Approaching a Nearly Zero Energy Building Integrated with PCM by Optimization of Energy Sources

Abstract

In recent years, population growth, the enhancement of carbon emissions generation, and higher energy consumption have caused the movement to nearly zero-energy buildings. Additionally, the various strategies, phase change materials (PCMs) are suitable for reducing the energy consumption of a building. The focus of this study is to investigate the results of three scenarios that explore all the effective parameters for selecting a suitable Phase Change Material (PCM) for hot climate conditions in Saudi Arabia. The first scenario worked on choosing the best phase change material based on the climatic conditions and the selected area. To complete the optimization, the best thickness and placement of the two-layer phase change material were investigated in the second and third scenarios. The results indicated that optimized building using PCM 29 with 50 mm thickness reduced the energy consumption and carbon dioxide production by 20.72% and 21.05%, respectively. Furthermore, the outcomes of the study on two-layer phase change materials with different arrangements illustrated that the most proper location of PCMs caused 255.38 MWh of electricity consumption and 155.71 × 10³ kg of carbon dioxide production. Finally, as a novel integration, the results of using one-layer and two-layer PCM were added to the HOMER software to find the optimal hybrid energy systems. The findings showed that by integrating photovoltaic panels, diesel generation, batteries, and the grid, the cost of energy reached USD 0.162. Additionally, the grid purchase by using one-layer and two-layer phase change material was decreased by 21.2% and 24.3% compared to the base case.

1. Introduction

In recent years, high energy consumption and carbon emissions of buildings have been an important challenge around the world [1]. According to the prediction of the International Energy Agency (IEA), energy consumption can increase by 50% [2]. In this regard, achieving the goal of zero/nearly zero energy buildings can be an effective means [3]. A nearly zero energy building is a building that has lower energy utilization and supplies energy demands by utilizing various technologies [4]. Renewable energy technologies and energy-efficient measures such as effective insulation, efficient ventilation, proper windows, and energy storage means are the methods used for reducing the building’s energy utilization [5,6].

Thermal energy storage methods, such as phase change material (PCM), can improve the efficiency of renewable energy systems and reduce the building’s required energy [7]. Phase change materials utilize latent heat during the phase change to control the temperature during the day and night periods [8,9]. Phase change materials are categorized into three groups: organic, inorganic, and eutectic. Different properties, such as environmental, kinetic, economic, thermophysical, and chemical, are important factors that vary for each PCM [10]. Phase change material can be used in various sections of buildings and renewable systems integrated with buildings. Integration of PCMs with walls can store energy 5–10 times more than walls, reduce thermal fluctuations, and generate thermal comfort. Encapsulation, direct incorporation, stabilization, and impregnation are the techniques utilized for PCM integration with the wall [11].

Phase change materials are used for the building’s thermal comfort, heating, and cooling load reduction [12]. By the encapsulation method of generating wall-integrated PCM, Younsi and Naji [13] studied the performance of utilizing a PCM-mortar layer integrated with the building’s wall. Melting temperature, wallboard thickness, and PCM’s mass fraction were analyzed. The findings showed that the best mass fraction of PCM was 20–30% for setting the maximum thermal comfort and thermal storage. Furthermore, the incorporation of PCM reduced the indoor temperature during summer and shifted the cooling loads’ peak. To reduce the thermal comfort requirement in the buildings, Khan et al. [14] investigated the influence of the PCM’s position and integration role on its performance. The results indicated that by utilizing PCM, the fluctuations of indoor temperature were decreased. Integration of PCM with the building’s instruction can decrease the amount of CO₂. Yang et al. [15] simulated a building that utilized PCM blocks in the walls. The CO₂ generation and required load for electricity, lighting, cooling, and hot water were investigated. The outcomes indicated that the utilization of PCM blocks reduced the needed load. In addition, the CO₂ generation was decreased by about 160 kg annually. Various thicknesses and types of PCMs affect their performance in a building. In this regard, Anter et al. [16] conducted a study under the climate conditions of Egypt. Lauric acid, RT-44HC, RT-35HC, RT-42, RT-31, and RT-27 with 8, 6, 4, 3, 2, and 1 cm thickness were tested in various locations of the wall. The results displayed that RT-35HC was the best and decreased the internal heat flux. The utilization of the efficient PCM located in the 1.5 cm of outer and inner walls caused a 66% decrease in overall energy gain during summer. In the simulation by Design Builder, which utilized PCM in the building’s envelope, Jahangir and Alimohamadi [17] investigated the thermal comfort condition of a building. Different PCMs were selected and located in the building envelope to achieve higher performance. The results indicated that the Bio PCM in the inner and outer layers decreased the cooling load by about 246.15 and 154 Wh/m², respectively. Also, the outcomes displayed that the best thickness of PCM can be effective in decreasing the building’s required load.

As a means of supplying the required loads of buildings, hybrid energy systems can be utilized. Hybrid systems can be helpful for moving toward a nearly zero energy building or zero-energy buildings. The efficient design of hybrid solar energy systems and energy management strategies is important for enhancing the performance of zero-energy buildings. Different systems, such as photovoltaic panels (PVs), diesel generators (DGs), batteries (Bat), grids, and wind turbines (WT), are used as the hybrid energy system’s sources. In the study of Zhang et al. [18], PV/Bat and PV/Bat/DG hybrid systems were utilized for supplying the electricity for the building. Various optimization methods were applied to the system. The findings confirmed that the meta-heuristic search algorithm was effective. For higher reliability, reducing the size of the system, and lower cost in zero-energy buildings, Al-Odat et al. [19] researched the grid/PV/wind hybrid system for providing a factory’s electricity load under the climate conditions of Kuwait. The optimization was performed by the HOMER software. The outcomes demonstrated that the cost of generated electricity was 0.082 USD/kWh; however, the conventional cost of electricity in the mentioned region was 0.12 USD/kWh. Boruah and Chandel [20] studied a hybrid system consisting of PV/grid/Bat under the weather conditions of India. PVsyst, Solar Labs, and HOMER software were used to plan and calculate energy production for the system. The findings illustrated that the PV plant and battery with 200 kW and 250 kWh, respectively, had the best performance. The payback period of the mentioned system was 6.15 years, and the cost of produced energy was 4.21 INR/kWh.

To decrease the required energy and carbon emissions production, Al Huneidi et al. [21] proposed a PV system on the roof of a building. Simulation of the designed system in HOMER software showed that the suggested system was more economically and environmentally efficient and could decrease the building’s energy demand. By utilizing various renewable energy systems and optimizing design methods with the integration of HOMER and Design Builder software, a net-zero energy building can be achieved. Elenga et al. [22] optimized a building under the climate conditions of Congo, Africa. The outcome of the simulation demonstrated that a 39.15% energy saving was obtained. In addition, the utilized renewable energy system provided all the required energy load and saved 3341.84 kgCO₂eq/year. To decrease the usage of fossil fuels, Sady et al. [23] modeled a building coupled with underground air ducts and a Trombe wall. The optimized hybrid system, including a PV, an inverter, a battery, and a wind turbine, was analyzed. The findings illustrated that 44% and 39% reductions were achieved in heating and cooling loads, respectively. Also, the applied passive methods caused a 12.5% decrement in net present cost.

Recently, the importance of nearly zero/zero energy buildings has been remarked. In this regard, different methods were used for reaching zero-energy buildings. In this study, a generic building was simulated in Design Builder software under the climate conditions of a hot city in Saudi Arabia. The PCM layer is added to the construction besides the other energy reduction methods, which are discussed in the different articles. Phase change material addition to the building causes more thermal comfort and prevents thermal fluctuations. While several studies worked on the utilization of PCMs in buildings, no investigation has selected the best PCM based on the weather condition, optimized the thickness and multi-layer configuration of PCMs, and used HOMER software to integrate the building with PCM into an optimized hybrid energy system. The main novelty of this study is its specific scenarios in which the various types, thicknesses, and locations of PCM are discussed, and the results are added to HOMER software as input data for the optimization of energy sources. Specifically, in the recent investigation, the thickness and location of one-layer and two-layer PCM are analyzed, and the best arrangement of layers is selected. Following the goal of reaching nearly zero-energy buildings, the optimized building is coupled with the hybrid energy systems by HOMER software. This novel integration obtained the best renewable system based on the cost of energy (COE) value. Finally, the economic and environmental aspects are considered, which are helpful for the selection of the best systems for future works.

2. Methodology

This study aims to analyze the energy consumption of a prototypical large-scale building designed for hot climate conditions using Design Builder simulation software V6. The energy consumption of the primary simulated building was optimized based on the orientation, windows, construction materials, and insulation. Then, for lower energy utilization, lower emission production, and thermal comfort, the PCM layer was added to the wall and roof construction. Different PCMs with various melting points, thicknesses, and locations in the wall were analyzed. For achieving nearly zero-energy buildings, renewable sources were considered and analyzed in the HOMER software Pro 3.18.4. Finally, the system was investigated economically and environmentally. Figure 1 demonstrates a general flowchart of the work procedure.

2.1. Geographical and Climatic Conditions

The simulation was performed under the climate conditions of a hot city in Saudi Arabia, with a high global horizontal irradiation (GHI) capability [24]. The longitude and latitude of the selected town are 47.1167° E and 17.4850° N, respectively. The harsh desert climate of Saudi Arabia, with high temperatures and solar radiation, makes it an ideal location to study the effect of PCM in evaluations and optimizations [25,26]. Figure 2a,b illustrates the ambient temperature and solar radiation of the simulated building’s location during a year. The average ambient temperature and GHI are 28.3 °C and 6.501 kWh/m².day.

2.2. Design Builder Software

Design Builder software is utilized for building simulations in a user-friendly environment and high quality. The advanced Design Builder tools reduce the modeling time and enhance efficiency. Various analyses, such as design optimization, cost, daylighting, HVAC, energy and comfort, CFD, and different reports, are applicable [28].

2.3. Building Components

The selected location was added to the Design Builder software, and the orientation of the building was considered in the first step of design. A one-floor generic sample building with an area of 1403 m² is shown in Figure 3.

The building consists of walls, partitions, a floor, a roof, semi-exposed walls, and windows. The specification of each part of the building is given in

Table 1. Summary of materials used for the design of the simulated building model.

	Materials	Thickness	Conductivity (W/m-K)	Specific Heat (J/kg-K)	Density (kg/m3)
External Wall	Granite	0.0200	2.8000	1000.00	2600.00
	Cement/Plaster/Mortar	0.0300	0.7200	840.00	1860.00
	Concrete Block	0.2000	0.5100	1000.00	1400.00
	PUR Polyurethane Board	0.0742	0.0280	1590.00	35.00
	Concrete Block	0.1000	0.5100	1000.00	1400.00
	Gypsum Plastering	0.0200	0.4000	1000.00	1000.00
	Paint Plasterboard	0.0100	0.2100	1000.00	700.00
Flat Roof	Roof Tile	0.0300	0.8400	800.00	1900.00
	Cement/Plaster/Mortar	0.0200	0.7200	840.00	1760.00
	Foam-polyurethane	0.0030	0.0280	1470.00	30.00
	Cement/Plaster/Mortar	0.0100	0.7200	840.00	1760.00
	Cast Concrete	0.0500	1.1300	1000.00	2000.00
	PUR Polyurethane Board	0.0742	0.0280	1590.00	35.00
	Aerated Concrete Slab	0.200	0.1600	840.00	500.00
	Gypsum Plastering	0.0100	0.4000	1000.00	1000.00
Internal Partitions	Gypsum Plasterboard	0.0250	0.2500	1000.00	900.00
	Air gap 10 mm	0.1000	0.3000	1000.00	1000.00
	Gypsum Plasterboard	0.0250	0.2500	1000.00	900.00
Ground Floor	Urea Formaldehyde Foam	0.1327	0.0400	1400.00	10.00
	Cast Concrete	0.1000	1.1300	1000.00	2000.00
	Floor/Roof Screed	0.0700	0.4100	840.00	1200.00
	Timber Flooring	0.0300	0.1400	1200.00	650.00

. The building is a one-floor, multi-zone building consisting of nine zones. The base zone, which is located in the center of the building, has an area of about 707 m². This large building is not intended to replicate a typical residential structure in the region but rather to serve as a representative model for analyzing PCM and energy integration strategies in large, hot-climate buildings. The simulation focuses on evaluating performance under controlled conditions to assess energy savings, costs, and environmental impacts. The length and width of the building are 49.3 m and 31.5 m, respectively. The external windows were double-glazed Clr 6 mm/13 mm air. Furthermore, the HVAC system of the building consists of a 4-pipe fan coil unit with heating and cooling seasonal COPs of 0.85 and 1.80, respectively.

2.4. Energy Saving Scenarios

The first aim of the study is to optimize the building by construction materials, orientation, proper windows in terms of number and material, and insulation, which are discussed in the previous section. In the second step of the study, a PCM layer is added to the wall and roof construction. In this regard, various PCM thicknesses and locations for adding PCM to the building’s wall and roof are investigated. The obtained results are compared with the base case, which consists of none of the first optimization methods. Then, the nearest outcome to a nearly zero energy building was transferred to HOMER software for the next evaluations and optimizations for adding renewable energy sources to the building.

• First Scenario

Phase change materials are capable of storing energy and releasing it during the phase change process in a specific temperature range [29]. Utilizing PCMs integrated into the various parts of buildings, including indoor coatings, ceilings, and walls, can reduce the transferred heat to the indoor environment of a building and decrease the temperature fluctuations [30]. Phase change materials can be incorporated into a building by methods like form-stable PCM composites, shape-stabilized PCMs, microencapsulation, macroencapsulation, immersion, and direct incorporation. Based on the selected location, which is located in a hot weather condition with a high GHI amount, PCMs with different phase change temperatures are selected. The selected PCMs should have proper melting points that can work with their maximum capacity. Moreover, their latent heat, specific heat, density, and conductivity are other important criteria for selection. The material type, presence of any added components, and purity are factors that affect the latent heat of PCM [31]. The utilized PCMs with their properties are demonstrated in

Table 2. Thermal properties of the utilized PCMs in the test building.

Materials	Latent Heat (J/kg)	Specific Heat (J/kg·K)	Density (kg/m3)	Conductivity (W/m·K)	Melting Point (°C)
InfinitRPCM21C	200,000.00	3140.00	929.00	0.8150	20–22
InfinitRPCM23C	200,000.00	3140.00	929.00	0.8150	22–24
InfinitRPCM25C	200,000.00	3140.00	929.00	0.8150	24–26
InfinitRPCM29C	200,000.00	3140.00	929.00	0.8150	28–30
PCM 32C	200,000.00	3150.00	8750.00	0.9120	30–32

. The goal of the first scenario is to select the best PCM for the mentioned building. In this regard, the same thickness was considered, and the PCM layer was located in the middle layer of the wall and roof construction.

• Second Scenario

In the second step of the simulation, an analysis was performed on the different thicknesses of the PCM layer in the wall and roof construction. Neither the low nor the high thickness of the PCM layer will work effectively, so finding the best thickness was necessary. For all the selected PCMs, various thicknesses, including 10, 30, 50, 70, 90, 110, 130, 150, and 170 mm, were considered. The analysis was performed to choose the best thickness of the selected PCM and compare various PCM thicknesses economically.

• Third Scenario

After choosing the proper PCM and thickness for the simulated building, the performance of the two-layer PCM was investigated. The general analyses showed that adding a PCM layer to the roof of a building had a greater effect than the walls with a PCM layer. In this regard, the place of PCM layers among the roof materials was analyzed. The third scenario is divided into two sections. The first section is when the investigation was for two same PCM layers. The second section of the third scenario is when two different layers of PCM are considered. Phase change materials with different phase change temperatures were located in various roof layers.

Table 3. Placement arrangements of single- and two-layer PCMs in wall and roof constructions, used to compare their impact on building electricity consumption and CO₂ emissions.

Model	Utilized PCMs in Wall/Outer Roof Layer/Inner Roof Layer
A	21/21/21
B	23/23/23
C	25/25/25
D	29/29/29
E	29/29/21
F	29/29/23
G	29/29/25
H	29/25/21
I	29/25/23
J	29/23/21
K	29/32/29
L	29/32/25
M	29/32/23
N	29/32/21

demonstrates all the considered arrangements for locating PCM layers in the wall and roof construction. Notably, the phase change material with the higher melting point should be in the outer layer of the constructed roof due to its higher melting point. By utilizing the PCM with a lower melting point in the outer layer of the building, the transferred heat is wasted to increase the temperature of the melted PCM after the phase change process. As a result, the lower heat is transferred to the PCM with a higher melting point at the inner layer of the building.

2.5. Mathematical Equations of Design Builder Software

The following equations were utilized for determining the key parameters for the calculation of essential values of research. The recent simulation used the EnergyPlus engine and the finite difference solution algorithm as the calculation method. For the PCM layer utilized in the building’s construction, the following equations were utilized [32].

$$\mathrm{C}\rho ∆\mathrm{x}{\displaystyle \frac{{\mathrm{T}}_{\mathrm{j}}^{\mathrm{i}+1}-{\mathrm{T}}_{\mathrm{j}}^{\mathrm{i}}}{∆\mathrm{t}}}={\mathrm{K}}_{\mathrm{w}}{\displaystyle \frac{{\mathrm{T}}_{\mathrm{j}+1}^{\mathrm{i}+1}-{\mathrm{T}}_{\mathrm{j}}^{\mathrm{i}+1}}{∆\mathrm{x}}}+{\mathrm{K}}_{\mathrm{E}}{\displaystyle \frac{{\mathrm{T}}_{\mathrm{j}-1}^{\mathrm{i}+1}-{\mathrm{T}}_{\mathrm{j}}^{\mathrm{i}+1}}{∆\mathrm{x}}}$$

(1)

where j − 1, j + 1, and j are the nodes related to the nodes toward the outer, inner, and modeled sides of the building, respectively. The i and i + 1 terms are defined for the previous and present simulation time steps. Also, in the above equation, $\rho$ (kg/m³), C (kJ/kg K), $∆\mathrm{x}$ (m), and $∆\mathrm{t}$ (s) represents the density, specific heat capacity, thickness of layer, and time step, respectively. In the following, evaluations related to the thermal conductivity of the interface between the j and j − 1 node (${\mathrm{K}}_{\mathrm{E}}$) and the thermal conductivity between the surface of the j and j + 1 node (${\mathrm{K}}_{\mathrm{w}}$) are shown.

$${\mathrm{K}}_{\mathrm{w}}={\displaystyle \frac{{\mathrm{K}}_{\mathrm{j}+1}^{\mathrm{i}+1}-{\mathrm{K}}_{\mathrm{j}}^{\mathrm{i}+1}}{2}}$$

(2)

$${\mathrm{K}}_{\mathrm{E}}={\displaystyle \frac{{\mathrm{K}}_{\mathrm{j}-1}^{\mathrm{i}+1}-{\mathrm{K}}_{\mathrm{j}}^{\mathrm{i}+1}}{2}}$$

(3)

The simulation engine of the software provides two means for simulating PCMs for relating temperature curves to enthalpy in the conduction finite difference solution algorithm. The first method is the basic method, which in a temperature/enthalpy curve is utilized for freezing and melting states. The hysteresis method is the second method that uses various temperature/enthalpy curves for the melting and freezing process [33]. For the modeling and simulation of PCM as a heat storage unit, several basic assumptions were considered [34,35].

• The PCM parameters in liquid and solid states are varied from each other.
• The effect of natural convection in the PCM’s melting process is neglected.
• The ambient heat loss is ignored.
• The one-dimensional airflow and two-dimensional heat transfer from PCM are assumed.
• The symmetrical heat transfer between PCM and air is considered.

In the following, Equation (4) demonstrates the transient heat transfer of phase change material. In the mentioned equation, $\theta =T_m-T_{PCM}$ which $T_m$ and $T_{PCM}$ represents the PCM melting temperature (°C) and PCM temperature (°C), respectively [36].

$${\rho }_{PCM}{C}_{p,PCM}{\displaystyle \frac{{\partial \theta }_{PCM}}{\partial t}}={\displaystyle \frac{\partial }{\partial x}}\left({\lambda }_{PCM}{\displaystyle \frac{{\partial \theta }_{PCM}}{\partial x}}\right)+{\displaystyle \frac{\partial }{\partial y}}\left({\lambda }_{PCM}{\displaystyle \frac{{\partial \theta }_{PCM}}{\partial y}}\right)-{\rho }_{PCM}L{\displaystyle \frac{\partial \alpha }{\partial t}}$$

(4)

where $\alpha$, L (J/kg °C) and ${\lambda }_{PCM}$ (W/m °C) are defined as PCM’s local liquefaction coefficient, evaporation heat, and PCM’s thermal conductivity, respectively. Also, the $C_p$ is the specific heat of PCM (J/kg °C). The phase transition happened in the range of $T_l$ and $T_s$ which shows the lower and upper limits of the liquid and solid phase temperatures. In the following, definitions for $T_l$, $T_s$, and $\alpha$ are demonstrated [37].

$$T_l=T_m+{\displaystyle \frac{\Delta T}{2}}$$

(5)

$$T_s=T_m-{\displaystyle \frac{\Delta T}{2}}$$

(6)

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(7)

2.6. HOMER Software

The US National Energy Laboratory (NREL) developed software for renewable-based energy systems optimization, sensitivity analysis, and techno-economic simulation. The HOMER software is utilized as a tool for hybrid optimization of multiple energy resources such as grid, wind, solar, and geothermal. The output of the simulation conducted by the mentioned software can provide reliable and fast solutions for solving challenges in the field of energy [38].

2.7. HOMER Optimization

After selecting the proper PCM with the efficient thickness and placement in the building’s construction, the results were used as input for HOMER software. This simulation aims to reach nearly zero energy building with the appropriate renewable energy systems and affordable cost. In this regard, the system with no optimization as the base case and the system with the optimal one-layer and two-layer PCM were added to the HOMER simulation. The systems illustrated in Figure 4 were considered according to the weather conditions of the selected location.

Grid, PV, battery, converter, and DG were utilized for choosing the best system with higher performance and lower cost. The electricity consumption in the hot months of the year is enhanced because of the higher cooling load requirement. The electricity utilization involves room electricity, equipment, room lighting, cooling, and domestic hot water (DHW). In this study, for higher reliability of results, a 10% variation is considered in the electrical load input value.

The power grid is the main source of supplying the electrical load. However, in recent years, with the increase in population growth, electricity consumption, and power plant capacity shortages, the utilization of renewable energies has become popular. In this regard, for more energy saving and filling the probable blackouts, the PV, battery, and DG were added to the considered system.

Table 4. The electricity tariffs at off-peak, shoulder, and peak periods.

Summer Peak Time (USD/kWh)	Spring and Fall Peak Time (USD/kWh)	Winter Peak Time (USD/kWh)	First Off-Peak Period (USD/kWh)	Second Off-Peak Period (USD/kWh)	Shoulder Period (USD/kWh)
1.2500	0.5900	0.3600	0.12000	0.0800	0.200
						Color in Schedule

presents the average prices at each time of year. Six various prices were added for more accurate calculations. Moreover, the grid rate schedule was demonstrated in Figure 5 for more clarification and comparison with the given prices for each time of year.

In this study, the inflation rate and nominal discount rate were considered 1.6% and 6.00%, respectively. In addition, the prices and specifications of utilized components are given in

Table 5. Prices and specifications of utilized components.

Component	Data
PV
Model	Sharp ND-250QCS
Cell Type	Polycrystalline silicon
Operating Temperature	47.5 °C
Temperature Coefficient	−0.485%/°C
Efficiency	15.3%
Lifetime (years)	20
Replacement Cost (USD)	1200/kW
Capital Cost (USD)	1500/kW
Generator
Model	Generic Small Genset (Gen50)
Minimum Load Ratio	25%
Dry Weight
Rated RPM
Lifetime (hours)	15,000.00
Replacement Cost (USD)	357.00
Nominal Cost (USD)
Capital Cost (USD)	428.00
Capacity search space (kW)	0, 5, 10
Converter
Inverter Efficiency	95%
Rectifier Efficiency	95%
Lifetime (years)	15
Replacement Cost (USD)	300.00
Capital Cost (USD)	300.00
O&M Cost (USD/year)	3.00
Battery
Model	Generic 1 kWh Li-Ion
Roundtrip Efficiency	90%
Nominal Voltage (V)	6
Nominal Capacity (kWh-Ah)	1–167
Maximum Discharge Current (A)	500
Maximum Charge Current (A)	167
Lifetime	15
Throughput (kWh)	3000.00
Replacement Cost (USD)	400.00
Capital Cost (USD)	400.00
O&M Cost (USD)	10.00

.

2.8. Mathematical Equations of HOMER Software

HOMER software can evaluate various parameters such as net present cost (NPC), cost of energy (COE), and salvage value (S). The net present cost is defined as the system’s total costs minus all the incomes during the project’s lifetime [39]. The following equations are utilized for NPC calculation [40]:

$$\mathrm{NPC}={\displaystyle \frac{C_{total}}{CRF(j,n)}}$$

(8)

$$C_{total}=C_{capital}+C_{replacement}+C_{O\&M}-C_{salvage}$$

(9)

$$\mathrm{CRF}(\mathrm{j},\mathrm{n})={\displaystyle \frac{j{(j+1)}^n}{{(1+j)}^n-1}}$$

(10)

where the capital recovery factor is defined as CRF, and n shows the functional life years of the project. Also, j is the real interest rate evaluated by Equation (11).

$$\mathrm{j}={\displaystyle \frac{j^{\prime }-f}{1+f}}$$

(11)

In the above equation, the $j^{\prime }$ and $f$ are the nominal interest rate and specified inflation rate, respectively. The other definition is the cost of energy, which is calculated by Equation (12). The COE (per kWh) presents the average beneficial electrical energy generation cost of the system [41].

$$\mathrm{COE}={\displaystyle \frac{C_{annual,totalcost}}{E_{served}}}$$

(12)

$$C_{annual,totalcost}=\mathrm{CRF}(\mathrm{j},\mathrm{n})\times C_{NPC}$$

(13)

$$E_{served}=E_{Ac}+E_{Dc}+E_{def}+E_{grid,sales}$$

(14)

In Equation (14), $E_{def}$ and $E_{grid,sales}$ are the deferrable load served and sold energy to the grid. Also, the $E_{Ac}$ and $E_{Dc}$ are defined as the total energy utilized for serving AC and DC primary loads, respectively. The final income of the project is called salvage value, which is defined as Equation (15) [39]. In this study, the lifetime of the project was determined to be 25 years. In the following equation, $R_{project}$, $R_{comp}$, and $c_{rep}$ are the lifetime of the project, components’ cost, and replacement cost, respectively.

$$\mathrm{S}=c_{rep}.{\displaystyle \frac{R_{comp}-(R_{project}-R_{comp}.INT(\frac{R_{project}}{R_{comp}}))}{R_{comp}}}$$

(15)

3. Results and Discussion

In this study, the schematic of the considered building was designed. The various initial data were given to the Design Builder software, and the consumed energy for different parts of the building was obtained. After the required calculations, the results were added to the HOMER software for economic analysis and choosing the best hybrid energy system.

3.1. Design Builder Results

In the simulation, the effect of utilizing various PCMs, different PCM thicknesses, and the location of placing PCM in the wall’s construction on the consumed energy load of the building was investigated. The indoor temperature and transferred heat had a relation with the thickness of the walls and roof construction. In the initial simulation, the modeled building was configured without insulation, double-glazed windows, or other energy-efficient features. The results indicated the highest and lowest amounts of electricity consumption were about 41.421 MWh and 15.701 MWh for July and December, respectively. Figure 6a illustrates the total consumed electricity for each month, while Figure 6b demonstrates the details of electricity consumption. The electricity was utilized for room lighting, equipment, cooling, and domestic hot water (DHW). The required energy for heating the building was supplied by natural gas instead of grid electricity. Another important parameter in building simulation is carbon dioxide (CO₂) production. The annual CO₂ production in the primary design was 204.94 × 10³ kg.

Different primary optimization techniques were applied to reduce the building’s energy consumption. In this step, the total annual electricity reached 292.78 MWh. The main saving happened in the required energy for cooling, which had about an 11% decrement compared to the base model. For more reduction in energy consumption and environmental emissions, more optimization was performed by adding a PCM layer to the building’s construction. The PCM layer was added to the walls and roof of the building for storing the heat and releasing it during cloudy and nighttime for more thermal comfort and energy saving. By considering the weather conditions of the considered city, the PCMs with 21, 23, 27, 29, and 32 °C melting points were used in the first scenario. Figure 7 shows the influence of PCMs with different melting points on the total electricity consumption of the building.

The results of analyzing PCMs with various melting points and similar thermal conductivities and heat capacities demonstrated that during the summertime and in warmer environments, PCM 29 had better performance. Phase change materials have the highest impact on reducing energy consumption when the phase change temperature is close to the ambient temperature. Storing and releasing energy at the nearest ambient temperature to the phase change temperature can provide the most indoor thermal comfort. The higher temperature requires a higher melting point of PCM for efficient function. However, the mentioned PCM was not proper in March, April, May, November, and December. The performance and efficiency of PCM are completely related to the average temperature of the month. So, when the temperature of the considered day or month is low, the PCM with the lower phase change temperature is more efficient. The reason is that the PCM with a lower phase change temperature can absorb the heat and release it to the environment under the mentioned weather conditions. However, PCM with a high phase-changing temperature can only absorb heat to increase the PCM temperature and cannot activate and utilize the PCM’s latent heat. In this regard, the proper PCM is the one that can be highly matched with the ambient temperature. Generally, by considering the total performance of PCM during the year, PCM 29 was the most efficient among all based on the temperature of the selected location. By utilizing the proper PCM, the total consumed electricity reached 264.78 MWh. The outcomes demonstrated a 9.56% and 20.72% decrement compared to the base model and primary optimized model, respectively. The total consumed electricity with PCM 21, PCM 23, PCM 25, and PCM 32 was 268.1 MWh, 266.64 MWh, 267,63 MWh, and 269.69 MWh, respectively. In the environmental aspect, by utilizing the efficient PCM, the generated CO₂ amount reached 160.86 × 10³ kg, which showed a 21.5% reduction in emissions.

Table 6. Comparative results of PCMs with different melting points.

	Total Electricity (MWh)	Total Gas (MWh)	CO2 Emission (×103 kg)	Total Cooling (MWh)	Zone Heating (MWh)
Base Model	333.99	13.57	204.93	315.00	11.53
PCM 21	268.1	2.29	162.9	204.85	1.94
PCM 23	266.64	2.05	161.97	203.22	1.74
PCM 25	267.63	1.86	162.53	204.89	1.57
PCM 29	264.78	2.12	160.86	199.88	1.8
PCM 32	269.29	2.52	163.66	207.98	2.15

illustrates the comparative results of various PCMs’ performance.

Selecting the proper type of PCM utilized in a building is related to the outdoor temperature and environmental conditions. Phase change materials use the melting and freezing process for storing and releasing heat, so the efficient thickness should be selected. Lower or higher thickness of the PCM layer causes inappropriate phase change and function. The proper thickness of PCM is affected by various parameters, including climatic conditions, thermal properties, and energy efficiency goals. The most efficient cooling and heating process requires a specific thickness of the PCM layer. It should be mentioned that the economic aspects have an important role. By utilizing more PCM, the electricity consumption will be decreased, but it will incur more costs. In this regard, for choosing the best thickness of PCM, a balance should be considered between the energy-saving amount and costs. The highest energy consumption in a building is related to the cooling and heating loads, which can be decreased by using phase change material. Figure 8 illustrates the performance of various PCMs by varying the thicknesses for the designed building and specified climatic conditions.

To specify the efficient thickness of PCM, the electrical load decrement by applying different thicknesses compared to the previous thickness was evaluated by the following relation.

$$Y={\displaystyle \frac{Load\left(A\right)-Load\left(B\right)}{Load\left(A\right)}}\times 100$$

(16)

where A and B are the previous thickness and present thickness, respectively. The analysis was performed for PCM 21, 23, 25, 29, and 32 with various thicknesses for walls and roofs under the climate conditions of the selected region. The changes in the thickness were applied the same for the walls and roof of the building. The electricity consumption was reduced by the thicker PCM layer. The point in the selection of the best PCM for the considered simulation was the total cost compared to the energy-saving amount. The maximum Y was related to a 29 degrees Celsius melting point with a 50 mm thickness, which utilized 264.78 MWh. To reach the same energy savings as the other PCMs, higher thicknesses should be utilized, which increases the total costs.

Table 7. Comparison of different PCMs and thicknesses based on electricity consumption, CO₂ emissions, heating/cooling loads, and gas usage.

	Total Electricity (MWh)	Required Thickness (mm)	CO2 Production (×103 kg)	Heating (MWh)	Cooling (MWh)	Gas (MWh)
PCM 21	264.43	130	160.66	1.89	201.56	2.22
PCM 23	264.9	90	160.9	1.68	201.25	1.98
PCM 25	264.8	130	160.48	1.53	201.28	1.8
PCM 29	264.78	50	160.86	1.8	199.88	2.12
PCM 32	264.8	170	160.92	2.04	203.36	2.39

illustrates a comparison of all the analyzed PCMs that have similar electricity consumption with different thicknesses. The outcome of the sensitivity analysis on the thickness of the PCM showed that the 50 mm thickness was the best among the utilized PCMs, especially for the PCM 29, which demonstrated the highest performance for the considered building. In this regard, PCM 29 with 50 mm thickness was selected as the best for the building. In the following, another sensitivity analysis was conducted on the placement of optimum PCM.

After selecting the best PCM with the most efficient thickness, the placement of the PCM in the roof layer was analyzed. The heating and cooling simulation results indicated that the changes in the roof construction had the highest influence on the energy consumption of the building. So, the replacement of the PCM layer in the construction was just conducted on the roof of the building. In the third scenario, the analysis of the two-layer PCM location was conducted. Figure 9 illustrates the results of the sensitivity analysis of the placement of the PCM layer. Different placements, as described in Table 3 of the methodology section, were studied. The simulations investigated the effect of a two-layer PCM with various placements on the total electricity consumption of the building. In this scenario, the results of both similar and different types of PCMs were provided. The best performance for the third scenario for two similar PCM layers was obtained by applying the B arrangement with a total electricity consumption of 256.07 MWh. By applying two similar layers of PCM 23, the energy consumption was reduced by about 4% compared to the one layer of PCM 23 located in the middle of the construction. In contrast, utilization of one layer of PCM in the middle of construction was performed 3.2% better than two layers of PCM 29. In the analysis outcome of two various PCM layers in the roof construction, the F arrangement showed better energy saving with a total electricity consumption of 255.38 MWh. In the mentioned arrangement, the CO₂ production was 155.71×10³ kg. In the different PCM layer analyses, the K arrangement had the lowest performance, with the required total cooling and electricity energy of 212.82 MWh and 271.97 MWh. Eventually, the final results demonstrated that the outer layer of PCM related to the environmental condition, so the placement of PCM with a higher melting point worked better compared to the PCM with a lower melting point in the outer layer.

3.2. HOMER Results

This study aims to move toward zero/nearly zero energy buildings with reasonable costs. In this regard, after finding the proper optimizations and PCMs for the considered building, the results were used as input for the HOMER software. The best system configuration and renewable energy utilization were obtained by considering economic and environmental aspects. Three different loads related to the base case, one-layer PCM, and two-layer PCM were added to the HOMER. The average daily electricity consumption of the mentioned modes was 915.04 kWh, 725.42 kWh, and 699.67 kWh, respectively.

Table 8. HOMER optimized results for the base case and the best of one-layer and two-layer PCM.

Scenario	PV (kW)	Battery (kWh)	Converter (kW)	Grid Purchase (kWh)	Generator Working Hours	Initial Cost (USD)	NPC (USD)	COE (USD)	RF (%)
Base Case	184	429	114	55,747	2930	486,421	996,045	0.162	79.2
One-layer PCM	142	323	85.6	43,890	2827	375,829	781,737	0.162	77.6
Two-layer PCM	135	316	84.1	42,188	2821	363,157	754,342	0.163	77.2

illustrates the obtained results by considering various loads for the simulated building.

The results of simulations indicated that conducting optimizations and adding one-layer PCM and two-layer PCM caused 21.26% and 24.32% reductions in grid purchase compared to the base case. Also, the initial cost of the mentioned optimizations reduced the cost up to USD 110,592 and USD 123,264, respectively. The mentioned building’s cost reduction is due to the utilization of the PCM properties, which can absorb and store heat during the day when the temperature is high and release it during the night. Using PCM layers can reduce energy consumption during peak hours, which results in cost reduction, especially for areas where energy prices are different during peak hours. Furthermore, the utilization of PCM layers can improve indoor thermal comfort, which reduces the dependence on the HVAC system and decreases the energy consumption. In addition to conducting economic analysis, by considering the project lifetime, the considered cash flow should be investigated. Figure 10 demonstrates the related cash flow of the base case. The cash flow shows the replacement, fuel, salvage, operating, and capital costs of all components.

According to the conducted analysis, the highest capital cost was for the PV panel, with the amount of USD 276,332. As the PV’s lifetime was set to 20 years, the replacement cost after its lifetime was considered to be about USD 223,829 which was the highest replacement cost among all the components. Based on the considered strategy, after the project’s lifetime, the salvage amount of the project was about USD 168,782.

As shown in Figure 11, for the optimized simulations, the highest capital costs were related to PV, battery, converter, and generator, respectively. The evaluated simple payback in one-layer and two-layer optimized systems was 6.24 and 6.23 years. Utilizing generators as an auxiliary system for PV and preventing COE increment was a proper choice. In addition, the existence of batteries for storing the produced electricity and using it at peak periods enhanced the system’s performance. In terms of environmental aspects, by considering PV besides the generators, the CO₂ and CO production of the base case were 58,486 and 180 kg/year. In contrast, by adding PCM layers to the building and optimization, the amount of CO₂ was reduced by about 14.5% and 16.4%, respectively.

3.3. Accuracy of Method and Simulation Results

The recent investigation has no similar experimental work to use validation or verification methods to prove the accuracy of our simulation. In this regard, various methods are used to check the correctness of the model and simulation. In the first step, all of the equations used for PCM in the simulation are consistent with the intrinsic properties of this material. To obtain the most accurate results from the effect of PCM in the building, three states of material, including solid, liquid, and transition, are considered. These equations and results are consistent with similar papers working on phase change materials [42,43,44]. Additionally, incorporating detailed equations for PCM has effectively demonstrated its insulating properties in the results. All insulators have a critical radius (thickness), and findings confirm this, reinforcing the reliability of the obtained data. The results provided in Table 7 clearly demonstrate that achieving the lowest electricity consumption requires a sharp reduction in energy utilization at a specific thickness and temperature. This highlights the critical thickness of the PCM applied to the wall. For PCM 29, the critical thickness is observed at 50 mm, further validating the accuracy of the heat transfer equations formulated for phase change material. On the other hand, the related equations and methods used in the simulation in the HOMER section of this article are completely consistent with the investigation conducted by Tamjid Shabestari et al. [39].

4. Conclusions

Due to the increased importance of zero- and nearly zero-energy buildings in recent years, the investigations are moving toward reducing energy consumption and carbon emissions. In this study, a representative large-scale building model was developed using Design Builder to simulate performance under hot climate conditions. Primary energy-saving strategies were applied, and three novel scenarios were investigated: selecting the appropriate PCM, determining its optimal thickness, and analyzing the configuration of a two-layer PCM system. The results of the most efficient scenario were then used as input in HOMER software to identify the optimal hybrid renewable energy system. This integrated approach, combining PCM analysis and renewable energy optimization, produced the following outcomes:

• The primary optimizations conducted on the designed building reduced the consumed energy by about 11% compared to the base case.
• According to the selected area, PCM 29 performed better and decreased the energy consumption by about 20.72% and 9.56% compared to the base case and optimized system. Furthermore, the produced CO₂ reduction was 44.08 × 10³ kg, which demonstrated a great environmental impact decrement.
• Based on the defined scenarios, the best thickness for PCM 29 was 50 mm, and the energy consumption was 264.78 MWh.
• Various strategies were applied for finding the best arrangement of two-layer PCM in the roof. The results indicated that the total energy utilization and CO₂ production were 255.38 MWh and 155.71 × 10³ kg, respectively, at the best arrangement.
• The HOMER results indicated that the optimized system includes PV, DG, Bat, and grid, in which the COE reached about USD 0.162.
• The grid utilization decreased by about 21.2% and 24.3% by utilizing one-layer PCM and two-layer PCM in the building.

Future Suggestions:

• Broader analysis of seasonal variations is recommended.
• It is suggested to investigate the analyzed parameters and scenarios on the various housing types and multi-storey buildings.
• Investigation and comparison of various climate conditions are proposed.
• The detailed analysis of the effect of temperature variations during a day on the various phase change materials’ performance is recommended.
• The effect of ambient temperature on the various types of phase change materials, including bioPCMs, hydrated salt PCMs, and their comparison, is proposed.