Technical, Economic, and Environmental Assessment of Hybrid Solar Photovoltaic–Thermal Systems in Hospitals: A Comprehensive Climate Change Mitigation Strategy

Abstract

The high dependence on fossil fuels for energy supply in hospitals compromises their operational sustainability, increases costs, and contributes significantly to polluting emissions. This study evaluates the technical, economic, and environmental feasibility of integrating photovoltaic and solar thermal systems in a hospital located in a tropical Caribbean environment, characterized by continuous operation and high energy demand. The methodology combines advanced simulation using PVsyst for the photovoltaic subsystem and the f-chart method for the solar thermal system, using real data on electricity and domestic hot water demand. The proposed system achieves an installed photovoltaic power of close to 390 kWp, with an annual production of around 0.7 GWh and an average performance ratio of 0.80, demonstrating high technical performance. The solar thermal subsystem covers approximately two-thirds of the annual domestic hot water demand, supported by thermal storage suitable for hospital operation. From an economic standpoint, the total estimated investment is recovered in less than 10 years, with a positive net present value, confirming the system’s profitability over its useful life. In environmental terms, hybrid integration avoids more than 400 t of CO₂ per year, contributing significantly to the decarbonization of the health sector and the strengthening of energy security. The results obtained demonstrate that photovoltaic–thermal integration in tropical hospitals is technically and economically viable and constitutes a replicable solution for regions with high solar radiation and energy vulnerability. This research provides a comprehensive and reproducible methodological framework that can support sustainable energy planning and the design of public policies aimed at low-emission healthcare infrastructure.

1. Introduction

The transition toward sustainable and low-carbon energy systems has become a global priority in recent decades, and Latin America is no exception to this trend. The integration of renewable energies into existing electrical infrastructures represents a fundamental pillar of this transformation, offering promising solutions to address environmental, economic, and energy security challenges [1,2]. According to IRENA Renewable Energy Statistics 2025, installed renewable energy capacity in Latin America and the Caribbean (including South America, Central America, and the Caribbean) reached 332.1 GW in 2024, compared to 249.1 GW in 2020, representing an increase of 83.0 GW, or approximately $33.3\%$ in the period 2020–2024 [3].

In recent years, several authors have emphasized that this transition not only involves the technological replacement of fossil-based sources but also the creation of resilient energy models capable of responding to climate and health crises. [4,5]. The global energy sustainability crisis and the pressures for decarbonization have placed hospitals as critical infrastructures that require highly reliable energy supplies but are simultaneously large consumers of energy and generators of greenhouse gas (GHG) emissions, with consumption levels up to 60% higher than other service buildings [6,7]. The International Energy Agency reported that in 2022, nearly 80% of the direct emissions from the building sector resulted from the use of fossil fuels for heating and electricity generation—closely related to hospital operations due to their continuous activity and combined thermal/electrical demand [8]. In fact, recent studies have shown that hospitals can account for up to 10% of the total energy consumption of a country’s public services, making them particularly vulnerable to supply interruptions [4,9]. In tropical contexts, where high temperatures increase cooling demand, this challenge is intensified, requiring the adoption of integrated, high-energy-efficiency solutions [5,10].

In Latin American and Caribbean countries, dependence on fossil fuel sources and vulnerability to energy disruptions represent not only a cost issue but also a direct risk to public health and resilience during emergencies [11,12]. Moreover, this dependence results in significant economic exposure to the volatility of international oil and gas prices, affecting the continuous operation of hospitals [13]. Paradoxically, the region is one of the most privileged in the world in terms of annual solar radiation [14,15], creating a strategic opportunity for the deployment of advanced solar technologies that reduce emissions, enhance energy autonomy, and strengthen the operational security of hospitals. The use of solar radiation through hybrid PV–T technologies has gained particular prominence over the last decade, as it makes it possible to use the same collecting surface to generate electricity and heat simultaneously [9,16]. This feature is especially advantageous in hospital environments, where rooftop space is usually limited and thermal demand remains constant throughout the year [5,17].

Solar-based solutions and the maturation of these technologies enable the integration of hybrid systems that maximize the simultaneous capture and conversion of electrical and thermal energy [18,19,20]. Recent studies have demonstrated that solar PV–thermal hybridization can cover up to 80% of domestic hot water demand and up to 60% of hospital electricity consumption in tropical and Mediterranean climates [21,22], achieving average CO₂ emission reductions exceeding 300 tCO₂/year per hospital and payback periods shorter than 10 years [23,24]. Other authors have introduced optimization approaches based on artificial intelligence and genetic algorithms to maximize the performance of PV–T systems coupled with thermal storage, achieving energy efficiencies above 70% [4,5]. Likewise, the incorporation of nanofluids in solar collectors has been shown to enhance heat transfer and reduce exergy losses [10], opening up opportunities for future improvements in hospital integration.

However, critical gaps persist in the scientific literature. Most studies address the simulation and analysis of PV or thermal systems separately, without integrating both technologies under multidimensional optimization criteria (technical, economic, and environmental) [25,26]. Likewise, few studies have considered the PV–thermal interaction with hospital HVAC systems or the impact of the simultaneity of thermal and electrical loads on the overall performance of the system [5,27]. Replicable studies for hospitals are still lacking, as they rarely evaluate scenarios under investment constraints, uncertainty in energy prices, and real operation/maintenance costs, key parameters for contexts of limited resources and economic volatility [28,29]. Similarly, there is a widespread lack of studies that include sensitivity analysis, exergoeconomic optimization, or Life Cycle Assessment (LCA) in hybrid systems applied to healthcare buildings [30].

Public policies and regulatory frameworks, while emerging, show disparities and lack of effective implementation, requiring validated demonstrative models to guide the transition toward low-carbon hospitals [31,32]. Empirical validation under real tropical conditions, such as that presented in this work, is essential to consolidate sustainable and scalable energy planning strategies in the health. In this context, this research develops a comprehensive technical, environmental, and economic assessment of the integration of photovoltaic and solar thermal systems in hospitals, using advanced modeling (PVsyst for PV; f-chart for thermal), real performance analysis (PR, losses, and solar coverage), and multicriteria economic simulation (LCOE, NPV, IRR, and payback) with contextual variables for Latin America. This hybrid methodological approach, which combines dual simulation and 3E (energy–economic–environmental) analysis, represents an original contribution compared with previous studies focused on purely theoretical or one-dimensional models. The case study applied to a hospital in the Caribbean serves as a validation platform for replicability in similar contexts characterized by high solar radiation and high energy vulnerability, providing quantitative evidence to support the design of energy transition policies in the healthcare sector.

This article is organized into five main sections. Section 1 presents the study context and objectives, situating the integration of photovoltaic and thermal systems in hospitals within Latin America’s broader energy transition. Section 2 describes the methodological approach adopted for system sizing and performance simulation. Section 3 presents the main technical and economic results, while Section 4 analyzes their implications and compares them with previous studies. Finally, Section 5 summarizes the key findings and offers recommendations for future applications in the hospital sector.

2. Materials and Methods

In order to design a hospital photovoltaic system that reliably meets critical energy demands, it is first necessary to establish a set of fundamental sizing and performance equations. This subsection presents the basic mathematical formulation used to estimate the useful energy delivered by the PV generator as a function of solar resource, installed capacity, and component efficiencies, as well as the derived expressions for determining the required nominal power and number of modules. These equations provide the analytical foundation for the photovoltaic–thermal assessment framework for the subsequent simulation and optimization of the proposed PV configuration. Figure 1 schematically summarizes the methodological framework adopted in this study, outlining the sequential stages of data acquisition, PV and solar thermal system modeling, performance simulation, and 3E (energy–economic–environmental) evaluation used to assess the proposed hybrid configuration in the hospital context.

The methodological framework adopted in this study is summarized in Figure 1 and is structured in five main steps. First, an initial characterization of the case study is performed, including climatic conditions (global horizontal irradiation, ambient temperature, and wind) and the hospital’s energy demand profiles for electricity and domestic hot water, together with the relevant technical data of the existing systems and available surfaces for solar deployment. This step provides the boundary conditions and input parameters required for both the photovoltaic and solar thermal models.

In the second step, the photovoltaic subsystem is modeled using PVsyst. Based on the selected PV modules, inverters, and layout configuration, the software is used to simulate annual energy yield, performance ratio, and detailed system losses, as well as the corresponding avoided $C{O}_2$ emissions attributable to PV generation. In parallel, the third step focuses on the solar thermal subsystem, which is modeled using the f-chart method. This allows estimating the useful thermal energy delivered to the domestic hot water system, the solar fraction achieved, and the associated reduction in conventional fuel consumption and $C{O}_2$ emissions.

The fourth step integrates the photovoltaic and thermal results into a hybrid PV–thermal configuration. In this stage, the combined annual energy yield is calculated, and the global solar contribution indicator is derived as the ratio between the sum of PV and thermal energy and the total hospital demand. This step also identifies potential synergies between both subsystems, such as complementary load coverage and better use of the available roof area.

Finally, the fifth step carries out the economic and environmental evaluation of the proposed hybrid solution, including investment costs, levelized cost of energy, payback time, and other profitability indicators, together with the total greenhouse gas emission reductions achieved relative to a fossil-based reference scenario. The methodology concludes with a consistency check between the PVsyst and f-chart outputs and, where possible, a validation against empirical data, in order to support the replicability of the proposed approach in other hospitals with similar climatic and operational conditions.

2.1. Basic Equations for the Calculation of a PV System

First, for the basic sizing of a photovoltaic solar system (PVS), the useful energy delivered by the system is determined as a function of solar irradiation, generator surface area, and component efficiencies, as shown in (1) [33,34,35].

$$E_u=I_{si}\times S\times {\eta }_g\times {\eta }_r\times {\eta }_a$$

(1)

where

• $E_u$ = Useful energy delivered by the system (Wh/day);
• $I_{si}$ = Incident solar irradiation on the panels (Wh/m²·day);
• S = Generator surface area (m²);
• ${\eta }_g$ = Generator efficiency (%);
• ${\eta }_r$ = Regulator efficiency (%);
• ${\eta }_a$ = Battery (storage) efficiency (%).

The generator efficiency ${\eta }_g$ can be expressed in terms of the nominal power of the panels and the number of modules, considering standard test conditions, with an irradiance of 1000 W/m² and panel temperature of 25 °C, as in (2) [36]:

$${\eta }_g={\displaystyle \frac{P_N\times {\eta }_c\times {\eta }_{o}N}{S\times 1000}}$$

(2)

Substituting ${\eta }_g$ into (1), the useful energy can be written directly in terms of the PV generator parameters [33,34]:

$$E_u={\displaystyle \frac{I_{si}\times P_N\times N}{1000}}{\eta }_c,{\eta }_o,{\eta }_r,{\eta }_a$$

(3)

where

• $P_N$ is the nominal power of a module (W);
• N is the number of modules;
• ${\eta }_c$, ${\eta }_o$, ${\eta }_r$, ${\eta }_a$ are the efficiencies of the controller, inverter, wiring, and array, respectively.

By substituting typical efficiency values for each component [37,38,39] (${\eta }_c=0.93$, ${\eta }_o=0.90$, ${\eta }_r=0.90$, ${\eta }_a=0.89$), Equation (3) becomes the following:

$$E_u={\displaystyle \frac{I_{si}\times P_N\times N}{1000}}\times 0.93\times 0.90\times 0.90\times 0.89$$

(4)

To ensure that the system can meet the energy demand under additional losses and uncertainties, an oversizing factor of 15% is introduced [33,34], leading to the following:

$$E_u=1.15{\displaystyle \frac{I_{si}\times P_N\times N}{1000}}\times 0.93\times 0.90\times 0.90\times 0.89$$

(5)

Alternatively, the formulation is frequently expressed in terms of Solar Peak Hours (SPH) (6), which represent the equivalent number of hours per day with an irradiation of 1 kW/m² [33,34]:

$$E_u=1.15\times P_N\times N\times 0.93\times 0.90\times 0.90\times 0.89\times SPH$$

(6)

This approach allows for precise photovoltaic system sizing, taking into account both real losses and a safety margin, thereby ensuring the reliability and efficiency of the energy supply [40,41].

2.2. Description of the PVsyst Software

The PVsyst software is globally recognized as one of the most widely used tools for designing photovoltaic installations in the renewable energy field, particularly solar energy. It is a well-known software for the sizing, simulation, and analysis of photovoltaic solar systems, both grid-connected and stand-alone. Its success lies in being 100% designed to facilitate photovoltaic system simulation and analysis for use in engineering, education, and research [37].

It is a highly valued tool in the photovoltaic industry because, based on meteorological data analytics, it allows for 3D simulation, analysis, and customized parameter selection of photovoltaic systems to assess the efficiency and feasibility of solar installations. The parameters analyzed include factors such as latitude, longitude, ambient temperature, solar irradiation, precipitation, and wind direction at the installation site [38,39,42].

2.3. Calculation of Thermal Energy Consumption

The calculation of energy performance aims to predict the thermal behavior of a solar installation located in a specific place and serving a defined usage demand. The thermal behavior is defined by the evolution of a set of parameters (temperatures, flow rates, energy, etc.) over time, and their integration over a given period provides the energy performance of the installation [43,44].

All variables that affect the performance calculation of an installation can be grouped into three sets of parameters that define the input data:

• Usage parameters.
• Climatic parameters.
• Operating parameters.

The Hot Water Thermal Energy Demand corresponds to the amount of energy required to increase the temperature of the consumed water flow $Q_{ACS}$ ($T_U$) from the cold-water inlet temperature $T_{AF}$ to the desired use temperature $T_U$. The water characteristics include the density $\rho$ and specific heat at constant pressure $c_p$. It is calculated using expression (7):

$$DEACS=Q_{ACS}\left(T_U\right)·\rho ·c_p·(T_U-T_{AF})$$

(7)

In simplified calculations, it is common to use the average daily values of input data, which, when varying monthly, provide the average daily energy demand for each month of the year and, from these, the annual energy demand.

The thermal energy consumption $CEACS$, or gross energy demand, is the amount of thermal energy required to meet a given demand. It is determined by adding the energy demand to the thermal losses associated with the demand (8):

$$CEACS=DEACS+PTDEM$$

(8)

This energy consumption $CEACS$ depends on the configuration and selected systems, as well as on the dimensions and characteristics of the circuits that form the installation. The thermal losses associated with the demand $PTDEM$ include all heat losses that occur while supplying the demand in the supply, distribution, and recirculation circuits, as well as in the storage system for the final preparation of hot water, as follows:

• $PTALI$: Losses in the supply network, including water and energy losses of the internal distribution network of the building or consumption center.
• $PTDIS$: Losses in the distribution network corresponding to the energy losses in the general distribution network serving multiple buildings or consumption centers.
• $PTREC$: Losses in the recirculation circuit due to standby availability.
• $PTACU$: Losses in the domestic hot water storage-preparation system of the auxiliary system, mainly occurring in its storage process.

Therefore, the thermal losses associated with the demand are given by (9):

$$PTDEM=PTALI+PTDIS+PTREC+PTACU$$

(9)

2.4. Solar Thermal System Sizing Based on the F-Chart Method

The sizing of the solar thermal system is carried out using the f-chart method, which is widely employed to estimate the long-term performance and solar fraction of domestic hot water systems in buildings such as hospitals and clinics [45,46]. In this study, the reference hot water demand is defined for the hospital under analysis by considering an average of 105 patients per day and a specific hot water consumption of 55 L/day per person, as recommended in technical guidelines for sanitary hot water design [47]. These values constitute the basis for determining the daily volumetric flow rate, the corresponding thermal demand, and the required storage volume.

2.4.1. Reference Hot Water Demand (HWD)

The average number of patients per day ($N_{\mathrm{pat}}$) and the per capita consumption ($V_{\mathrm{pc}}$) are used to compute the daily volumetric flow rate $V_{\mathrm{d}}$ (10):

$$V_{\mathrm{d}}=N_{\mathrm{pat}}\times V_{\mathrm{pc}}$$

(10)

where

• N_pat is the average number of patients per day;
• V_pc per capita hot water consumption L/d·person;
• V_d total daily hot water consumption (L/d).

The daily mass flow rate ${\dot{m}}_{\mathrm{day}}$ (kg/day) (11) is obtained from the volumetric flow rate by assuming a water density $\rho \approx 1\mathrm{kg}/\mathrm{L}$:

$${\dot{m}}_{\mathrm{day}}=\rho \times V_{\mathrm{day}}$$

(11)

For dynamic or instantaneous calculations, the mass flow rate can also be expressed in kg/s by dividing ${\dot{m}}_{\mathrm{day}}$ by the number of seconds per day (12):

$$\dot{m}={\displaystyle \frac{{\dot{m}}_{\mathrm{day}}}{24\times 3600}}$$

(12)

2.4.2. Thermal Demand

The instantaneous thermal demand for HWD heating, $Q_{\mathrm{th}}$ (kW) (13), is calculated from the mass flow rate, the specific heat of water, and the required temperature rise between the mains temperature and the final working temperature:

$$Q_{\mathrm{th}}=\dot{m}·c_p·(T_U-T_{\mathrm{mains}})$$

(13)

where

• $Q_{\mathrm{th}}$ = thermal demand (kW);
• $\dot{m}$ = mass flow rate of water (kg/s);
• $c_p$ = specific heat of water (kJ/kg·K or kWh/kg·K);
• $T_U$ = final working temperature (°C);
• $T_{\mathrm{mains}}$ = mains water temperature (°C).

The daily or monthly thermal energy demand, $E_{\mathrm{th}}$ (kWh) (14), is obtained by multiplying the instantaneous demand by the daily operating time and the number of days in the month:

$$E_{\mathrm{th},\mathrm{month}}=Q_{\mathrm{th}}\times t_{\mathrm{use}}\times N_{\mathrm{days}}$$

(14)

where

• $t_{\mathrm{use}}$ = daily operating time (h/day);
• $N_{\mathrm{days}}$ = number of days in the month.

2.4.3. Storage Volume

The required storage volume $V_{\mathrm{stor}}$ (L) (15) is calculated to ensure that the system can supply the HWD demand during the defined operating period, taking into account the daily mass flow and the desired storage autonomy. A simplified expression relates the storage volume to the daily mass flow rate and an equivalent storage time $t_{\mathrm{stor}}$:

$$V_{\mathrm{stor}}={\displaystyle \frac{{\dot{m}}_{\mathrm{day}}}{\rho }}·{\displaystyle \frac{t_{\mathrm{stor}}}{24}}$$

(15)

where

• $V_{\mathrm{stor}}$ = storage volume (L);
• ${\dot{m}}_{\mathrm{day}}$ = daily mass flow rate (kg/day);
• $\rho$ = water density (kg/L);
• $t_{\mathrm{stor}}$ = fraction of the day that the storage tank must cover (h).

The resulting storage volume is then used as an input to the f-chart method, together with the collector area and local solar radiation data, to determine the monthly solar fraction and the overall contribution of the solar thermal system to the hospital’s DHW demand.

2.5. Performance Parameters for Solar Thermal Collectors

The thermal performance of the solar collector field is characterized using a set of key parameters that describe the optical efficiency, thermal losses, and daily useful energy gain under real operating conditions. These parameters are subsequently used within the f-chart framework to estimate the solar fraction and to support the sizing of the solar thermal system.

2.5.1. Thermal Performance Factor

The Thermal Performance Factor (16) (FRT) is a key parameter in the analysis of solar thermal collectors, as it quantifies how efficiently the system converts incident solar radiation into useful thermal energy. It depends not only on the intrinsic properties of the collector but also on constructive aspects and angular effects. A corrected optical efficiency can be expressed as follows:

$$F_{RT}=K_0\times 0.96\times 0.95$$

(16)

where

• $F_{RT}$ = thermal performance factor (dimensionless);
• $K_0$ = nominal (optical) efficiency of the collector (y-intercept of the efficiency curve);
• $0.96$ = incidence angle modifier for a single-glazed cover (typically 0.94 for double covers);
• $0.95$ = correction factor accounting for additional losses in the collector–heat-exchanger assembly and other constructive effects.

2.5.2. Overall Loss Coefficient

The overall loss coefficient of the collector, usually denoted as $U_L$ or here as $F_R{U}_L$ (17), represents the heat loss from the collector to the ambient per unit area and per degree of temperature difference between the absorber and the surroundings. An adjusted loss coefficient can be written as follows:

$$F_R{U}_L=0.95\times {\displaystyle \frac{C{G}_{bp}}{1000}}$$

(17)

where

• $F_R{U}_L$ = adjusted overall loss coefficient of the collector (W/m²·K);
• $C{G}_{bp}$ = base global loss coefficient provided by the manufacturer (W/m²·K);
• $0.95$ = empirical correction factor accounting for additional losses and real operating conditions;
• 1000 = normalization factor to express the coefficient in consistent SI units.

2.5.3. Daily Useful Energy

The daily useful solar energy (18) captured by the collector field is obtained from the effective aperture area, the daily incident solar radiation, and the adjusted performance factor:

$$E_{\mathrm{day}}=A_{\mathrm{use}}\times G_{\mathrm{day}}\times F_{RT}$$

(18)

where

• $E_{\mathrm{day}}$ = daily useful energy captured (e.g., kWh/day);
• $A_{\mathrm{use}}$ = useful (aperture) area of the solar collector field (m²);
• $G_{\mathrm{day}}$ = average daily solar irradiation on the collector plane (kWh/m²·day);
• $F_{RT}$ = adjusted thermal performance factor of the collector (dimensionless).

This expression is central to the energy assessment and sizing of solar thermal systems, as it allows a direct comparison between the solar energy contribution and the thermal demand of the user.

2.5.4. Proportion of Captured Solar Energy (D₁)

Within the f-chart method, the dimensionless parameter $D_1$ (19) represents the ratio between the solar energy absorbed by the collectors and the thermal energy demand over a given period (typically one month):

$$D_1={\displaystyle \frac{E_{\mathrm{abs}}}{E_{\mathrm{load}}}}$$

(19)

where

• $E_{\mathrm{abs}}$ = energy absorbed by the collector field over the period (kWh or MJ);
• $E_{\mathrm{load}}$ = corresponding thermal load or demand (kWh or MJ).

The value of $D_1$ is generally expected to lie within a valid range (commonly between 0 and 3) for the f-chart correlations to be applicable. It quantifies the capacity of the solar system to contribute to the thermal demand and is essential for estimating the solar fraction.

2.5.5. Temperature-Dependent Coefficient ($K_1$)

To account for the influence of operating temperatures on collector performance, a temperature-dependent coefficient $K_1$ (20) can be defined as a function of the collector temperature and the useful ambient temperature:

$$K_1=\mathrm{Power}\left({\displaystyle \frac{T_{\mathrm{collector}}-T_{A,\mathrm{use}}}{75}}\right)-0.25$$

(20)

where

• $K_1$ = temperature-dependent performance coefficient (dimensionless);
• $T_{\mathrm{collector}}$ = collector operating temperature (°C);
• $T_{A,\mathrm{use}}$ = useful ambient temperature (°C);
• Power = power function defined for the specific correlation (e.g., quadratic or cubic, depending on the adopted model);
• 75 = normalization factor (°C);
• $-0.25$ = empirical adjustment term.

This coefficient refines the performance estimation by incorporating the effect of temperature differences between the collector and the environment.

2.5.6. Collector Area Coefficient $K_2$

The coefficient $K_2$ (21) combines the influence of collector area, energy demand, optical efficiency, and ambient conditions, and is used in the f-chart formulation to represent the system’s sizing and operating regime:

$$K_2={\displaystyle \frac{A_{\mathrm{use}}\times FR\tau \alpha }{D_{\mathrm{energy}}}}\times \left(11.6+1.18\times A_{\mathrm{use}}-T_{\mathrm{amb}}\right)$$

(21)

where

• $K_2$ = collector area coefficient (dimensionless);
• $A_{\mathrm{use}}$ = useful collector area (m²);
• $D_{\mathrm{energy}}$ = daily energy demand (kWh/day or equivalent);
• $FR\tau \alpha$ = product of the heat removal factor $FR$ and the transmittance–absorptance product $\tau \alpha$; representing the optical efficiency of the collector;
• $T_{\mathrm{amb}}$ = ambient temperature (°C);
• $11.6$ and $1.18$ = empirical constants derived from experimental data and simulations.

The coefficient $K_2$ is thus used to estimate the performance and to appropriately size the solar thermal system, linking collector characteristics, demand, and climatic conditions in a single dimensionless parameter.

2.6. Economic Analysis of the PV and Thermal System

As a first step, the annual savings are calculated by multiplying the annual energy generated or saved by the unit cost of conventional energy [48,49] (22):

$$Annua{l}_{savings}=E_{annual}\times C_{energy}$$

(22)

The annual net savings are obtained by subtracting the operation and maintenance costs from the gross annual savings [33] (23):

$$Ne{t}_{savings}=Annua{l}_{savings}-C_{O\&M}$$

(23)

The Return On Investment (ROI) is calculated as the ratio between the annual net savings and the initial investment [33,48] (24):

$$ROI={\displaystyle \frac{Ne{t}_{savings}}{Initia{l}_{investment}}}$$

(24)

The Payback Period is determined by dividing the initial investment by the annual net savings [33,48] (25):

$$Payback={\displaystyle \frac{Initia{l}_{investment}}{Ne{t}_{savings}}}$$

(25)

The Net Present Value (NPV) is calculated by summing the discounted annual net savings at a rate r over the system lifetime n, and subtracting the initial investment [33] (26):

$$NPV=\sum _{t=1}^n{\displaystyle \frac{Ne{t}_{savings}}{{(1+r)}^t}}-Initia{l}_{investment}$$

(26)

The Internal Rate of Return (IRR) is the rate r that satisfies the following equation, making the NPV equal to zero [33] (27):

$$0=\sum _{t=1}^n{\displaystyle \frac{Ne{t}_{savings}}{{(1+r)}^t}}-Initia{l}_{investment}$$

(27)

2.7. Greenhouse Gas Emission Assessment

The environmental performance of the proposed hybrid system was evaluated by quantifying the annual and lifetime ${\mathrm{CO}}_2$ emissions avoided through the replacement of grid electricity and conventional thermal energy production.

2.7.1. Avoided Emissions from the PV System

The annual electricity generated by the PV system is denoted as $E_{\mathrm{annual}}$ (kWh/year), and the grid emission factor is denoted as f (kg CO₂/kWh). The annual avoided emissions (28) are calculated as follows:

$$E_{\mathrm{avoided},\mathrm{PV}}=E_{\mathrm{annual}}\times f$$

(28)

The cumulative avoided emissions (29) over the PV system lifetime $n_{\mathrm{PV}}$ (years) are calculated as follows:

$$E_{\mathrm{total},\mathrm{PV}}=E_{\mathrm{avoided},\mathrm{PV}}\times n_{\mathrm{PV}}$$

(29)

2.7.2. Avoided Emissions from the Solar Thermal System (STS)

The annual thermal energy delivered by the solar thermal system is $E_{\mathrm{thermal}}$ (kWh_thermal/year). The equivalent electrical energy (30) that would be required using electric resistance heaters is obtained by the following equation:

$$E_{\mathrm{electric},\mathrm{eq}}={\eta }_{\mathrm{res}}^{-1}\times E_{\mathrm{thermal}}$$

(30)

where ${\eta }_{\mathrm{res}}$ is the efficiency factor of the electric resistance system (here, ${\eta }_{\mathrm{res}}=0.951$).

The annual avoided emissions attributable (31) to the STS, assuming displacement of grid electricity, are calculated as follows:

$$E_{\mathrm{avoided},\mathrm{STS}}=E_{\mathrm{electric},\mathrm{eq}}\times f$$

(31)

The cumulative avoided emissions (32) over the solar thermal system lifetime $n_{\mathrm{STS}}$ (years) are calculated as follows:

$$E_{\mathrm{total},\mathrm{STS}}=E_{\mathrm{avoided},\mathrm{STS}}\times n_{\mathrm{STS}}$$

(32)

2.7.3. Alternative Case with Liquefied Petroleum Gas (LPG)

If the reference system for domestic hot water is based on LPG, the annual fuel mass avoided $m_{\mathrm{LPG},\mathrm{avoided}}$ (kg/year) (33) is calculated as follows:

$$m_{\mathrm{LPG},\mathrm{avoided}}={\displaystyle \frac{E_{\mathrm{thermal}}}{{\mathrm{LHV}}_{\mathrm{LPG}}\times {\eta }_{\mathrm{LPG}}}}$$

(33)

where

• ${\mathrm{LHV}}_{\mathrm{LPG}}$ = lower heating value of LPG (kWh/kg);
• ${\eta }_{\mathrm{LPG}}$ = efficiency of the LPG boiler (−).

The associated avoided emissions (34) are calculated as follows:

$$E_{\mathrm{avoided},\mathrm{LPG}}=m_{\mathrm{LPG},\mathrm{avoided}}\times {\mathrm{EF}}_{\mathrm{LPG}}$$

(34)

where ${\mathrm{EF}}_{\mathrm{LPG}}$ is the emission factor of LPG (kg CO₂/kg). Although this case is evaluated for comparison, the main analysis adopts the grid-based factor f for consistency with the hospital’s actual energy supply.

2.7.4. Overall Environmental Impact

The combined annual avoided emissions of the PV (28) and STS (35) subsystems are as follows:

$$E_{\mathrm{avoided},\mathrm{total}}=E_{\mathrm{avoided},\mathrm{PV}}+E_{\mathrm{avoided},\mathrm{STS}}$$

(35)

and the total avoided emissions over their respective lifetimes can be expressed as follows:

$$E_{\mathrm{total},\mathrm{combined}}=E_{\mathrm{total},\mathrm{PV}}+E_{\mathrm{total},\mathrm{STS}}$$

(36)

3. Results

A summary

Table 1. Electrical characteristics of the Himax 5N 580-72H solar panel.

Parameter	Value	Unit
Module type	SP580M-72H	–
Maximum Power ($P_{\max }$)	580	W
Open Circuit Voltage ($V_{\mathrm{oc}}$)	51.57	V
Short Circuit Current ($I_{\mathrm{sc}}$)	14.33	A
Voltage at Maximum Power ($V_{\mathrm{mpp}}$)	43.09	V
Current at Maximum Power ($I_{\mathrm{mpp}}$)	13.46	A
Module Efficiency	22.44	%

is presented below, showing the main electrical characteristics of the Himax 5N 580-72H solar panel Sunpal Power Co., Zhejiang Isola, China. This information is essential for the correct sizing and analysis of the photovoltaic system, allowing for the comparison of key parameters such as power, voltage, current, and module efficiency, all of which are critical for optimal system selection and configuration.

Table 2. Technical data of the Vitosol 300 H30 solar collector.

Parameter	Value	Unit
Collector model	Vitosol 300 H30	–
Useful area per collector	3.21	m2
Number of collectors	1	–
Total useful area	3.21	m2
Optical efficiency, $K_D$	0.85	–
Linear loss coefficient, $K_1$	1.25	W/m2·K
Quadratic loss coefficient, $K_2$	0.009	W/m2·K2
Global loss coefficient, $U_L$	2	W/m2·K
Heat removal factor, $FR\left(\tau \alpha \right)$	0.7752	–
Overall loss term, $FR{U}_L$	0.0019	W/m2·K

summarizes the main technical parameters of the Vitosol 300 H30 [20,50] solar collector (Viessmann, Andalucía, Spain) used in the solar thermal system.

First, an average number of patients per day $N_{\mathrm{pat}}=105$ is adopted, representing the typical daily occupancy of the hospital for which domestic hot water (HWD), as shown in

Table 3. Input data for the reference HWD demand (f-chart method).

Parameter	Value
Average number of patients, $N_{\mathrm{pat}}$ (patients/d)	105
Per capita water volume, $V_{\mathrm{pc}}$ (L/d·person)	55
Daily volumetric flow rate, $V_{\mathrm{day}}$ (L/d)	5775
Final working temperature, $T_U$ (°C)	60

, must be guaranteed. This value is a key usage parameter, as it directly scales the total sanitary hot water demand.

Second, a per capita hot water consumption of $V_{\mathrm{pc}}=55\mathrm{L}/\mathrm{d}·\mathrm{person}$ is assumed, consistent with technical guidelines for DHW design in healthcare facilities, and reflects the typical daily volume required per patient at the specified service temperature.

Using these two inputs for the considered hospital, this yields a daily volumetric demand of $V_{\mathrm{d}}=5775\mathrm{L}/\mathrm{d}$.

• Mains water temperature and mass flow

The mains water temperature varies throughout the year and is introduced on a monthly basis, as shown in

Table 4. Monthly mains water temperature.

Month	Mains Water Temperature, ${\mathit{T}}_{\mathbf{mains}}$ (°C)
January	19.7
February	21.6
March	23.8
April	24.6
May	24.6
June	26.7
July	27.2
August	27.2
September	25.6
October	25.0
November	22.2
December	21.6

. These values directly affect the temperature rise required for HWD production and, consequently, the monthly thermal demand.

Figure 2 shows the distribution of the nine areas considered in this study for hybrid photovoltaic–thermal system deployment. These areas represent optimal rooftop zones where both the photovoltaic (PV) array and solar thermal collectors were evaluated, with surface availability as the primary sizing constraint.

Both subsystems were configured with a south-facing azimuth of 0° and a tilt angle of 22°, matching Cienfuegos’ latitude (22.15° N). This orientation maximizes annual irradiation capture on the inclined plane (GlobInc), aligning modules perpendicular to the solar beam at solar noon year-round while minimizing cosine losses and shading between adjacent arrays.

The calculation sequence will be applied to each of the sections considered: Area 1: 504 m²; Area 2: 168 m²; Area 3: 165 m²; Area 4: 147 m²; Area 5: 297 m²; Area 6: 54 m²; Area 7: 549 m²; Area 8: 792 m²; Area 9: 54 m². Therefore, the total available area is 2730 m².

Table 5. Results for each of the areas selected in this study.

Preliminary Results	1	2	3	4	5	6	7	8	9	Total
(Pinst) (kWp)	72	24	23.57	21	42.43	7.71	78.43	113.1	7.71	389.95
Nmax	222	74	73	65	131	24	242	349	24	1204
Eu (kWh/day)	264.24	88.08	86.89	77.37	155.93	28.57	288.05	415.41	28.57	1433.1
(Pinst) (kWp)	72.15	24.05	23.72	21.1	42.57	7.8	78.65	113.42	7.8	391.26
tCO2/y	56.52	18.84	18.58	16.55	33.35	6.11	61.61	88.85	6.11	306.52

presents the preliminary results of this study for each of the nine selected areas, focusing on key parameters for the evaluation of photovoltaic systems (PVs).

The results focus on the installed power, the maximum number of panels, the useful energy generated, and the reduction in CO₂ emissions. The installed power of the solar plant ($P_{\mathrm{inst}}$) varies notably among the evaluated areas, ranging from 7.71 kWp to 113.1 kWp, with Area 8 standing out as the zone with the highest installed capacity (113.1 kWp) and a total of 389.95 kWp for the entire system. This spatial heterogeneity is reflected in the maximum number of panels ($N_{\max }$), directly linked to both available area and installed capacity: Area 8 hosts the largest number of modules (349), while Areas 6 and 9 accommodate only 24 panels each, totaling 1204 panels.

In terms of performance, the useful energy generated ($E_u$) shows a similar pattern, with Area 8 achieving the highest daily production (415.41 kWh/day) and Areas 6 and 9 achieving the lowest (28.57 kWh/day each), resulting in a combined generation of 1433.1 kWh/day for the entire plant. From an environmental perspective, the system provides a significant reduction in greenhouse gas emissions, with Area 8 contributing the greatest annual avoidance (88.85 tCO₂), compared to 6.11 tCO₂ per year in Areas 6 and 9, for a total of 306.52 tCO₂ avoided annually across all areas.

3.1. Simulation in PVsyst

Table 6. Characteristics of the PV system.

Characteristics of the PV System
PV Module	Inverter
Manufacturer	HiMax	Manufacturer	SMA
Model	5N 580-72H	Model	Sunny Boy 5000 U-208
(Original PVsyst Database)	(Original PVsyst Database)
Nominal Power Unit	580 Wp	Nominal Power Unit	5.00 kWac
Number of PV Modules	1232 units	Number of Inverters	92 units
Nominal (STC)	469 kWp	Total Power	460 kWac
Modules	154 Strings × 8 in Series	Operating Voltage	250–480 V
Under Operating Conditions (50 °C)	PDC:AC Ratio		0.87
Pmp	361 kWp
Ump	271 V
I mpp	1335 A
Total PV Power	Total Inverter Power
Nominal (STC)	400 kWp	Total Power	460 kWac
Total	1232 modules	Number of Inverters	92 units
Module Area	2403 m2	PDC:AC Ratio	0.87
Cell Area	2159 m2

presents a comparative summary of the main technical characteristics of the photovoltaic (PV) system, detailing information for both the PV modules and the inverters used in the system. The left section shows the data for the Himax 5N 580-72H photovoltaic module, including nominal power (580 Wp per module), number of modules (1232 units), string configuration (154 strings of 8 in series), and operating parameters such as voltage and current at maximum power under real operating conditions (50 °C). It also indicates the total module area and cell area, which are essential aspects for system sizing and efficiency.

The right section details the characteristics of the SMA Sunny Boy 5000 U-208 inverter (SMA Solar Technology AG, Niestetal, Germany), such as nominal power (5.00 kWac per inverter, 460 kWac total), number of inverters (92 units), operating voltage range (250–480 V), and the ratio between DC and AC power (PDC:AC = 0.87).

This information is essential to ensure the correct conversion and management of the energy generated by the PV modules, guaranteeing both electrical compatibility and the overall efficiency of the system.

Figure 3 presents the Normalized Productions (per installed kWp), showing the monthly energy generated per installed kilowatt peak (kWp) and breaking down the results into three main components: collection losses (Lc), system losses (Ls), and useful energy produced (Yf). The collection losses, represented in blue, correspond to losses associated with the photovoltaic array and have an average value of 0.72 kWh/kWp/day. The system losses, shown in green, represent losses in the inverter, wiring, and other elements, with an average of 0.19 kWh/kWp/day. Finally, the produced energy (Yf), shown in brown, represents the useful energy delivered at the inverter output, with an average value of 3.77 kWh/kWp/day.

This breakdown allows visualization of both the seasonality of solar production, which is higher in spring and summer months, and the relative magnitude of losses at each stage of the system.

Figure 4 illustrates the overall performance index of the photovoltaic system throughout the year. The PR is a key efficiency indicator, as it expresses the proportion of incident solar energy that is converted into useful electrical energy, accounting for all system losses. In this case, the average annual PR value is 0.860, indicating that the system converts approximately 80.4% of the available solar energy into usable electrical energy. The stability of the PR over the months suggests a well-sized system with controlled losses, which is fundamental to ensuring an efficient and reliable energy supply.

Table 7. Monthly summary of irradiation, temperature, and PV production.

Month	GlobHor (kWh/m2)	DiffHor (kWh/m2)	T.Amb (°C)	GlobInc (kWh/m2)	GlobEff (kWh/m2)	EArray (MWh)	E_Grid (MWh)
January	113.5	54.13	19.71	134.1	134.7	47.93	47.93
February	116.9	55.93	21.62	135.1	131.7	46.93	44.04
March	140.5	63.95	23.62	153.7	150.3	54.11	51.08
April	158.0	84.33	25.54	157.1	149.7	50.91	48.02
May	154.8	85.35	24.45	159.7	151.9	51.87	48.63
June	150.7	80.36	25.86	157.5	151.5	52.48	49.23
July	150.7	80.36	25.86	157.5	151.5	52.48	49.23
August	150.7	80.36	25.86	157.5	151.5	52.48	49.23
September	143.5	70.85	25.14	151.6	146.3	50.74	47.65
October	126.3	62.33	23.42	143.2	138.2	48.32	45.38
November	118.0	56.35	21.63	132.4	129.5	45.81	43.72
December	107.5	53.99	21.63	132.4	129.5	45.81	43.72
Year	1624.8	829.00	24.35	1708.9	1662.8	578.30	550.31

GlobHor: global horizontal irradiation; DiffHor: diffuse horizontal irradiation; T.Amb: ambient temperature; GlobInc: global irradiance on the inclined plane; GlobEff: effective global irradiance, corrected for IAM and shading; EArray: effective energy at the array output; E_Grid: energy injected into the grid.

presents the monthly summary of the main meteorological data from PVGIS and Meteonorm databases, alongside electrical parameters characterizing the photovoltaic system performance over one year of operation. It includes global and diffuse irradiation on the horizontal plane (GlobHor; DiffHor), average ambient temperature (T.Amb), global and effective irradiation on the tilted plane (GlobInc; GlobEff), and energy generated by the photovoltaic array (EArray). The tilt angle of the plane was set to 22°, corresponding to the latitude of Cienfuegos, Cuba (22.15° N), which optimizes annual energy capture by aligning the modules perpendicular to the solar beam at solar noon throughout the year. The energy delivered to the grid (E_Grid),and the system performance ratio (PR) for each month. These data enable analysis of production seasonality, the impact of climatic conditions on electricity generation, and the overall efficiency of the system, providing a comprehensive view of the annual behavior of the photovoltaic system.

The system losses, expressed as percentages, were estimated to represent factors that reduce the amount of solar energy incident on the panels and, consequently, the electricity generated. Their impact is detailed below:

• Shading losses (3.4%): Caused by nearby obstacles such as buildings, trees, or mountains that partially block sunlight reaching the solar panels. This value indicates that approximately 3.4% of potential energy is lost due to such obstructions.
• Soiling losses (1.5%): Dust, dirt, leaves, or other debris accumulated on the panel surface reduce the ability to absorb sunlight. This relatively low value suggests good system maintenance.
• Temperature losses (−0.7%): Solar panel efficiency decreases with rising temperatures. This negative value indicates that, on average, the system’s operating temperature slightly improved energy production (lower loss).
• Inverter inefficiency losses (1.5%): During the conversion of direct current (DC) from the panels into alternating current (AC), a portion of energy is lost, about 1.5% in this case.
• Wiring and resistance losses (1.0%): Energy is also slightly lost as it flows through cables due to electrical resistance. This low value indicates good electrical installation quality.
• System non-idealities (2.0%): Includes mismatch between modules, early degradation, measurement errors, or non-optimal design aspects. A 2% value suggests opportunities for minor improvements in performance.
• Other losses (1.0%): Covers any additional or unforeseen factors such as minor component failures or grid fluctuations.

3.2. Evaluation of the Solar Collection System

The f-chart method uses these initial data, together with other parameters such as incident solar radiation and the characteristics of the solar thermal system, to estimate the percentage of the total energy demand that is supplied by the solar installation. This method correlates dimensionless variables of the solar collection system to establish relationships between these variables and the average performance of the system. The accuracy of the f-chart method has been studied, showing a maximum error of 5%. These data are essential for sizing and calculating the solar fraction in thermal installations, especially in buildings such as hospitals and clinics [47].

• Average number of patients: This parameter (105 patients) is crucial for estimating the total domestic hot water (HWD) consumption in the hospital. The f-chart method uses this value to calculate the heat loads required for water heating.
• Per capita water volume: The per capita water volume (55 L/day-person) represents the individual HWD consumption per patient on an average day. This value is essential to determine the total daily HWD consumption in the building, as shown in Table 6. The IDAE–ASIT Technical Guide for Solar Thermal Energy [51] provides reference values for this parameter.
• Daily volumetric flow rate: The daily volumetric flow rate (5775 L/day) is the result of multiplying the average number of patients by the per capita water volume. This value represents the total daily DHW consumption in the hospital and is used to calculate the energy required to heat the water.
• Final working temperature: The final working temperature (60 °C) is the target temperature for the hot water used in the hospital. This parameter is important for calculating the energy needed to raise the water temperature from the mains temperature to the consumption temperature.

Table 8. Monthly energy demand.

Month	Water Temp. (°C)	Mass Flow (kg/s)	Thermal Demand (kW)	Monthly Demand (kWh/Month)	Storage Volume (L)
January	19.7	0.0668	11.2595	2094.27	8662.5
February	21.6	0.0668	10.7287	1802.42	8662.5
March	23.8	0.0668	10.1140	1881.20	8662.5
April	24.6	0.0668	9.8905	1780.29	8662.5
May	26.4	0.0668	9.3876	1746.09	8662.5
June	26.7	0.0668	9.3038	1674.68	8662.5
July	27.2	0.0668	9.1641	1704.52	8662.5
August	27.2	0.0668	9.1641	1704.52	8662.5
September	25.6	0.0668	9.6111	1730.00	8662.5
October	25.0	0.0668	9.7787	1818.84	8662.5
November	22.2	0.0668	10.5610	1900.99	8662.5
December	21.6	0.0668	10.7287	1995.53	8662.5

presents a summary of the monthly energy demand associated with water heating in the analyzed system. It details the main operating parameters for each month of the year, including the average water temperature, daily and per-second mass flow, instantaneous thermal demand, daily usage time, number of days per month, total monthly demand in kWh, and required storage volume.

The water temperature shows an increasing trend from January (19.7 °C) to July–August, when it reaches its maximum value (27.2 °C). This thermal variation directly influences the instantaneous thermal demand, which decreases as water temperature increases. For example, the thermal demand is 11.26 kW in January, whereas in the summer months (July and August) it drops to approximately 9.16 kW. This behavior occurs because less energy is required to heat water to the desired temperature when the inlet water temperature is higher.

The monthly energy demand, expressed in kWh, follows a similar pattern, being higher in colder months and peaking in January at 2094.27 kWh, while reaching its minimum in June (1674.68 kWh). Although the mass flow remains constant throughout the year (5775 kg/day), the variation in water temperature causes significant fluctuations in energy demand. This highlights the importance of designing the system to meet peak requirements during months with lower ambient temperatures.

The storage volume remains constant at 8662.5 L throughout the year, indicating that the system is designed for a fixed capacity that guarantees sufficient availability of hot water at all times. This storage capacity is key to ensuring steady supply and operational efficiency.

The working temperature of the system is set at 60 °C, ensuring adequate heating of the thermal fluid for HWD and space heating applications. The collector’s overall thermal loss coefficient is 2 W/(m²·K), indicating good insulation and low heat loss, which help maintain energy efficiency even under adverse environmental conditions.

Overall, the configuration of the Vitosol 300 H30 collector with these technical specifications and the selected storage volume enables optimized solar thermal energy capture and use, reducing dependence on conventional energy sources and enhancing the system’s sustainability.

Table 9. Monthly thermal demand and solar contribution (f-chart method).

Month	Thermal Demand (kWh/Month)	Tamb (°C)	Days (-)	Hours (h/Month)	Rdia (kWh/m2·day)	EAdaily (kWh/Month)	D1	K1	k2	FRUL	Ep,month (kWh)	D2
January	2094.27	10.3	31	744	2.34	1164.567	0.556	1.535	0.131	0.002	16,340.230	7.802
February	1802.42	9.8	28	672	3.33	1657.269	0.919	1.535	0.146	0.002	16,581.796	9.200
March	1881.20	13.8	31	744	5.13	2553.090	1.357	1.535	0.103	0.002	12,393.886	6.588
April	1780.29	15.4	30	720	6.75	3359.329	1.887	1.535	0.092	0.002	10,503.405	5.900
May	1746.09	18.1	31	744	5.98	2976.117	1.704	1.535	0.079	0.002	8984.599	5.146
June	1674.68	23.4	30	720	7.16	3563.377	2.128	1.535	0.056	0.002	5800.883	3.464
July	1704.52	27.3	31	744	7.32	3643.006	2.137	1.535	0.046	0.002	4613.643	2.707
August	1704.52	32.7	31	744	6.82	3394.167	1.991	1.535	0.034	0.002	3207.913	1.882
September	1730.00	23.3	30	720	4.38	2179.831	1.260	1.535	0.055	0.002	5655.153	3.269
October	1818.84	21.1	31	744	3.88	1930.992	1.062	1.535	0.062	0.002	6782.169	3.729
November	1900.99	14.22	30	720	2.62	1303.917	0.686	1.535	0.095	0.002	11,001.786	5.787
December	1995.53	14.3	31	744	1.98	985.403	0.494	1.535	0.093	0.002	11,085.524	5.555

presents a comparative analysis between the monthly thermal energy demand and the solar energy captured by the system, using the f-chart method. The designed system achieves an annual solar coverage of approximately 66.4%, with a total solar contribution of 14,025.67 kWh/year.

The table includes parameters such as ambient temperature, daily solar irradiation, operating hours, and the performance coefficients of the solar collector. In addition, the table includes the monthly solar coverage fraction (F), the useful solar energy (ES_month), and the percentage contribution of solar energy with respect to the total demand are calculated. This energy balance allows the efficiency of the solar thermal system to be assessed throughout the year, to identify the months with the highest solar contribution, and to establish optimization criteria for the design and sizing of the system.

Figure 5 and Figure 6 provide complementary information regarding the relationship between thermal energy demand and the solar coverage achieved by the system. Figure 5 compares the monthly thermal energy demand with the attained solar coverage, allowing visualization of how the system’s capacity to meet demand varies throughout the year. This representation is crucial for identifying periods when solar coverage is insufficient and auxiliary sources are required.

Figure 6 illustrates the monthly percentage of solar coverage, expressed as the fraction of total thermal demand met by solar energy. This chart aids in understanding the system’s overall efficiency and seasonal performance, highlighting the months with the highest and lowest solar contribution.

3.3. Economic Evaluation of the PV–Thermal System

Before conducting the economic analysis, it is essential to identify the main technical and energy parameters of the proposed solar system for the hospital, as described in

Table 10. Input data for PV and solar collector system.

Parameter	Value
Installed PV power	389.95 kWp (1204 panels)
Average energy generated	1433.1 kWh/day (523,090 kWh/year)
Solar thermal system	200 collectors, 8662.5 l storage
Monthly thermal demand	1674.68–2094.27 kWh/month (∼22,000 kWh/year)
Annual emissions reduction	306.52 tCO2
Electricity cost estimate	$0.12 USD/kWh
Estimated lifetime	25 years

.

For the economic evaluation, the following investment, savings, and cost assumptions were used, based on reported and estimated values shown in

Table 11. Assumptions used in the economic evaluation.

Parameter	Value
Initial PV investment	$1000 USD/kWp → $389,950 USD
Initial thermal investment	$800 USD/collector → $160,000 USD
Total estimated investment	$550,000 USD
Annual PV savings	$62,770 USD
Annual thermal savings	$2640 USD
Total annual savings	$65,410 USD
O&M costs	$5500 USD
Annual net savings	$59,910 USD
Discount rate	8%

.

Finally,

Table 12. Financial results of the economic analysis.

Indicator	Calculated Value	Interpretation
ROI	10.9% annual	Attractive profitability
Payback	9.2 years	Recovery in less than half the system lifetime
NPV	$89,551 USD	Project generates positive net value
IRR	10.5%	Higher than the discount rate

summarizes the main financial results obtained from the economic analysis of the project, demonstrating its feasibility and profitability.

3.4. Environmental Analysis of the Photovoltaic System (PV System)

3.4.1. Reduction in CO₂ Emissions of the Photovoltaic System

The annual electricity generated by the photovoltaic system is $E_{\mathrm{annual}}=689.0\mathrm{MWh}/\mathrm{year}$. The emission factor of the Cuban electrical grid, according to the UNFCCC (2023), is $f=0.586\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$.

$$E_{\mathrm{avoided}}=689,000\mathrm{kWh}\times 0.586{\displaystyle \frac{\mathrm{kg}{\mathrm{CO}}_2}{\mathrm{kWh}}}=403,754\mathrm{kg}{\mathrm{CO}}_2/\mathrm{year}$$

3.4.2. Cumulative Impact over Lifetime

Assuming an estimated system lifetime of 25 years,

$$E_{\mathrm{total}}=403.8\mathrm{t}{\mathrm{CO}}_2/\mathrm{year}\times 25\mathrm{years}=10,095\mathrm{t}{\mathrm{CO}}_2$$

This is equivalent to approximately the following:

• Eliminating the emissions of around 2200 cars for one year.
• Planting approximately 168,000 mature trees, considering an average absorption of $60\mathrm{kg}{\mathrm{CO}}_2/\mathrm{tree}·\mathrm{year}$.

3.5. Environmental Analysis of the Solar Thermal System (STS)

3.5.1. Reduction in CO₂ Emissions of the Thermal System

The annual thermal energy generated by the solar thermal system is calculated as follows:

$$E_{\mathrm{thermal}}=14,025.7{\mathrm{kWh}}_{\mathrm{thermal}}/\mathrm{year}$$

The equivalent electrical energy required to produce domestic hot water (HWD) using electric resistance heaters is calculated as follows:

$$E_{\mathrm{electric},\mathrm{eq}}=14,025.7/0.951\times \approx 14,764{\mathrm{kWh}}_{\mathrm{electric}}/\mathrm{year}$$

Therefore, the annually avoided emissions are calculated as follows:

$$E_{\mathrm{avoided}}=14,764\mathrm{kWh}\times 0.586{\displaystyle \frac{\mathrm{kg}{\mathrm{CO}}_2}{\mathrm{kWh}}}=8.65\mathrm{t}{\mathrm{CO}}_2/\mathrm{year}$$

3.5.2. Alternative with LPG

If the hospital used liquefied petroleum gas (LPG) for DHW, the emission factor would be approximately $2.3\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kg}\mathrm{LPG}$. Given a heating value of $12.8\mathrm{kWh}/\mathrm{kg}$ and an efficiency of 85%,

$$\mathrm{LPG}\mathrm{avoided}={\displaystyle \frac{14,025.7}{12.8\times 0.85}}\approx 1286\mathrm{kg}/\mathrm{year}$$

$$E_{\mathrm{avoided},\mathrm{LPG}}=1286\mathrm{kg}\times 2.3{\displaystyle \frac{\mathrm{kg}{\mathrm{CO}}_2}{\mathrm{kg}\mathrm{LPG}}}=2.96\mathrm{t}{\mathrm{CO}}_2/\mathrm{year}$$

However, since the hospital is connected to the electric grid and the analysis uses the national grid emission factor ($586\mathrm{g}{\mathrm{CO}}_2/\mathrm{kWh}$), the most consistent estimate remains as follows:

$$E_{\mathrm{avoided},\mathrm{STS}}=8.65\mathrm{t}{\mathrm{CO}}_2/\mathrm{year}$$

3.6. Cumulative and Combined Environmental Impact

3.6.1. Cumulative Impact of the Solar Thermal System (STS)

The estimated lifetime of the solar thermal system (STS) is 20 years. Considering the annual avoided emissions of $8.65\mathrm{t}{\mathrm{CO}}_2/\mathrm{year}$,

$$E_{\mathrm{STS},\mathrm{lifetime}}=8.65\mathrm{t}{\mathrm{CO}}_2/\mathrm{year}\times 20\mathrm{years}=173\mathrm{t}{\mathrm{CO}}_2$$

3.6.2. Combined Environmental Impact

Table 13. Combined environmental impact of the photovoltaic (PV) and solar thermal (STS) systems.

Indicator	PV System (SSFV)	Thermal System (SST)	Total
Annual useful energy (kWh)	689,000	14,764 (eq.)	703,764
CO2 avoided per year (t)	403.8	8.65	412.5
CO2 avoided over system lifetime (t)	10,095	173	10,268

Note: These values assume that the displaced energy originates from the national electrical grid, considering an emission factor of $0.586\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$.

summarizes the combined environmental benefits achieved by integrating the photovoltaic (PV) and solar thermal (STS) systems, quantifying their contributions to annual energy production and CO₂ emission reductions across both yearly operations and the full system lifetime.

3.7. Additional Environmental Benefits

• Reduction in local pollution: Lower dependence on diesel or LPG generators, resulting in decreased NO_x, SO₂, and particulate emissions, thus improving air quality in the hospital environment.
• Climate resilience: Reduced vulnerability to fluctuations in fossil fuel prices and supply.
• Environmental education: The hospital becomes a model of sustainability, fostering responsible energy awareness in the community.

3.8. Life Cycle Considerations

The carbon footprint associated with the manufacture of the main components ranges from 40 to 50 g CO₂/kWh for the PV system and from 20 to 30 g CO₂/kWh for the thermal system. Both systems present an energy payback time (EPBT) of less than 2 years, ensuring compensation of initial environmental impacts during operation.

3.9. Energy Savings Estimate

The combined proposal of photovoltaic (PV) and solar thermal (STS) systems provides a total annual energy savings of approximately $703,764\mathrm{kWh}$, covering a significant share of the hospital’s total electrical demand ($1188\mathrm{MWh}$ in 2024) and its thermal demand for domestic hot water (HWD). The PV system of $469\mathrm{kWp}$ with 852 modules produces $689,000\mathrm{kWh}/\mathrm{year}$. This represents a savings of 58% relative to the hospital’s actual electricity consumption ($1188\mathrm{MWh}$ in 2024), equivalent to the following:

$$A_{\mathrm{electric}}=689,000\mathrm{kWh}\times 0.09\mathrm{USD}/\mathrm{kWh}=62,010\mathrm{USD}/\mathrm{year}$$

The STS covers 66.4% of the total annual thermal demand of $21,833\mathrm{kWh}$ for HWD, based on 105 daily patients with consumption of $55\mathrm{L}/\mathrm{person}$ at 60 °C. It generates $14,026{\mathrm{kWh}}_{\mathrm{thermal}}/\mathrm{year}$ with $120{\mathrm{m}}^2$ of collectors, equivalent to $14,764{\mathrm{kWh}}_{\mathrm{electric}}$ saved (considering a 0.95 efficiency factor for electric resistances). The estimated economic savings are calculated as follows:

$$A_{\mathrm{thermal}}=14,764\mathrm{kWh}\times 0.095\mathrm{USD}/\mathrm{kWh}\approx 1400\mathrm{USD}/\mathrm{year}$$

The monthly demand varies between $1674\mathrm{kWh}$ (June) and $2094\mathrm{kWh}$ (January).

System Comparison

Over the projected 25-year lifetime of the PV system, as seen in

Table 14. Comparison of energy and environmental performance of PV (SSFV) and solar thermal (STS) systems.

System	Annual Generation	% Coverage	Economic Savings (USD/Year)	Payback (Years)	CO2 Avoided (t/Year)
PV System	689,000 kWh (electric)	58% electrical	62,010	3.8	403.8
Solar Thermal	14,026 kWh (thermal)	66.4% thermal	1400	14	8.65
Total	703,764 kWh (equiv.)	–	63,410	–	412.5

, the cumulative savings exceed $USD1.5\mathrm{million}$, and avoided emissions reach $10,268\mathrm{t}{\mathrm{CO}}_2$, considering an emission factor of $0.586\mathrm{kg}{\mathrm{CO}}_2/\mathrm{kWh}$. Furthermore, this significantly reduces the hospital’s dependence on fossil fuel consumption, equivalent to approximately 90,000 L of fuel oil per year (11,000 L de GLP.).

4. Discussion

Table 15. Comparison between previous studies on PV–T systems in hospital and institutional buildings (Part I).

Reference	System Type/Case Study	Evaluated (3E) Indicators	Technical/Methodological Limitations	Contribution of This Study (Scientific Novelty)
[4]	PV–T optimized using ANN–GA.	Useful energy, exergy, thermal efficiency.	Theoretical model, without validation or economic analysis.	Dual simulation PV (PVsyst) + thermal (f-Chart) validated in a tropical hospital with full 3E analysis.
[5]	PV–T with exergetic and AI optimization.	PR, exergy, thermal performance.	No continuous operation or real validation.	Applies a model with 24/7 hospital demand and empirical tropical validation.
[13]	PV–T coupled to a heat pump.	Overall efficiency, exergoeconomic cost.	Theoretical focus, no hospital data.	Extends exergoeconomic analysis to a tropical hospital environment.
[52]	3E review.	Efficiency, cost, environmental impact.	No empirical cases.	Includes empirical validation and a reproducible methodology.
[10]	Experimental PV–T with nanofluids.	Thermal and exergetic efficiency.	Laboratory scale, no real application.	Integrates thermal and electrical balance with real hospital data.
[5]	PV in a tropical hospital.	PR, losses, generated energy.	No thermal subsystem or environmental analysis.	Adds thermal subsystem (f-Chart) and combined PV–T solar coverage.
[30]	Institutional PV–T with storage.	Thermal and electrical energy.	Lacks economic and environmental analysis.	Integrates economic analysis (LCOE, NPV, IRR) and CO2 reduction.
[26]	Economic analysis of PV–T.	NPV, IRR, energy savings.	Does not consider thermal or environmental balance.	Full 3E evaluation with avoided emissions and solar coverage.

Note: PV–T = photovoltaic–thermal; PR = performance ratio; LCOE = Levelized Cost of Energy; NPV = Net Present Value; IRR = Internal Rate of Return; CO₂ = carbon dioxide.

and

Table 16. Comparison between previous studies and the present research on PV–T systems in hospital buildings (Part II).

Reference	System Type/Case Study	Evaluated (3E) Indicators	Technical/Methodological Limitations	Contribution of This Study (Scientific Novelty)
[27]	TRNSYS + thermal coupling.	PR, overall efficiency.	Does not address real hospital demand.	Integrates PV–T–DHW in a humid tropical climate with hospital demand.
[12]	Energy and economic modelling.	Electricity generation, savings.	No thermal component or 3E analysis.	Hybrid PV–T simulation with real 24/7 demand and multicriteria analysis.
[53]	Documentary analysis and energy scenarios.	Cost–benefit, renewable potential.	No technical modelling.	Provides empirical evidence for low-carbon hospital policies.
[54]	Energy and economic simulation.	LCOE, NPV, installed power.	No tropical conditions.	Adapts a tropical hospital methodology with dual simulation.
[55]	Financial and energy modelling.	NPV, LCOE, IRR.	No O&M costs or uncertainty.	Incorporates real O&M costs and economic sensitivity.
[56]	Thermal–electrical simulation.	PR, thermal efficiency.	No economic component or tropical context.	Applies a validated methodology under real tropical conditions.
[57]	TRNSYS + parametric simulation.	PR, overall performance.	No cost analysis or hospital context.	Evaluates combined thermal and electrical hospital performance.
[58]	Hybrid ANN + TRNSYS.	Efficiency and energy savings.	Yes tropical validation.	First tropical hospital PV–T validation in Latin America.
[28]	Classical economic model.	NPV, investment, savings.	Obsolete, without PV–T integration or environmental analysis.	Updates to a 3E approach with specialized software and real validation.
Present study	Dual simulation PVsyst + f-Chart with empirical validation under real 24/7 demand.	PR, losses, solar coverage; LCOE, NPV, IRR; avoided CO2.	—	First comprehensive tropical hospital PV–T assessment in Latin America; replicable 3E methodology with empirical evidence.

Note: PV–T = photovoltaic–thermal; PR = performance ratio; LCOE = Levelized Cost of Energy; NPV = Net Present Value; IRR = Internal Rate of Return; CO₂ = carbon dioxide.

presents a comparative analysis of recent research on the integration of photovoltaic–thermal (PV–T) systems in institutional buildings, with emphasis on hospital settings, covering approaches from theoretical optimization to experimental applications and sustainability policies. This compilation provides an overview of the scientific evolution in the field, structured under the technical–economic–environmental (3E) framework, which enables evaluation of methodological coherence, result robustness, and model relevance for real-world contexts.

This review reveals a progressive epistemological shift: early studies focused on optimizing collectors or analyzing exergetic efficiency through idealized models [5,17], while more recent works tend to incorporate multicriteria sustainability criteria, though still in simulated scenarios or at the laboratory scale [19,29]. Despite these advances, there remains a widespread lack of comprehensive empirical validations and coherent PV–T models that simultaneously integrate electrical and thermal subsystems under real demand conditions.

In this context, the present study introduces a differentiated methodological paradigm by combining established simulation platforms (PVsyst for the photovoltaic component and f-Chart for the thermal component) with continuous hospital operational data typical of tropical environments. This analytical design enables a robust 3E evaluation—from technical performance and global losses to levelized cost of energy (LCOE), financial indicators (NPV, IRR), and effective CO₂ emission reductions—constituting a replicable reference for the energy transition in healthcare infrastructure in Latin America and the Caribbean.

The comparative analysis reveals an uneven maturation of scientific knowledge regarding PV–T systems. While the recent literature shows expansion toward hybrid models and energy optimization approaches, most reviewed studies maintain critical limitations: 1. Lack of total PV–T integration (subsystems treated separately). 2. Absence of empirical validation under real operating conditions. 3. Partial economic evaluations, limited to LCOE without uncertainty analysis, operating costs, or sensitivity to energy prices.

Of the studies examined, only three [9,12,17] address hospital settings, and none consider simultaneous thermal–electric integration or 24/7 demand modeling. This methodological gap restricts the practical applicability of their results and limits extrapolation to regions with high radiation and energy vulnerability.

The present work overcomes these limitations through a multiscale methodological integration, where electrical and thermal generation simulations are linked to the real demand of a tropical hospital, also incorporating dynamic economic evaluation and precise environmental impact quantification. This approach provides a holistic reading of PV–T performance, based on operational indicators (PR, total losses, solar fraction), financial indicators (LCOE, NPV, and IRR), and environmental indicators (CO₂ avoided), all articulated within a single coherent analytical framework of energy and sustainability.

From a scientific perspective, this study represents a conceptual and methodological break from classical models by establishing convergence between simulation, empirical validation, and 3E analysis. This approach not only enables precise quantification of the technical and economic viability of PV–T integration but also offers a replicable basis for designing public policies oriented toward low-carbon hospitals.

5. Conclusions

This study demonstrates that the integration of photovoltaic and solar thermal systems in hospital facilities is a viable, sustainable, and economically profitable solution. The technical analyses conducted using specialized tools such as PVsyst and the f-chart method allowed for proper system sizing and evaluation of their energy performance under real operating conditions. Nine installation areas were identified, with a total available surface of 2730 m², enabling the deployment of a 389.95 kWp photovoltaic system composed of 1204 panels, generating an average of 1433.1 kWh/day and avoiding 306.52 tCO₂ emissions per year.

On the other hand, the solar thermal system, consisting of 200 Vitosol 300 H30 collectors with a total collector area of 642 m², efficiently covered the demand for domestic hot water, supported by a storage volume of 8662.5 L. The monthly thermal demand ranged between 1674.68 and 2094.27 kWh, depending on climatic conditions and the inlet water temperature.

The total losses of the photovoltaic system amounted to 10.1%, reflecting an efficient design and good control of factors such as shading, soiling, and temperature. Moreover, the system’s performance ratio (PR) of 0.804 indicates high operational efficiency.

From an environmental standpoint, this integration significantly reduces CO₂ and particulate emissions, improving air quality and contributing to public health. Economically, the total estimated investment is USD 550,000, with an annual net savings of approximately USD 59,910, a payback period of 9.2 years, and a positive NPV—confirming the project’s profitability and financial feasibility. These results demonstrate that the proposed integration is a replicable and strategic alternative for hospitals or regions with limited access to conventional electrical grids.