fluids (HTF) based on their benefits and costs in CSP applications. Most frequently, however, fuzzy logic is adopted for control design, as proposed by [13] for reducing the daily peak demands in limited-capacity, battery-based energy storage systems or by [14] to track the maximum power point of a PV array in a stand-alone photovoltaic system. Fuzzy logic controllers are preferred over model-based approaches whenever the mathematical model of the plant is not available or is highly uncertain [15]. This is especially true in complex solar power plants, where operative modes are identified according to the internal configuration of the plants and switching between modes is based on priority rules [16]. In this regard, yir authors of [17] proposed a supervisory controller of an isolated hybrid AC/DC microgrid, where a state machine was used to govern the transitions between the different operating modes, while fuzzy logic was adopted to maintaining the desired state of charge (SOC) of the battery banks. In [18], a fuzzy logic controller was developed with the goal of minimizing the power profile fluctuations in a residential grid-connected microgrid, while keeping the SOC of the battery within secure limits. However, such approaches do not address the problem of discriminating between different load profiles in order to adjust the control logic accordingly. Furthermore, to the best of the authors' knowledge, there is no paper in the literature that considers the use of fuzzy logic to control small-scale concentrated solar ORC plants for the built environment.