Optimizing Hybrid Heating Systems: Identifying Ideal Stations and Conducting Economic Analysis Heating Houses in Jordan

Jordan has limited natural resources and high-energy prices. According to the Word Bank organization review, During the last two decades, Jordan's population has also more than doubled to 11 million, increasing the energy demand. Further [1], the Jordanian government estimated that the Jordan population will rise to over 19 million by mid – 2050 [2]. The estimated yearly personal energy consumption rate in Jordan was 1746 kWh on average (as of 2021) [3]. This coupled with climate change is going to have a major impact on energy demand and the amount of usable energy sources available. Action is required immediately to reduce demand, increase supply, and apply the principles of a circular economy to meet future energy requirements.

Jordan is a country that is hugely reliant on imported fossil fuels to meet its energy needs. Referring to the International Renewable Energy Agency (IRENA) Report 2021 [4]. It was illustrated from the report that 4% of the total supply of energy in Jordan came from renewable energy and 96% of the total energy from hydrocarbons fuels (coal, gas, and oil). Furthermore, the report illustrated that only 3% of the energy supply in Jordan increased during the period 2014-2019, while solar energy was the main supply of renewable energy. During 2022, the fossil fuels rapidly increased in Jordan. The Jordanian government raised the prices of oil derivatives many times during the year 2022. The prices of diesel usually used for resident building heating increased by 47% during the period from May 2021 to May 2023. The price of an LPG gas for central disruption increased to 33% according to the Ministry of Energy retail prices of petroleum products [5, 6]. This is due to rises in global energy prices and due to the increase in the energy demand in Jordan.

There are alternative energy sources can be used to reduce the risk of raised prices of oil derivatives including solar thermal energy, heat pumps, and hybrid heating systems. In accord with the report of the Ministry of Energy in Jordan 2021 about 48% of the electrical energy in Jordan quantities in residential buildings [3]. There are many benefits to using hybrid energy systems. They are more efficient than traditional systems, and they can help reduce greenhouse gas emissions, providing sustainable energy and utilizing natural resources optimally. Hybrid systems can also provide backup power in case of a power outage. Due to the oil derivatives price shocks, it is necessary to optimize and select the most adequate domestic heating system depend on the location and weather. It is necessary to find alternative energy sources because fossil fuels contribute to pollution. Hybrid conventional solar collectors are utilized, but their effectiveness is significantly reduced on cold, cloudy, and windy days [7]. In Jordan, some of these hybrid systems are designed with thermal storage systems [8, 9], or without storage systems [10-12].

Researchers have used optimization methods to select the best energy model configurations for a system. Optimizing capacity and operation strategies is crucial to the efficiency of a system. Generally, studies aim to maximize energy efficiency, reduce carbon dioxide emissions, and reduce total annual costs. For example, in Tianjin, China, the researchers Lu et al. [13] developed a bi-level model optimization method suitable for coupled multi-energy systems, which can improve energy efficient and more stably. Comparing the optimized system to the traditional system, 36.2% of annual operating costs can be saved. The researchers Liu et al. [14] used multi-objective optimization technique to optimize distributed energy systems combined with hybrid energy storage and reduce annual global emissions of carbon dioxide by 51.7%–73.2%. The team of Ren et al. [15] discussed using multi-objective optimization technique to improve a hybrid heating and cooling system that integrates solar and geothermal energy sources. According to the results, the hybrid system configuration in China that uses a heat pump with electricity achieves better performance than other configurations. Wu et al. [16] compared configuration and operation optimization methods. Using the collaborative optimization method, multiple decision variables were determined using a genetic algorithm and orthogonal experimental design. Zhang et al. [17] show that it is possible to save 5.5% of energy in a hotel by using multi-population genetic algorithms to enhance a combined cooling, heating and geothermal heat pump. Kalbasi et al. [18] discussed the use of hybridized systems for heating water and spaces in sixteen stations in Belgium. As a result of using TSOL software in energy dynamic simulation, the researchers concluded that evacuated tube solar water with an electric boiler is both cost-effective and attractive. The ARAS technique, VIKOR, and TOPSIS optimization methods were also applied.

The researchers Franco et al. [19] applied a dynamic simulation to study energy systems in a building in Pisa. They concluded that an energy savings of up to 44% could be achieved through occupant-centred control strategies and demand-controlled ventilation in large non-residential buildings. Multi-objective optimization approach applied by the researchers [20] Based on different scenarios relating to climate change and energy price aberration, the method is applied to a building in Tehran. As a result of energy retrofitting, up to 73% of CO₂ emissions can be reduced, and thermal discomfort can be reduced by up to 46%. For buildings’ space heating loads in the integrated community energy system, the researcher Jin et al [21] conducted a numerical study to demonstrate that a balanced scheduling scheme can be obtained with a bi-level model predictive control method by balancing consumer energy costs with the profits of the operator of an integrated community energy system (ICES). The model predictive control (MPC) was based mixed-integer linear program that formulated as an optimization of a bilevel model predictive control model using the Karush-Kuhn-Tucker optimality conditions. The researchers [22] formulate and develop computationally efficient heuristics for solving the optimal power-water-heat flow problem. By combining convex relaxation and convex–concave procedures, the proposed heuristic is able to solve the OPWHF in an iterative manner. The simulation shows that the proposed framework improves the operational flexibility of the integrated system during periods of time-varying energy prices and photovoltaic generation. In order to optimize HVAC systems in buildings, Lu et al. [23] employed adaptive neuro-fuzzy inference systems. The simulation study compares the optimization method with traditional methods. Based on the results, the proposed method significantly increases system performance.

The objective of this research is to determine the overall heating capacity required for a residential building across six locations in Jordan, employing three variations of a central hybrid heating system. These systems incorporate an evacuated tube solar collector in conjunction with either an auxiliary boiler or a heat pump. The research can illustrate which central heating system is more suitable depending on the location and climate in Jordan.

Figure 1 illustrates the Global Horizontal Irradiation (GHI) in Jordan, a Middle Eastern country covering an expanse of 89,213 square kilometers, with around 75% of its landmass classified as desert terrain [24].

In Jordan, the climate is predominately Mediterranean; there are too short transitional periods in the year: the first begins around the beginning of October, and the second about the middle of April. The climate in Aqaba and the Dead Sea is characterized by a hot and dry desert climate compared to Amman, which has a Mediterranean climate that is milder [25]. Figure 1 shows long term (1999-2018) average global horizontal irradiation AGHR in Jordan and selected cities for different regions in Jordan. The range AGHR in Jordan is between 2000-2900 kWh/m², while Maan has the highest global radiation more than 2900 kWh/m². In this research, six selected cities for different regions in Jordan were obtained, namely. Amman, Aqaba, Maan, Karak (Ghor El Safi), Irwaished, and Irbid to cover most climates in Jordan.

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Figure 1. Global horizontal irradiation in Jordan and selected cities for different regions in Jordan [26]

The objective of this study is to optimize and compare three central heating system types: (1) a hybrid central heating system consists of a solar collector and gas boiler. (2) A hybrid central heating system consists of a solar collector with diesel boiler. (3) A hybrid central heating system consist of a solar collector and heat pump. This research also evaluates and compares various optimization methods, which include:

•   Additive Ratio Assessment (ARAS)

•   TOPSIS method

•   VIKOR method

•   Multi-Objective Optimization based on Ratio Analysis (MOORA)

•   Analytic Hierarchy Process (AHP)

Furthermore, the research will discuss the impact of factors such as CO₂ emissions avoided, heating solar fraction, system efficiency, economic effect, and the impact of climate data in six locations in Jordan. Then, the research tries to optimize the best configurations. The novelty of this research lies in its ability to demonstrate the optimal hybrid heating system tailored to the unique combination of location in Jordan and climate data.

The level of idealization is utilized in the ARAS approach to order the alternatives [32, 33]. Three stages make up the ARAS method [32]. The m×n decision-matrix is designed in the first step, where m is the number of choices (rows), and n is the number of criteria (columns).

$R=\left[\begin{array}{ccccc}r_{01} & \cdots & r_{0 j} & \cdots & r_{0 n} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ r_{i 1} & \cdots & r_{i j} & \cdots & r_{i n} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ r_{m 1} & \cdots & r_{m j} & \cdots & r_{m n}\end{array}\right] i=\overline{0, m} ; j=\overline{1, n}$            (10)

The function of the i-th option in the j-th criterion is represented by $r_{i j}$. The optimum value for the j criterion is also $r_{0 j}$. The optimum value of the j variable is established by Eq. (11).

$\begin{aligned} & r_{i j}=\max _i r_{i j}, \text { if } \max _i r_{i j} \text { is preferable } ; \\ & r_{i j}=\min _i r_{i j}, \text { if } \min _i r_{i j} \text { is preferable. }\end{aligned}$         (11)

The initial decision matrix's values are converted to values that fall between (0-1) and (0-∞) using the normalizing technique. The second stage involves normalizing the initial input values for each criterion to the form $\bar{r}_{i j}$, which corresponds to the elements of the matrix $\bar{R}$ and is defined by Eq. (12).

$\bar{R}=\left[\begin{array}{ccccc}\bar{r}_{01} & \cdots & \bar{r}_{0 j} & \cdots & \bar{r}_{0 n} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ \bar{r}_{i 1} & \cdots & \bar{r}_{i j} & \cdots & \bar{r}_{i n} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ \bar{r}_{m 1} & \cdots & \bar{r}_{m j} & \cdots & \bar{r}_{m n}\end{array}\right] i=\overline{0, m} ; j=\overline{1, n}$           (12)

The following criteria, whose maxima represent the preferred values, are normalized:

$\bar{r}_{i j}=\frac{r_{i j}}{\sum_{i=0}^m r_{i j}}$             (13)

By using a two-stage process, the criteria, whose preferred values are minima, are normalized:

$r_{i j}=\frac{1}{r_{i j}^*}, \bar{r}_{i j}=\frac{r_{i j}}{\sum_{i=0}^m r_{i j}}$             (14)

In the third step, the normalized matrix $\bar{R}$ is combined with the weights to create the matrix $\hat{R}$. $w_j$ stands for the weight of each $\mathrm{j}$-criterion.

$\hat{R}=\left[\begin{array}{ccccc}\hat{r}_{01} & \cdots & \hat{r}_{0 j} & \cdots & \hat{r}_{0 n} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ \hat{r}_{i 1} & \cdots & \hat{r}_{i j} & \cdots & \hat{r}_{i n} \\ \vdots & \ddots & \vdots & \ddots & \vdots \\ \hat{r}_{m 1} & \cdots & \hat{r}_{m j} & \cdots & \hat{r}_{m n}\end{array}\right] i=\overline{0, m} ; j=\overline{1, n}$               (15)

$\hat{r}_{i j}=\bar{r}_{i j} * w_j$               (16)

The optimality function values are specified by the following equation:

$S_i=\sum_{j=1}^n \hat{r}_{i j}, i=\overline{0, m}$             (17)

where, $S_i$ represents the value of the option i's optimality function. Each option's alternative utility is determined by contrasting it with the best value, designated $S_0$. According to Eq. (18) the utility degree equation $\left(K_i\right)$, is found for option $A_i$. Consider the fact that $K_i$ values are used to rank the alternatives.

$K_i=\frac{S_i}{S_0}, i=\overline{0, m}$             (18)

Inconsistent and disproportionate criterion decision problems were the motivation for the development of VIKOR. The VIKOR model bases ranking on how close a solution is to the ideal one. Four steps make up the VIKOR technique [37, 38].

Step 1: Using Eq. (26), the optimum ideal solution $f_j^*$ (PIS) and the undesirable ideal solution $f_j^{-}$ (NIS) are derived:

$\begin{aligned} & f_j^*=\left\{\max _i f_{i j} \mid j \in I_1\right\}, f_j^*=\left\{\min _i f_{i j} \mid j \in I_2\right\} \\ & f_j^{-}=\left\{\min _i f_{i j} \mid j \in I_1\right\}, f_j^{-}=\left\{\max _i f_{i j} \mid j \in I_2\right\}\end{aligned}$               (26)

where, I₁ and I₂ are, correspondingly, a set of profit and cost criteria.

Step 2:  $S_i$ and $R_i$ values are determined using the Eqs. (27) and (28):

$S_i=\sum_{j=1}^n w_j\left(f_j^*-f_{i j}\right) /\left(f_j^*-f_j^{-}\right)$             (27)

$R_i=\max _j\left[w_j\left(f_j^*-f_{i j}\right) /\left(f_j^*-f_j^{-}\right)\right]$           (28)

where, $w_j$ is the weighted criteria.

Step 3: Using Eq. (29) to calculate the $Q_i$ index:

$Q_i=v\left(\frac{S_i-S^*}{S^{-}-S^*}\right)+(1-v)\left(\frac{R_i-R^*}{R^{-}-R^*}\right)$               (29)

where, $S^*=\min S_i, S^{-}=\max S_i, R^*=\min R_i, R^{-}=$ $\max R_i$, and the weights $v$ and $(1-v)$ represent attractiveness and regret, respectively. Depending on the decision-maker, $v$ can have any value between 0 and 1.

Step 4: The higher the priority and rank, the lower the $Q_i$ value.

When making decisions to address complicated issues, the Analytic Hierarchy Process (AHP) is applied with multiple criteria or alternatives. Developed by Saaty [40], AHP broadly used in various fields, for instance: engineering, economics, social sciences, and environmental studies.

The basic idea behind AHP is to break down a complex decision problem into smaller, more manageable parts, and after that compare each component's relative importance using pairs of evaluations. By doing so, AHP offers a well-structured framework for making decisions, and helps decision-makers to clarify their preferences and priorities. The AHP method divided into the following steps:

Step 1: Structure the issue: Create a decision hierarchy, define the decision problem, and list the criteria and alternatives.

Step 2: Pairwise comparison: Evaluate the relative importance of each criterion and alternative by pairwise comparisons using a 9-point scale. The scale ranges from 1 (equally important) to 9 (extremely more important). Assuming, for instance, that criterion A is twice as significant as criterion B, then A given a value of 2 and B a value of 1/2.

Step 3: Weight calculation: Calculate the weights of the criteria and alternatives by normalizing the comparison matrix for pairs. The weights are the relative importance of each criterion and alternative, and they should add up to one. To calculate the weight of criterion i, the ith column's sum divided by the comparison pairs matrix. The pairwise comparison matrix is divided by the sum of the jth row in order to determine the weight of alternative j.

Step 4: Consistency check: By calculating the consistency index (CI) and consistency ratio (CR), you may determine whether the pairwise comparison matrix is consistent. The CI is a measure of how consistent the judgments are, and the CR is a ratio of the CI to a random consistency index. If the CR is less than 0.1, the judgments considered consistent.

Step 5: Aggregation: Aggregate the weights of the criteria and alternatives to get an overall ranking. The aggregation done in several ways, such as multiplying the weights of the criteria and alternatives or using a weighted sum.

Here is the equation for normalizing the pairwise comparison matrix:

$W_{i j}=\frac{A_{i j}}{\sum_j A_{i j}}$                  (34)

where, $W_{i j}$ is the weight of criterion i relative to criterion $\mathrm{j}, A_{i j}$ is the judgment of criterion i compared to criterion $j$, and $\sum_j A_{i j}$ is the sum of the ith column.

Here is the equation for computing the consistency index:

$C I=\frac{\lambda_{\max }-n}{n-1}$               (35)

where, $\lambda_{\max }$ is the pairwise comparison matrix's highest eigenvalue, and n is the number of criteria or options.

Here is the equation for computing the consistency ratio:

$C R=\frac{C I}{R I}$             (36)

where, RI is the random consistency index, which depends on the size of the pairwise comparison matrix as shown in Table 2.

Table 2. Consistency of random matrices for AHP method

Table 4 illustrates the output data corresponding to each system as depicted in Figure 2. Upon examination of the outcomes for the Amman station, it is evident that the solar water heating system exhibits superior performance across all evaluated indices, encompassing system efficiency, avoided CO₂ emissions, solar fraction, and fuel saving.

Table 4 presents the results of one-year dynamic simulations. The results indicate that the Aqaba and Amman stations had the highest and lowest performance, respectively, with the former supplying 99.55% and the latter supplying 75.81% of the total required heating demand through Evacuated Tube Solar Water (ETSW). The average total solar fraction for the stations studied in Jordan was found to be 87.49%, based on the results. The stations in Maan, Irwaished, Amman, and Irbid had a total solar fraction lower than the average (i.e., less than 87.49%), indicating that they may not be as appropriate for the intended purpose.

In terms of the thermal power needed for space heating, Maan station has the highest solar contribution with 8.388 GJ per year, while Ghor El-Safi station has the lowest with 2.843 GJ per year. The total solar power transferred for heating annually is 34.898 GJ, which results in an average heating power of 5.816 GJ per station. This average heating power produced by each station is equivalent to a heating solar fraction of 75.94%.

Regarding the potential for providing sanitary hot water, Maan and Ghor El-Safi stations have the highest and lowest solar contribution amounts, respectively. The installation of ETSW in Maan resulted in a supply of 10.202 GJ/year of hot water, while for Ghor El-Safi station, this figure was 9.023 GJ/year. In total, the solar contribution to domestic hot water (DHW) for all the stations under study was 58.485 GJ, resulting in an average supply of hot water power of 9.748 GJ per station. This average supply of hot water power is equivalent to a DHW solar fraction of 99.61%.

Regarding the avoidance of CO₂ emissions resulting from the non-utilization of fossil fuels, Maan station is considered the most efficient with an annual CO₂ emission avoidance of 1923.10 kg, 1,478.50 kg, and 1,375.70 kg when using diesel, gas, and heat pump auxiliary boilers, respectively. On the other hand, Ghor El-Safi station is the least efficient with an annual CO₂ emission avoidance of 1,384.80 kg, 1,064.60 kg, and 878.10 kg for the same auxiliary boilers. The average annual CO₂ emission avoidance for each station using the same auxiliary boilers are 1675.85 kg, 1288.40 kg, and 1151.71 kg. In total, all stations combined will avoid emitting 8.23 tons of CO₂ emissions per year.

9.1 Weighting of indices

Figure 3 displays the outcomes of the BWM method's final weighting of the indexes based on meeting with several academic researchers in the field of renewable energy as clarified by Kalbasi et al. [18]. It is evident from the results that "Total solar fraction" and "DHW solar fraction" carry the most significant weight, while "Solar contribution to heating" has the least weight. A comparison of the index weights can be observed in Figure 3. Additionally, the BWM technique's consistency ratio for the indexes is 0.07306381.

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Figure 3. Comparison of the weight of indexes

9.2 Ranking of stations

Figure 4 displays the outcomes of a study that assessed the suitability of different stations using five optimization techniques: ARAS, TOPSIS, VIKOR, MOORA, and AHP. The techniques used a method ranking index, such as Ki, Yi, and Qi, to rank the stations. The study revealed that Maan station was the most suitable according to three out of five optimization techniques, while Aqaba station was ranked first in two methods. The ranking presented in Figure 4 was based on a diesel auxiliary boiler. However, when a gas-powered auxiliary boiler was used, the same ranking was obtained as shown in Table 5. In contrast, when a heat pump was used as an auxiliary boiler, the order of some intermediate stations changed, although the first and last preferred stations remained the same.

Table 4. The outputs for each station

		ARAS		TOPSIS		VIKOR		MOORA		AHP
Location	Type	Ki	Rank	TOPSIS score	Rank	Qj	Rank	Yi	Rank	Yi	Rank
Maan Airp.	Oil-fired boiler	0.145288888	1	0.563640027	3	0.165442786	3	0.424713996	1	0.926854382	1
GHOR EL SAFI	Oil-fired boiler	0.141639488	3	0.677652911	2	0.106699213	2	0.412766177	3	0.901313906	3
Hotel-4 irwaished	Oil-fired boiler	0.140046855	4	0.468840561	4	0.381581446	4	0.409234588	4	0.894741424	4
Amman Airp.	Oil-fired boiler	0.129812393	6	0.259087471	6	0.934670484	5	0.379440412	6	0.83359929	6
AQABA INTL AIRPORT	Oil-fired boiler	0.144243661	2	0.712718768	1	0.010070757	1	0.420507377	2	0.917176491	2
Irbid	Oil-fired boiler	0.130540722	5	0.266286238	5	0.947052684	6	0.381520915	5	0.837960855	5
Location	Type
Maan Airp.	Gas-fired boiler	0.145286491	1	0.563627025	3	0.165442786	3	0.424704272	1	0.926854382	1
GHOR EL SAFI	Gas-fired boiler	0.141638608	3	0.677676562	2	0.106697187	2	0.41276175	3	0.901318833	3
Hotel-4 irwaished	Gas-fired boiler	0.14006035	4	0.469062236	4	0.381216078	4	0.409273338	4	0.894845288	4
Amman Airp.	Gas-fired boiler	0.129810269	6	0.259060844	6	0.934662893	5	0.37943163	6	0.833600242	6
AQABA INTL AIRPORT	Gas-fired boiler	0.144241317	2	0.712728469	1	0.01010154	1	0.420498548	2	0.917172611	2
Irbid	Gas-fired boiler	0.130538279	5	0.266257426	5	0.947052684	6	0.381511199	5	0.837959732	5
Location	Type
Maan Airp.	Heat pump	0.146028798	1	0.578517441	3	0.165442786	3	0.427024913	1	0.926854382	1
GHOR EL SAFI	Heat pump	0.140300932	4	0.651500521	2	0.107149745	2	0.408899756	4	0.889842559	4
Hotel-4 irwaished	Heat pump	0.140519758	3	0.484268234	4	0.378872283	4	0.410750158	3	0.893389053	3
Amman Airp.	Heat pump	0.129991687	6	0.279253315	6	0.934808663	5	0.38008311	6	0.830575358	6
AQABA INTL AIRPORT	Heat pump	0.14328139	2	0.690146875	1	0.009558775	1	0.417758844	2	0.907800244	2
Irbid	Heat pump	0.130631517	5	0.284794018	5	0.947052684	6	0.381900123	5	0.834464357	5

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Figure 4. A comparison of each station's ranking using ARAS, TOPSIS, VIKOR, MOORA, and AHP

9.3 Assessing the amount of loses in Maan station

Eq. (4) from the "Help" section of the TSOL software provides more information on the heat losses for solar water heaters. First the energy received by the collectors can be calculated and then calculate the optical losses by deducting the energy output and heat losses. Depending on the length of the pipe, both convective and conductive heat transfer are responsible for the heat loss in the pipes.

From Figure 5, it can see that the solar collector receives 168.825 GJ of radiation, and each of the 20 solar collectors receives 8.44 GJ/m². The radiation on the horizontal surface in Maan is 8.028 GJ/m², as shown in Table 3. The tilted surface is what causes the difference in energy that the collector receives. The SWH at Maan station experiences 49.282 GJ of optical losses. As the temperature of the solar collector’s increases, heat loss amplifies, and it becomes 87.7 GJ. Figure 5 shows that losses in tanks constitute only 3.5% of the total losses (145.547 GJ). Overall, the loss analysis for this station shows that heat losses, then optical losses, are the main causes of losses. The least amount of losses occur in the buffer tank, main tank, internal piping, and external piping.

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Figure 5. Energy balance illustration for Maan station

9.4 Economics analysis of using ETSW

This section focuses on the economics of using ETSW to provide a portion of the building's heating needs as well as sanitary hot water usage. Payback time [41-43] is used to evaluate economic analysis and can be computed using the formula below:

$\begin{aligned} & { Payback \,Time [years] } =\frac{{ Total \,cost \,to \,purchase \,and\, install\, SWH \,system }}{ { Cost \,savings\, per\, year \,owing \,to \,using\, SWH }}\end{aligned}$               (37)

ETSW with HWST initially costs $13000 to purchase and install. On the other hand, the power needed for pumping can be computed simply using the power consumption of 0.025 kWh/m² [44]. The ability to save energy for each station is shown in Figure 6, which demonstrates that the energy-saving ranges from 5311.9 kWh/year (for Ghor El Safi) to 7376.6 kWh/year (for Maan).

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Figure 6. Energy saving potential in each station due to SWH system

In two distinct examples, the payback time's economic calculations were examined. In the first case, space heating and sanitary hot water were both provided by a hybrid system that included a gas boiler and ETSW. Figure 7 displays the payback time results based on a 0.85% gas boiler efficiency and a $0.06 per kWh natural gas pricing according to the average natural gas price in Jordan in 2022. The cost of using the SWH system is recouped economically in the best and least fortunate stations, respectively, in 8.1 (for Maan) and 10.9 (for Ghor El Safi) years.

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Figure 7. Payback time evaluation for each station for a hybrid gas or diesel boiler with ETSW

In the second case, the thermal energy needed to meet the building's heating needs was provided by a diesel boiler hybrid system with ETSW. The payback time results are displayed in Figure 7 and assume a 90% efficiency for the diesel boiler and a fuel cost of $0.18 per kWh according to the average diesel price in Jordan in 2022.

Like the first case, calculations revealed that the payback times for Maan (best) and Gore El Safi (worst) are 14.9 and 19.7 years, respectively. When the first and second cases are compared, it becomes clear that using a gas boiler makes expenditures in the usage of ETSW to meet the building's heating needs more appealing.

The payback period for the hybrid heat pump and ETSW system is long, equaling more than 50 years in Maan station, due to the high cost of the heat pump system and Jordan's relatively affordable electricity pricing. This enormous gap between costs and savings indicates how difficult it will be for the nation to implement sustainable energy alternatives. The long payback period for the hybrid heat pump and ETSW system can deter people and businesses from making the necessary up-front investments, even though they promise improved energy efficiency and diminished environmental impact in the long run.