The Use of PCMs and PV Solar Panels in Higher Education Buildings towards Energy Savings and Decarbonization: A Case Study

Abstract

Buildings are one the largest energy-consuming sectors in the world, and it is crucial to find solutions to reduce their energy consumption. One way to evaluate these solutions is using building simulation software, which provides a comprehensive perspective. In this article, using DesignBuilder software (v 6.1), the effect of using phase-change materials (PCMs) on the external walls and ceiling of the Department of Mechanical Engineering of Shahid Beheshti University (Tehran, Iran) has been investigated. The methodology involves the use of a layer of PCMs for three locations: (1) on the walls; (2) on the ceiling; and (3) on both the walls and ceiling, with/without PV panels, which leads to seven scenarios (alongside the reference one). The result shows that using PCMs has a greater impact on the heating load than the cooling one and is more effective on ceilings than walls. For the simultaneous use of PCMs in the ceilings and walls, the heating and cooling loads, in comparison with the initial condition of the building, are reduced by 24%, and 12%, respectively. When using solar panels, the heating load increases by 12.6%, and the cooling load decreases by 8.6%, whereas the total energy consumption of the building is fairly constant when using both PV panels and PCMs. In these last conditions, the primary evaluated values shifted significantly. Notably, CO₂ emissions saw a nearly 50% reduction, making the simultaneous use of PV panels and PCMs on both walls and ceilings the best performance option.

1. Introduction

Population growth and a lack of access to affordable sources of energy has become an international issue [1,2,3]. To address these problems, increasing energy efficiency is a pivotal topic among researchers [4]. Developed countries prioritize energy efficiency, leading to widespread implementation in all sectors [5,6]. Buildings are responsible for a significant amount of energy consumed compared to other sectors [7,8]. Although these percentages vary from country to country, buildings are roughly responsible for 30–40% of global energy demand [9,10], which can be significantly reduced by adopting energy-efficient strategies [11,12,13].

In buildings, space heating, space cooling, water heating, and lighting contribute to almost 70% of total energy consumption by consumers [14]. Estimating building energy consumption has become a key approach to achieving sustainable development goals related to energy consumption and reducing greenhouse gas emissions [15,16,17]. Simulation models can be used not only to compare the economic justification of energy savings during the design phase but also to assess various performances during the operational phase [18,19]. Building energy models provide valuable insight into energy consumption in buildings based on architecture, materials, and thermal loads [20,21,22,23].

Nowadays, different methods are used to save energy. One of the passive storage methods that is becoming popular is the use of phase-change materials (PCMs) [24,25].

Table 1. A state-of-the-art review of energy-efficient methods for buildings.

Reference	Method/Material	Short Description	Main Outcomes
Yang et al. [26]	Ceramsite-based shape-stabilized composite PCMs	Experimental combination of lauryl alcohol, stearic acid, and nanoparticles with ceramsite and thermal analysis with differential scanning calorimetry	The composite with 15% nanomaterials has optimum performance, leading to a reduced thermal/cooling load.
Li et al. [27]	Glazing equipped with PCMs	Review of cutting-edge technology associated with PCM-based glazing	Discussion of research methodologies, constraints, experimental test rigs, numerical models, and future developments.
Sedaghat et al. [28]	- Ansys fluent turbulent model - Arduino-based test rig	Experimental and numerical evaluation of using 32 innovative cases towards energy optimization in a cavity	In the optimum case, the heat transfer rate enhances by about 22%, showing an economically optimal and environmentally friendly method for building energy optimization.
Soares et al. [29]	- RT28 HC - Micronal DS 5001 X	Experimental study of two types of PCMs to identify which one is better for building applications	The melting and solidification time significantly improved in the presence of RT28, making it a superior option.
Zhang et al. [30]	Organic paraffin wax	Numerical model of PCM behavior under different temperature zones (18–24 °C) to transform excess electricity at off-peak tariff periods into thermal energy	The thermal resistance is the main factor, and with a thermal resistance of 0.4 m2 °C/W, the liquid fraction of the PCM is 0.074.
Heydari et al. [31]	DesignBuilder/DSF, BIPV, PCMs	The numerical/economic study of these configurations in four climates of Iran	The payback period of the case study with photovoltaic glass as the second skin of the building in the southern area of Iran (the warmest case) is optimal (10–11 years).
Soares et al. [32,33]	Free and microencapsulated forms of PCMs	Numerical and experimental study of two types of PCM behavior in the charging and discharging process	The results show the effective parameters in both the charging and discharging process, with the highest efficiency (62%) for Mediterranean climates.
Alghamadi et al. [34]	Solar panels, windows and door change, green walls and roofs, and wall insulation	Energy Plus-based simulation of a case study with different four strategies for energy savings	The result shows a huge decrease in heating and cooling consumption of 28% and 83%, respectively.
Fathi et al. [35]	Machine learning algorithms	Leveraging machine learning algorithms for auditing energy building	They developed a machine learning-based framework for energy building forecasting focusing on five effective parameters.
Olu-Ajayi et al. [36]	LM and DL algorithms	Leveraging machine and deep learning methods for energy building optimization	The results enable constructors to predict the energy usage of a building in the building design stage, and the Deep Neural Network (DNN) has the best performance in energy prediction.
Gervasio et al. [37]	EnergyPlus, RCCTE Portuguese methodology	Numerical energy simulations and life cycle assessment evaluation between embodied energy and operational energy	The data demonstrate that 80% of the building’s total energy is associated with cooling and heating load, and 16 years is needed for the operational energy to overcome the embodied energy.
Bre et al. [38]	EnergyPlus, NSGA-II/PCM-based cementitious composites	Parametric study of PCM-based cementitious composites to achieve optimal performance	By increasing the thermal conductivity of panels, the cooling loads decrease by up to 12.4%.
Santos et al. [39]	EN 15643–3:2012 [40], EN 16309:2014 [41], AHP	Developing a life cycle assessment of educational buildings for health and comfort	Creating a framework based on criteria, sub-criteria, and weighting from the AHP survey for students’ comfort.
Aziz et al. [42]	Heat exchanger with organic PCMs, PV panels	Investigation of cooperation usage of active PCMs and PV panels for energy saving and sustainability	This configuration with a thermal conductivity of 0.56 W/m·K and phase-change temperatures of 42 °C is optimum for building applications, having 6–30 years of payback time.
Shaghaghi et al. [43]	Paraffin (RT25)	Numerically investigate the flow rate of the storage tank and inlet air temperature overcharging and discharging process	The PCM stores cold energy of 1.4 kW and releases it within 2.1 h in the night.

displays a list of research works related to several methods developed and implemented for buildings’ energy efficiency improvement.

PCMs are used in various industries such as transportation, construction, pharmaceuticals, clothing, etc. [44,45]. These materials store both sensible and latent heat. As the ambient temperature increases, they gradually melt at their melting point, and as the temperature decreases, they release the thermal stored energy into the environment and solidify. Figure 1 shows how the state of the PCM changes with temperature and the energy storage capacity difference between its latent and sensible forms. Two primary objectives of using PCMs are to store solar energy (captured during daytime) and release it to the system when the temperature drops (during nighttime) and to shift the heating and cooling load from peak to off-peak periods. Considering the main concepts of PCM performance, some of the cutting-edge studies in this field are reviewed hereunder.

Ghani et al. [47] developed a numerical model for the non-linear heat storage process using the Layered Digital Dynamic Neural (LDDN) method. They considered temperature (25–70 °C) and the mass flow rate of fluid (0.045–0.05 kg/s) in the experimental heat exchanger as training data. The result shows that the model predicted the energy stored and discharged with almost 5% and 7% error, respectively. Kishore et al. [48] numerically investigated the performances of PCMs in five locations in the USA for different climate zones. The results show that using optimum PCMs due to climate conditions, the heat gain and loss are in the range of 3.5–47.2% and 2.8–8.3%, respectively. Saxena et al. [49] investigated phase-change materials (with multiple configurations) combined with bricks as a passive solution for cooling load reduction. These experiments were performed for peak summer conditions, with an ambient temperature above 40 °C. Bricks with single and double layers of phase-change materials had a temperature reduction of 4 °C–9.5 °C when compared to the normal sample. They observed an overall decrease in heat exchange between 16 and 20% for 24 h.

Gholami-Bouzanjani and Farid [50] investigated the performance of active and passive systems of phase-change materials for two specific chambers. One of the chambers had a wall coated with phase-change material, while the other one had an active air heat storage unit combined with a phase-change material. A comparison of the two systems with equal energy storage capacity confirmed a 22% energy saving and 32% more efficient peak load shifting for active systems. Kabdrakhmanova et al. [51] statistically developed formulas to investigate the economic and environmental aspects of using phase-change materials in eight cities with subtropical climates. The result showed that PCM-24 and PCM-27 have an optimum performance, with 12,635 kWh energy saving and 19.9% energy reduction. In addition, this concept is economically optimum for the USA and Brazil, and more environmentally friendly for the USA and China.

Heydari and Khoshkho [52] investigated the use of PCM in three scenarios of the inner, middle, and outer layers of the wall in different climates of Iran. PCMs added to the building’s wall did not significantly reduce energy consumption (less than 2%) due to inefficient absorption/release performance and office usage in the case study. Dehkordi and Afrand [53] studied the effect of using PCMs in buildings in four different cold and hot climate regions. The analysis shows that PCM-24 and PCM-22 are the best options for energy saving of 19–30 kWh/m² in hot locations and 30–55 kWh/m² in cold locations. Mustafa et al. [54] developed energy and momentum equations to investigate the dependency of indoor temperature and PCM selection for energy saving. They considered 21–26 °C as their set points, and the results show that using specific PCMs (SP-21EK), energy demand decreased by 43.2% and 20% for 22 °C and 26 °C, respectively. Marie et al. [55] reviewed the application of fluidized bed reactors in domestic thermochemical energy storage. Different mediums such as molten salts, zeolites, and composites were considered. They compared their performances as an environmentally friendly method for sustainable domestic thermal energy storage applications.

Al-Yasiri et al. [56] experimentally investigated the effect of using PCMs in hot climate buildings to satisfy energy consumption and CO₂ emission reduction. They considered indoor temperature reduction, thermal load reduction, CO₂ emissions saving, and energy cost saving as important parameters. In the case of 2 °C for indoor temperature reduction, the thermal load reduction is 8.71%, and CO₂ emissions saving is 1.35 kg/day. Furthermore, the usage of PCMs is more effective on roofs than on walls. Wang et al. [57] developed two types of adaptive building envelopes (Adaptive Ventilation and Sunlight Regulation Wall, AVSRW, and Adaptive Sunlight Regulation Wall, ASRW) by a combination of transparency shape-stabilized PCMs and reflective film to satisfy sustainable development goals. The output demonstrated that the energy-saving rate based on the AVSRW and ASRW methods was 84.26% and 38.71% in Beijing.

Mahdaoui et al. [58] experimentally studied the use of PCMs in hollow brick for building wall usage. They implemented numerical heat transfer models and concluded that adding PCMs to bricks controls the indoor temperature in a definite range and enhances thermal resistance. Zhou et al. [59] numerically explored the thermal performances of innovative dynamic PCM Trombe walls in comparison to a conventional one. They concluded that the novel wall is 79%/35% more thermally efficient than the static wall without/with PCMs. Refahi et al. [60] investigated the influence of a double-layer PCM wallboard position on energy savings in a four-story residential building in Tehran. The results show that the position of the PCM and insulation layer has a significant result and RT18/insulation/RT28, and RT28/RT18/insulation are optimum configurations for floors 1–3 and floor 4, respectively. With these configurations, 6.6% and 2.8% of heating and cooling energy were saved, respectively. Zhang et al. [61] studied the optimum strategy for energy saving with PCMs in nearly zero-energy buildings. They used mathematical methods and experimental battery-based test rigs to enhance energy efficiency. They reported that a battery with a 3.69 kWh rate enhances the PV self-consumption by almost 11 kW; as a result, 15.6% of costs are reduced.

Most educational buildings in Iran, like this case study, are old, and their energy efficiency is low. Thus, proposing and evaluating sustainable passive solutions to address this challenge is very important. In this vein, we inspire the commercialized PCM type throughout the world to adjust them with our case study for energy savings and decarbonization. Furthermore, some educational buildings have PV panels, and the shading effect influences the performance of this evaluated PCM material. Research for old educational buildings of this kind and climate was not found in the literature.

This research investigates the effect of using PCMs on the external walls and ceilings of the Department of Mechanical Engineering, Shahid Beheshti University, which is located in Tehran, Iran. For this purpose, simulation with DesignBuilder software is used, and the PCM is examined for three locations: (1) on the walls; (2) on the ceiling; and (3) on both the walls and ceiling, resulting in seven scenarios when considering the PV panels’ shading effect and the initial reference scenario. The comparison criteria of these three scenarios are the heating and cooling load of the building, total energy consumption, and the reduction in CO₂ produced by primary non-renewable energy sources. In this study, in addition to comparing the effectiveness of PCMs in different scenarios, the shading effect of photovoltaic (PV) panels, placed on the roof of the building, is also examined to compare how this shading will affect the performance of PCMs.

This article is structured as explained next. First, the materials and methods are described, including the case study (higher education building), the building location, the local climate, the evaluated scenarios (the reference one and seven additional scenarios by considering different configurations of PCM and PV panels), the DesignBuilder models (e.g., their building components and materials, the activity/occupancy related data, the building systems, and the model verification and validation using real measured data). Next, the main obtained results are presented and discussed, namely, the reference scenario, the scenarios with added PCMs, and the scenarios with PV panels (without and with PCMs). These results are related to the yearly predictions for the case study building, the total energy consumption, the heating and cooling loads, and the CO₂ emissions, allowing us to identify the most suitable configurations for energy savings and decarbonization. Finally, the main conclusions of this research study are outlined.

2. Materials and Methods

2.1. Case Study

The case study is the Department of Mechanical Engineering of Shahid Beheshti University (Tehran, Iran), and its southwest side is shown in Figure 2a. This building has two floors for educational use; the ground floor and first floor are 1795.4 and 1784.55 m², respectively. The building is located in Tehran city with a latitude of 35.74 and a longitude of 51.57. The structure of the building is in the form of a symmetrical H, the height of each floor is 3.5 m, and the dimensions and applications of other parts are described in Figure 2b, in which each floor includes 28 rooms, 4 offices, 2 dining rooms, 2 laboratories, and 4 restrooms.

2.2. Climate

Tehran, the capital of Iran, has a latitude of 35.41°N and a longitude of 51.19°E, with an elevation of 1190 m. Based on outdoor design conditions, there are five different regions in Iran: hot and humid, hot and dry, hot and semi-humid, moderate and humid, and moderate and dry. As shown in Figure 3, Tehran is located in the hot and dry region, the largest climatic zone of Iran. This city, with approximately 9 million people, includes about 10% of the total population living in Iran [62].

2.3. Evaluated Scenarios

For this study, as mentioned in

Table 2. List of evaluated scenarios.

Scenario	PCMs?	PV?	Short Description
Reference	No	No	Existing buildings without PCMs or PV panels
Scenario 1	Yes	No	PCMs on the walls
Scenario 2	Yes	No	PCMs on the ceilings
Scenario 3	Yes	No	PCMs on the walls and ceilings
Scenario 4	No	Yes	PV panels on the roof
Scenario 5	Yes	Yes	PCMs on the walls and PV panels on the roof
Scenario 6	Yes	Yes	PCMs on the ceilings and PV panels on the roof
Scenario 7	Yes	Yes	PCMs on the walls–ceilings and PV panels on the roof

, we consider 7 scenarios, in addition to the reference scenario. In the reference scenario, the building is in the initial stage without PCM and PV panels. Scenarios 1–3 are related to the usage of PCMs in walls, ceilings, and walls–ceilings. Scenario 4 is related to the reference building (without PCMs) and equipped with PV panels on the roofs. Furthermore, Scenarios 5–7 are the combinations of Scenarios 1–3 (containing PCMs) with PV panels. The effect of each of the 7 scenarios is carefully investigated on the heating/cooling load and CO₂ emissions.

2.4. DesignBuilder Models

DesignBuilder (DsB) software can simulate buildings from various aspects, such as architecture, heating and cooling systems, building materials, lighting, etc. DsB dynamically simulates building energy consumption, and calculations are performed using the building’s energy parameters accurately with respect to the weather conditions [63]. The pivotal role of this software becomes clearer during the stages of building design and modeling. Implementing minor and major design changes makes the impact on energy consumption or savings for the entire building or individual spaces evident. The modeling engine behind this software is EnergyPlus, developed by the United States Department of Energy [64]. It is one of the most precise software tools available in this field, and DsB v6.1.2 software [63] is used to simulate the building’s thermal behavior and energy efficiency.

Figure 4 illustrates the DsB model of the evaluated building (case study). In this section, we examine the effect of shading on the three scenarios while considering the need to carry out the correct cycle of energy absorption/release by PCMs (Scenarios 5–7). According to Figure 4b, it was simulated in 6 rows of photovoltaic panels on the building’s roof. These panels were modeled with a constant efficiency of 15%, covering a total area of 558 m² and a depth of 0.025 m. The simulation was performed using the EnergyPlus computational engine within the DesignBuilder software model.

Table 3. Technical specifications of the simulated PV panels.

Conductivity (W/m·K)	0.23
Specific Heat (J/kg·K)	1000.00
Density (kg/m3)	1100.00
Depth (m)	0.025

shows other technical specifications of the simulated PV panels.

2.4.1. Building Components and Materials

Figure 5 displays a schematic of the external walls and ceiling–roof of the building for the reference scenario. The external walls (Figure 5a) contain an external brickwork layer 30 cm thick and an inner gypsum insulating plaster (3 cm thick), having a thermal transmittance (U-value) of 1.44 W/m²·K. The ceiling–roof slab structure is made of cast reinforced concrete (15 cm), having an outer upper layer of asphalt (2.5 cm) and an inner lower layer of gypsum insulating plaster (3 cm), and their U-value is 2.10 W/m²·K.

Notice that the U-value of the original external walls agrees with the maximum value prescribed by the national buildings’ thermal regulation of Iran, i.e., 2.0 W/m²·K. However, the existing ceiling–roof slab U-value largely exceeds the maximum allowed (1.4 W/m²·K).

Table 4. Building components and materials and their properties.

Component	Material	t (m)	k (W/m·K)	Cp (J/kg·K)	ρ (kg/m3)	U-Value (W/m2·K)
External walls	Gypsum Insulating Plaster	0.030	0.18	1000	600	1.44
	Outer Brickwork	0.300	0.84	800	1700
Internal walls	Gypsum Plastering	0.015	0.40	1000	1000	2.01
	Inner Brickwork	0.100	0.62	800	1700
	Gypsum Plastering	0.015	0.40	1000	1000
Roof/Ceilings	Asphalt	0.025	0.70	1000	2100	2.10
	Cast Concrete	0.150	1.13	1000	2000
	Gypsum Insulating Plaster	0.030	0.18	1000	600
Upper floor	Gypsum Plastering	0.015	0.40	1000	1000	1.81
	Cast Concrete	0.150	1.13	1000	2000
	Mortar	0.020	0.72	840	1760
	Ceramic Tiles	0.005	0.80	850	1700
Ground floor	Mortar	0.100	0.72	840	1760	2.81
	Ceramic Tiles	0.005	0.80	850	1700
Window glazing	Generic Clear	0.003	0.90	-	-	1.96
	Air	0.013	-	-	-
	Generic Clear	0.003	0.90	-	-
Window frame	PVC	0.020	0.17	900	1390	3.47

shows the main building components and the materials used, as well as their properties, including their thickness (t), thermal conductivity (k), specific heat capacity (Cp), density (ρ), and overall coefficient of heat transfer (U-value). Moreover, the solar heat gain coefficient (SHGC) or solar factor of the window glazing system is 0.837.

Notice that the higher education building, used in this research work as a reference scenario, does not have any PCMs or PV panels. However, in the second and fourth sets of simulations in this research (Scenarios 1–3 and 5–7), a macro-encapsulated layered PCM (BioPCM M182/Q23) [65] has been used within the walls, as illustrated in Figure 6, and within the ceiling–roof slab, as illustrated in Figure 7. Moreover,

Table 5. Technical specifications of BioPCM M182/Q23 [65].

Parameter	Value
k (W/m·K)	0.200
Cp (J/kg·K)	1970
$\rho$ (kg/m3)	235
t (m)	0.0742
Melting point (°C)	23.0
Latent heat (J/g)	230
Energy storage capacity (kJ/m2)	825

shows the most relevant physical properties of these PCMs, including thermal conductivity (k), specific heat (Cp), density (ρ), thickness (t), melting point, latent heat (J/g), and energy storage capacity (kJ/m²). As displayed in Figure 6 and Figure 7, now, both external walls’ and ceiling-roof slabs’ U-values (i.e., 0.939 and 1.182 W/m²·K, respectively) are within the limits prescribed by the Iranian code of practice (i.e., 2.0 and 1.4 W/m²·K, respectively).

2.4.2. Activity/Occupancy-Related Data

The attendance schedule of people inside the buildings is considered from 7 am to 8 pm. The main activity-related template values assumed for this building DsB model are listed in

Table 6. Activity-related template values assumed in the DsB models.

Parameter	Value
Occupancy density (people/m2)	0.2034
Physical activity	Standing/walking
Metabolic factor	0.9
CO2 generation rate (m3/s-W)	3.82 × 10−8
Power density (W/m2)	4.74
Target illuminance (Lux)	300

.

2.4.3. Building Systems

The time of turning on/off the heating/cooling equipment is considered from 5 a.m. to 7 p.m.

Table 7. System specifications of the adopted HVAC DesignBuilder model.

HVAC 1 system template	Fan Coil Unit (4-Pipe), Air-Cooled Chiller
Heating system seasonal CoP 2	0.85
Cooling system seasonal EER 3	1.80
Domestic Hot Water (DHW) CoP 2	0.85

¹ HVAC—Heating, Ventilation, and Air Conditioning; ² CoP—Coefficient of Performance; ³ EER—Energy Efficiency Ratio.

shows the characteristics of the cooling and heating equipment of the building. The comfortable temperatures for the cold and hot seasons are 20 °C and 23 °C, respectively. All parameters and coefficients required for simulation in the software are calculated based on ASHRAE standards, which are demonstrated in Table 7 [66].

2.4.4. Model Accuracy Verification and Validation

The monthly consumption of energy carriers is available on the online website of the Gas and Electricity Distribution Company [67,68]. The main inputs of the implemented reference DsB model were previously provided within the preceding subsections. Regarding the building components and materials (Section 2.4.1), the original external walls are made of an outer brickwork layer (30 cm thick) and a gypsum insulating plaster (3 cm thick), exhibiting a thermal transmittance value of 1.44 W/m²·K. The modeled activity/occupancy-related data (Section 2.4.2) are typical for a higher educational building, which is occupied between 7 a.m. and 8 p.m. Regarding the building systems (Section 2.4.3), it can be highlighted that the working schedule for the heating/cooling equipment is between 5 a.m. and 7 p.m. Additionally, the DsB calculation options were set to 30 min for the computational time step, while the temperature control was set to “Air temperature”.

To verify and validate the reference DsB model [69], the monthly output data from the simulation were compared with the real electricity and gas consumption of the building during one year, as illustrated in Figure 8. To prove the accuracy of electricity and natural gas predictions, the RMSE (root-mean-square error) was computed (only 1.19 MWh and 1.16 MWh, respectively), showing a very good agreement between the numerical simulation results and measured data.

2.5. Governing Equations

In this section, the related equations for numerical studies of PCMs are demonstrated. Sensible heat refers to the amount of thermal energy stored by increasing the temperature of a solid or liquid using its heat capacity, as derived from Equation (1) [52].

$$Q={\int }_{T_{initial}}^{T_{final}}\left(m\times C_p\times dT\right)$$

(1)

where $Q$ is the thermal energy stored/released in the format of sensible heat (kJ); $T_{initial}$ is the onset temperature (°C); $T_{final}$ is the after-process temperature (°C); and $C_p$ is the specific heat (kJ/kg°C). Differential equations, e.g., Equation (2), are used to solve phase-change problems in real-world situations. In relation 2, one transient and two steady terms represent the distribution in two directions, and the last term applies the effect of the phase change [70].

$${\displaystyle \frac{\partial H}{\partial T}}={\displaystyle \frac{1}{r}}{\displaystyle \frac{\partial }{\partial r}}\left(\alpha r{\displaystyle \frac{\partial h}{\partial r}}\right)+{\displaystyle \frac{\partial }{\partial z}}\left(\alpha {\displaystyle \frac{\partial h}{\partial z}}\right)-\rho l_f{\displaystyle \frac{\partial f}{\partial t}}$$

(2)

In the equation above, the term $H$ is defined as follows:

$$\begin{array}{c}H\left(T\right)=h\left(T\right)+{\rho }_{S}f{l}_f\\ h\left(T\right)={\int }_{T_{initial}}^{T_{final}}\left(\rho \times C_p\times dT\right)\end{array}$$

(3)

To solve this equation, an equation for the moving boundary is needed, which is defined as Equation (4) as follows:

$${\displaystyle \frac{dr}{dt}}={\displaystyle \frac{k}{\rho l}}{\displaystyle \frac{dT}{dx}}$$

(4)

The set of Equations (1)–(4) can be numerically solved with different boundary and initial conditions for PCMs. In this paper, the equations are solved using a finite difference method in DesignBuilder (DsB) software v 6.1.

3. Results and Discussion

In this section, first, the obtained results for the reference scenario (original existing higher education building) are displayed. The effect of using PCMs in the walls and ceilings (Scenarios 1–3) will be analyzed in a second subsection, while the use of PV panels on the roof of the modeled building (Scenarios 4–7) will be assessed within the third subsection.

3.1. Reference Scenario

The predicted total energy consumption, heating and cooling load, and the amount of CO₂ emissions from the building are displayed in

Table 8. The obtained annual results for the reference scenario.

Parameter	Value
Total Energy Consumption (MWh/year)	557.82
Heating Load (MWh/year)	168.19
Cooling Load (MWh/year)	222.57
CO2 Emissions (Ton/year)	267.66

. As expected, the higher value is for the total energy consumption (557.82 MWh/year). Moreover, the cooling demand (222.57 MWh/year) is considerably bigger than the heating load (168.19 MWh/year). This could be related to the hot climate and the high SHGC of the window glazed system (0.837), having no shading devices and, thus, there is a consequent big solar heat gain across the windows. Regarding the predicted CO₂ emissions, they are about 267.66 tons/year.

3.2. Adding Phase-Change Materials

Table 9. The predicted annual values when adding PCMs to the building envelope.

Parameter	Scen. 1	Scen. 2	Scen. 3
Total Energy Consumption (MWh/year)	535.75	513.16	488.87
(Diff. to Ref. Scenario)	(−4.0%)	(−8.0%)	(−12.4%)
Heating Load (MWh/year)	153.27	143.48	128.06
(Diff. to Ref. Scenario)	(−8.9%)	(−14.7%)	(−23.9%)
Cooling Load (MWh/year)	216.8	202.62	196.13
(Diff. to Ref. Scenario)	(−2.6%)	(−9.0%)	(−11.9%)
CO2 Emissions (Ton/year)	259.92	250.93	242.67
(Diff. to Ref. Scenario)	(−2.9%)	(−6.3%)	(−9.3%)

displays the yearly predictions regarding the impact of using PCMs for three different scenarios, namely, when installed on the walls (Scenario 1), used on the ceilings (Scenario 2), and a combination of both ceilings and walls (Scenario 3).

Table 9 shows also the differences in relation to the reference scenario (Table 8), regarding total energy consumption, heating/cooling load, and CO₂ emissions, for each one of the first three considered scenarios.

As displayed in this table, the use of PCMs has a greater effect on reducing the heating load than the cooling demand, and also the placement of this material in the ceiling layers (Scenario 2) is more effective than the wall (Scenario 1). For instance, when used on the ceiling, the heating and cooling load reduction is 14.7% and 9.0%, respectively, while these values for PCM usage on the wall are only 8.9% and 2.6%, respectively. The ceiling has better potential for thermal energy exchange since this slab is directly exposed to solar radiation on the upper surface, whereas walls are exposed to solar radiation from different sides during the day, and some of them are shaded. Moreover, when the indoor air gets warmer inside a compartment, it tends to go up, reaching the ceiling where the PCMs will more easily absorb part of this heat, in comparison to the lateral walls.

The simultaneous PCM usage on both ceiling and walls (Scenario 3) reduces the heating load by 23.9% and the cooling load by 11.9%. Also, the reduction in CO₂ emissions from the main sources of energy production is approximately 243 tons per year (9.3% reduction).

3.3. Adding Solar PV Panels

3.3.1. Building without PCMs

Table 10. The predicted annual values when adding PV panels to the building without using PCMs (Scenario 4).

Parameter	Value
Total Energy Consumption (MWh/year)	559.73
(Diff. to Ref. Scenario)	(+0.3%)
Heating Load (MWh/year)	189.31
(Diff. to Ref. Scenario)	(+12.6%)
Cooling Load (MWh/year)	203.36
(Diff. to Ref. Scenario)	(−8.6%)
CO2 Emissions (Ton/year)	157.39
(Diff. to Ref. Scenario)	(−41.2%)
Energy Generation (MWh/year)	169.29

displays the predicted annual values when adding solar PV panels (Scenario 4) to the roof. By comparing Table 10 and Table 8 (reference scenario), it can be seen that the shading of the PV panels placed on the roof of the building has increased the heating load consumption by 12.6%, while the cooling load consumption decreased by 8.6%, compared to the reference scenario. Considering that the building is located in Tehran city, having both cold and hot periods, these results show a small change in the effect of shading panels on the total energy consumption (only +0.3%), but in cities where the energy requirements are different, they could have a far greater impact. Furthermore, the PV panels generate 169.29 MWh/year of energy, leading to a significant 41.2% reduction in CO₂ emissions from non-renewable primary energy sources.

3.3.2. Building with PV Panels and PCMs

Table 11. The obtained annual values for the building with PV panels and PCMs.

Parameter	Scen. 5	Scen. 6	Scen. 7
Total Energy Consumption (MWh/year)	535.99	514.74	489.94
(Diff. to Ref. Scenario)	(−3.9%)	(−7.7%)	(−12.2%)
Heating Load (MWh/year)	174.44	157.09	141.67
(Diff. to Ref. Scenario)	(+3.7%)	(−6.6%)	(−15.8%)
Cooling Load (MWh/year)	196.87	190.59	183.59
(Diff. to Ref. Scenario)	(−11.5%)	(−14.4%)	(−17.5%)
CO2 Emissions (Ton/year)	149.23	143.61	135.03
(Diff. to Ref. Scenario)	(−44.2%)	(−46.3%)	(−49.6%)

displays the obtained annual values when adding solar PV panels to the roof and PCMs to the walls (Scenario 5), to the ceilings (Scenario 6), and to both the walls and ceilings (Scenario 7). By comparing these values with the reference scenario (Table 8), the only value that increases (+3.7%) is the heating load for Scenario 5 (PCMs on the walls). This increase could be due to the shading effect provided by the PV panels on the roof, as previously seen in Scenario 4 (PV panels without PCMs), but there was an even higher heating demand increase (+12.6%) given the absence of PCMs.

The total energy consumption displayed in Table 11 is quite similar to the previous ones displayed in Table 9, showing again that the PCM effect is much more relevant than the PV panel shading effect (Table 10).

Again, similar to Table 10 (PV panels without PCMs), the major differences are related to CO₂ emissions reduction given the renewable energy (electricity) generated by the PV panels, ranging from −42.2% to −49.6% for Scenario 5 and Scenario 7, respectively (Table 11).

To have a better overview of the obtained results, Figure 9 displays a graphical plot for each one of the evaluated parameters, with and without PV panels. Starting with the heating load (Figure 9a), their increase is visible when there are PV panels, mainly due to their shading effect, for all PCMs scenarios, including when there are no PCMs. Now, looking at the cooling load (Figure 9b), as expected, the opposite happens, i.e., there is a decrease when the PV panels are modeled in the roof of the building since their shading can lower the temperature of the building’s indoor environment, and this way requires less cooling load in all four scenarios.

Regarding the total energy consumption (Figure 9c), their differences, before and after installing PV panels, are not relevant for the PCM scenarios evaluated, as well as without using PCMs. However, as illustrated in Figure 9d, there is a positive effect on the CO₂ emissions for all assessed scenarios. In fact, the PV panels allow for the production of a high amount of electricity (169.29 MWh/year), as previously displayed in Table 10), reducing the need for electricity generation using fossil fuels, which significantly reduces the CO₂ emissions in all evaluated scenarios. As also previously displayed in Table 11, the maximum CO₂ emissions reduction (−49.6%) occurs for Scenario 7 (PV panels with PCMs on ceilings and walls).

4. Conclusions

This article assessed the relevance of using PCMs and/or PV solar panels in a higher education building case study (Department of Mechanical Engineering of Shahid Beheshti University, Tehran, Iran) for energy savings and decarbonization. The numerical simulations were performed by making use of DesignBuilder software v 6.1, and the developed model was validated by comparison to in situ annual measurements. The evaluated PCM was BioPCM M182/Q23, while the PV solar panels have a constant efficiency of 15% and a total area of 558 m².

A total of eight different scenarios were evaluated. First, the reference scenario was assessed and validated (original higher education building without PCMs or PV solar panels). Next, the use of a PCM layer in three different locations of the building envelope was modeled: (1) walls; (2) ceilings; and (3) both walls and ceilings. After, a set of PV solar panels were modeled on the original building roof (Scenario 4, without PCMs). Finally, the second set of Scenarios 5–7 is similar to the previous set (Scenarios 1–3) but now simultaneously considers the existence of PV solar panels on the roof of the building.

4.1. Detailed and In-Depth Discussion

The results showed that Scenario 3, which involved using PCMs on both the walls and ceilings, was the best-evaluated solution among the first three scenarios, where the heating and cooling loads were reduced by around 24% and 12%, respectively, compared to the initial condition of the building (reference scenario). Additionally, the use of PCMs on the ceilings (Scenario 2) is significantly more efficient when compared to their use on walls (Scenario 3).

By adding PV panels to the case study, without PCMs (Scenario 4), compared to the reference scenario, the heating load increased by about 12.6%, and the cooling load was reduced by 8.6%, but the overall energy consumption of the building did not change significantly. However, given the electricity generated by the PV panels (169.29 MWh/year), there was a significant reduction in CO₂ emissions (around 41% less).

Regarding the simultaneous use of PCMs and PV solar panels (Scenarios 5–7), the shading effect and the electricity provided by these panels will strongly influence the main evaluated values. Again, the major impact of using PV panels is related to CO₂ emissions reduction, which reached a maximum value of almost 50%, i.e., a decrease of 132.63 tons/year for Scenario 7. Another interesting conclusion is that the total energy consumption is very similar with and without PV solar panels when using PCMs since the PV shading on the roof simultaneously increases the heating demand and decreases the cooling load.

4.2. Summary of Conclusions

PCMs are highly effective in shifting energy usage peaks and reducing overall energy consumption in buildings. However, it is important to consider that these benefits may not be practical for buildings that are not in full-time use, as the release of stored energy may not align with the building’s occupancy schedule. For educational and part-time use buildings, such as the university buildings studied in this research, it is crucial to consider the impact of solar radiation on the building envelope. This study found that PCMs in the roof and walls of part-time-use buildings, where capturing solar heat is key, can be very effective. However, obstacles that block the sun’s heat can significantly impact the performance of PCMs. This could be even more relevant for buildings surrounded by tall structures that obstruct solar energy. While rooftop solar panels are a well-known method for electricity generation and carbon dioxide reduction, the use of PCMs in such scenarios may be significantly affected regarding their related energy consumption reduction by reducing their efficiency.

4.3. Research Simplifications and Future Work

Notice that this research work has several simplifications, as briefly described next. Regarding the PV panels, their efficiency was assumed to be constant, neglecting the variations related to dust accumulation, temperature, and other influencing parameters (e.g., long-term degradation). Regarding the PCM-related simulations, their long-term degradation was also neglected. Moreover, some properties of the simulated PCMs, such as specific heat ($C_p$), depend on temperature variation, as these material’s properties update automatically, are evaluated within the DesignBuilder model, and are calculated by the EnergyPlus computation engine.

Future research could explore appropriate materials for innovative PCMs or find the best performance scenarios using optimization algorithms and artificial intelligence. Another interesting future research work is to perform a related life cycle assessment (LCA), as well as a cost–benefit analysis.