## 1 Introduction
The model was used to simulate the operation of the research farm of Punjab Agricultural University, Ludhiana. The model solved using the Gauss-Seidel iteration method confirms good agreement with the measured data related to the winter operation for a tomato crop. A dynamic greenhouse environment simulator was developed in ref. [21] to predict, with different configurations, greenhouse dynamic behavior. The model was implemented in a web-based interactive application that allowed for the selection of the greenhouse design, operational strategies, and weather conditions (four seasons at four geographical locations). To forecast the hourly demand for heating the conventional greenhouse a quasi-steady state time-dependent thermal model was developed [22]. The model considers the lumped estimation of the heat transfer parameters of the greenhouse. The model takes into account the indoor environmental control parameters of the greenhouse, the thermal and physical properties of crops, construction materials, and several hourly weather parameters, such as cloud cover, relative humidity, temperature, and wind speed. The heat gain from environmental control systems and the heat loss for plant evapotranspiration are further phenomena considered in this model. The authors highlight the importance of the environmental control systems to reduce the total heating requirements over the year by about 13-56%. A Chinese-style solar greenhouse for Canadian prairies modeled by a detailed TRNSYS model is presented in ref. [23]. The model, that is used to predict the transient heating requirement of the greenhouse, was also validated by a new heating simulation model. The same model developed in ref. [23] was improved in ref. [24] and validated adopting the data collected from a solar greenhouse in Manitoba, Canada. The yearly simulation reveals that in the coldest month (January), the daily average heating could be twice as high (6.3 MJ/m\({}^{2}\)-day) as the value obtained in a mild month such as March (3.4 MJ/m\({}^{2}\)-day). Comparing this solar greenhouse with a traditional, local one, the heating cost is approximately 55% lower. The open literature regarding dynamic modelling and multi-source integration is very wide [25; 26]. In ref. [27], solar, geothermal, and biomass systems were integrated into a novel renewable trigeneration plant for the production of power, heat, and cool, respectively. In particular, a 193 m\({}^{2}\) PV, a 159 kWh lithium-ion battery, a 30 kWe organic Rankine cycle, a 350 kWh biomass auxiliary heater, a geothermal well at 96 \({}^{\circ}\)C, and a 80 kWsingle-stage H\({}_{2}\)O/LiBr absorption chiller were the included technologies in this plant. The model of the plant was applied to the case study, a residential building in Campi Flegrei (Naples, South Italy). A primary energy saving of 139% was achieved due to the excess energy delivered to the grid. The achieved payback period was about 19 years, mainly due to the high capital cost of the used technologies. A novel ultra-highly efficient solar power system combining concentrating photovoltaic/thermal (CPVT) solar collectors coupled with an organic Rankine cycle (ORC) was investigated in ref. [28]. The CPVT collectors produced the power and heat used to supply the ORC unit. The solar field consists of a dish concentrator and a triple-junction PV layer, and it is equipped with a double axis tracking system. Both CPVT and ORC models are integrated in a more complex dynamic simulation model, developed in a TRNSYS environment. However, the results showed that the novel (ORC+CPVT) system produces only 6% more electrical energy than the system with only CPVT collectors. In ref. [29], two solar polygeneration plants designed to produce space heating and cooling energy, power, and fresh water are presented. These plants are designed for two small Mediterranean islands, the Favignana and Salina islands (South Italy). In the first layout, the CPVT collectors produced electric and thermal energy.