# Ranking Locations for Hydrogen Production Using Hybrid Wind-Solar: A Case Study

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## Abstract

**:**

## 1. Introduction

_{2}O), and hydrocarbon compounds like methane (CH

_{4}), coal, and oil, which are combinations of hydrogen with carbon.

## 2. Review of Literature

^{2}per day, and 2800 sunny hours per year, Iran could be an important solar power generation hub in West Asia and the world. It has been reported that, on average, Iran has approximately 700 sunshine hours in spring, 1050 h in summer, 830 h in autumn, and 500 h in winter [14].

^{2}in Vojvodina (one of the provinces of Serbia) is suitable for the construction of a wind farm. The ranking performed with the MABAC method showed a place near the village of Laudonovac to be the best location for building a wind farm in Vojvodina.

^{2}. Ranking conducted with VIKOR, OCRA, TOPSIS, and OWA methods also produced similar results. This study also demonstrated the good capability of the combined GIS-MCDM method in finding suitable places for wind farms.

## 3. Study Area

^{2}, which is approximately 12.5% of the total land area of Iran and makes it the fourth largest Iranian province. The neighboring provinces of Fars are Isfahan to the north, Yazd to the northeast, Kerman to the east, Hormozgan to the south, and Bushehr to the west, all of which have hot and dry climates. Figure 2 shows the location of these provinces in Iran. The exact location (longitude and latitude) of cities in this area is given in Table 1.

## 4. Methodology

#### 4.1. Multi-Criteria Decision-Making Methods

#### 4.1.1. ARAS

- Step 1:
- Forming the decision matrix (a matrix whose rows are decision alternatives and whose columns are evaluation criteria).
- Step 2:
- Determining the hypothetical ideal value (the highest value for positive criteria and the lowest value for negative criteria).
- Step 3:
- Normalizing the decision matrix using the linear method.
- Step 4:
- Weighting the decision matrix by multiplying the criteria weights of the decision matrix by the normalized criteria values using Equation (1).

- Step 5:
- Computing the total utility. In this step, the weighted normalized value should be summed in rows according to Equation (2). Here, the highest obtained ${S}_{i}$ value is the best, and the lowest ${S}_{i}$ value is the worst. The optimality function ${S}_{i}$ has a direct relationship with the ${x}_{ij}$ values and criteria weights ${w}_{j}^{*}$ and their relative impact on the final result. Thus, the variable with the highest optimality function value ${S}_{i}$ is the most effective. The superiority of alternatives can therefore be determined according to their ${S}_{i}$ value. This is why this method is known to perform very well in the evaluation and ranking of decision options [31].$${S}_{i}={\displaystyle \sum}_{j=1}^{n}{r}_{ij}$$
- Step 6:
- Computing the relative utility. The degree of utility of each alternative is important not only for identifying the best option, but also for determining the relative quality (desirability) of lower-ranked options. In this step, Equation (3) is used to compute the degree of utility of each alternative through a comparison with an analyzed variable, which is determined by the ideal state, i.e., ${S}_{0}$ [29].$${K}_{i}=\frac{{S}_{i}}{{S}_{0}},i=\left\{0,\dots ,m\right\}$$

#### 4.1.2. SAW

- Step 1:
- Forming the decision matrix.
- Step 2:
- Obtaining the normalized decision matrix using the linear norm method.
- Step 3:
- Obtaining the weight matrix.
- Step 4:
- Determining the best alternative using Equation (4) [33]:

#### 4.1.3. CODAS

- Step 1:
- Forming the decision matrix
- Step 2:
- Normalizing the decision matrix using the linear method.
- Step 3:
- Forming the weighted normalized decision matrix by multiplying the criteria weight by the normalized matrix using Equation (5) [36]:$${r}_{ij}={n}_{ij}{w}_{j}$$
- Step 4:
- Computing the negative ideal point in terms of each criterion, which is denoted by $n{s}_{j}$, as shown in Equation (6):$$n{s}_{j}=min{r}_{ij}$$
- Step 5:
- Computing Euclidean and Taxicab distances from the negative ideal using Equations (7) and (8), respectively [34]$${\mathit{E}}_{\mathit{i}}=\sqrt{{\displaystyle \sum}_{\mathit{j}=\mathbf{1}}^{\mathit{n}}{\left({\mathit{r}}_{\mathit{i}\mathit{j}}-\mathit{n}{\mathit{s}}_{\mathit{j}}\right)}^{\mathbf{2}}}$$$${\mathit{T}}_{\mathit{i}}={\displaystyle \sum}_{\mathit{j}=\mathbf{1}}^{\mathit{n}}\left|{\mathit{r}}_{\mathit{i}\mathit{j}}-\mathit{n}{\mathit{s}}_{\mathit{j}}\right|$$
- Step 6:
- Forming the relative evaluation matrix using Equation (9) [35].$${h}_{ik}=\left({E}_{i}-{E}_{k}\right)+\left(\psi \left({E}_{i}-{E}_{k}\right)\times \left({T}_{i}-{T}_{k}\right)\right)k=\left\{1,\dots ,n\right\}$$

- Step 7:
- Ranking the alternatives by summing their ${h}_{ik}$ values using Equation (11). The larger the ${H}_{i}$ value, the better the rank of the alternative [34].$${H}_{i}={\displaystyle \sum}_{k=1}^{n}{h}_{ik}$$

#### 4.1.4. TOPSIS

- Step 1:
- Forming the decision matrix.
- Step 2:
- Obtaining the normalized decision matrix using the vector norm method.
- Step 3:
- Creating the weight matrix based on one of the weighting methods.
- Step 4:
- Forming the weighted normalized matrix using Equation (12)

- Step 5:
- Determining the positive ideal solution ${\mathit{V}}_{\mathit{j}}^{+}$, which is the largest value for positive criteria and the smallest value for negative criteria (i.e., the best value for each criterion), and also determining the negative ideal solution ${\mathit{V}}_{\mathit{j}}^{-}$, which is the smallest value for positive criteria and the largest value for negative criteria (i.e., the worst value for each criterion).
- Step 6:
- Computing the Euclidean distance of each alternative from the positive and negative ideals using Equations (13) and (14) [8]:

- Step 7:
- Determining the relative closeness of each alternative to the ideal using Equation (15) [33]:$$\mathit{C}{\mathit{L}}_{i}^{*}=\frac{{\mathit{d}}_{\mathit{i}}^{-}}{{\mathit{d}}_{\mathit{i}}^{-}+{\mathit{d}}_{\mathit{i}}^{+}}$$
- Step 8:
- Ranking the alternatives in descending order of their $\mathit{C}{\mathit{L}}_{\mathit{i}}^{*}$ values.

#### 4.1.5. Aggregation Methods

#### Rank Averaging

#### Borda Method

#### Copeland Method

#### 4.2. Decision Criteria

#### 4.2.1. Solar Energy Potential

#### 4.2.2. Wind Energy Potential

#### 4.2.3. Electrolyzer

#### 4.3. Criteria Weighting

#### Best—Worst Method (BWM)

- Step 1:
- Specifying the set of criteria that will be used in the decision-making (c
_{1}, c_{2}, …, c_{n}). - Step 2:
- Identifying the best (most preferable) and worst (least preferable) criteria (in this step, the decision-maker generally specifies the best and worst criteria without making any comparison).
- Step 3:
- Determining the preference of the best criterion over other criteria on a scale between 1 and 9, with 1 indicating equal preference and 9 indicating complete superiority. The result of this vector comparison should be in the form of Equation (30).

- Step 4:
- Determining the preference of all criteria over the worst criterion on a scale between 1 and 9, with 1 indicating equal preference and 9 indicating complete superiority. The result of this vector comparison will be in the form of Equation (31).

**j**relative to the worst criterion

**w**.

- Step 5:
- Finding the optimal weights. The importance weight of criteria should be obtained in the format of $\left({\mathit{w}}_{\mathbf{1}}^{*},{\mathit{w}}_{\mathbf{2}}^{*},\dots ,{\mathit{w}}_{\mathit{n}}^{*}\right)$. This vector must be defined in such a way that for each criterion
**j**, $\frac{{\mathit{w}}_{\mathit{B}}}{{\mathit{w}}_{\mathit{j}}}={\mathit{a}}_{\mathit{B}\mathit{j}}$ and $\raisebox{1ex}{${\mathit{w}}_{\mathit{j}}$}\!\left/ \!\raisebox{-1ex}{${\mathit{w}}_{\mathit{w}}$}\right.={\mathit{a}}_{\mathit{j}\mathit{w}}$. Thus, to meet the mentioned conditions, the terms $\left|\frac{{\mathit{w}}_{B}}{{\mathit{w}}_{\mathit{j}}}-{\mathit{a}}_{\mathit{B}\mathit{j}}\right|$ and $\left|\frac{{\mathit{w}}_{\mathit{j}}}{{\mathit{w}}_{\mathit{w}}}-{\mathit{a}}_{\mathit{j}\mathit{w}}\right|$ should be minimum. Additionally, since it is assumed that the importance weights are non-negative and sum up to 1, the problem can be expressed as the mathematical programming problem of Equation (32) [46]:$$\mathit{Min}\mathit{max}\left\{\left|\frac{{\mathit{w}}_{\mathit{B}}}{{\mathit{w}}_{\mathit{j}}}-{\mathit{a}}_{\mathit{B}\mathit{j}}\right|,\left|\frac{{\mathit{w}}_{\mathit{j}}}{{\mathit{w}}_{\mathit{w}}}-{\mathit{a}}_{\mathit{j}\mathit{w}}\right|\right\}\phantom{\rule{0ex}{0ex}}\mathit{s}\mathit{t}.{\displaystyle \sum}_{\mathit{j}=\mathbf{1}}^{\mathit{n}}{\mathit{w}}_{\mathit{j}}=\mathbf{1}\phantom{\rule{0ex}{0ex}}{\mathit{w}}_{\mathit{j}}\ge \mathbf{0},\forall \mathit{j}$$

_{k}defined such that ${{\displaystyle \sum}}_{k=1}^{K}{\lambda}_{k}=1$, and their opinion about the preference of the j-th criterion relative to the best and worst criteria are ${a}_{Bj}^{k}$ and ${a}_{jw}^{k}$, respectively, then their opinions can be aggregated using Equations (37) and (38) [45].

## 5. Analysis

^{2}, respectively. Jahrom has the lowest potential for wind energy with 15 W/m

^{2}. Figure 3b shows the information map of the solar radiant energy potential that per square meter of the solar panel in the region can obtain. Darab, Fasa, and Bavanat have the highest solar energy radiation of 6.03, 5.94, and 5.91, respectively. Figure 3c shows the average temperature in the studied cities of Fars province. Northern cities, which are mostly mountainous, have lower average temperatures than southern cities. Temperature is considered as a negative factor due to its negative role in hydrogen production and storage processes. Figure 3e is the digital elevation map (DEM) that shows the elevation of each point above sea level. North of Fars province is a mountainous region with an average altitude of 3000 m, and moving to the south, the elevation decreases. Figure 3d is the map for distribution of population in the study area. Shiraz, with a population of 1,869,000, is the most populous city in Fars province. The cities of Kazerun, and Jahrom with 266,217 and 228,532 people, respectively, are the most populous cities in the study area after Shiraz. Figure 3f shows the distribution of the probability of natural disasters of floods and earthquakes in the region. This factor has a negative effect on attracting investors due to the risk of damaging the equipment. Figure 3g shows the cost criteria in the form of a map. Costs include the cost of providing land, infrastructure, and the cost of providing human resources. Shiraz, Firozabad, and Neyriz rank first to third in this criterion. Locations with less investment cost attract more investors. Fars province, because it is close to the sea, is affected by the humidity of the Persian Gulf. Southern cities are most affected by this situation. Figure 3h shows the humidity of each city in the study area. Humidity is considered a negative factor in the ranking due to the negative impact on equipment. Figure 3i shows the distribution of the main communication roads in the area. Shiraz is the main connection point of these roads due to its central location. Among the cities, Izad Khast has the least access to the main roads. According to experts, this criterion is one of the effective factors in investors’ decisions. Due to the urban and environmental constraints mentioned in the Table 2, the number of suitable lands is limited. Figure 3j shows the number of these places for each city, for which Izad Khast and Neyriz have the highest value.

#### Hydrogen Production Potential

_{2}emission reduction in kilograms [46]. This conversion is done by computing the greenhouse gas production equivalent of the amount of gasoline to be saved by using hydrogen as fuel. It should be noted that in the case of solar energy, a unit area (m

^{2}) refers to the horizontal surface on the ground, but in the case of wind energy, it refers to the area covered by turbine blades at a height of 40 m. Figure 4 shows the potential for renewable hydrogen production in the study area.

## 6. Conclusions

_{2}, many countries have begun to think about securing clean, sustainable, and renewable energy sources. There are, however, some limitations to the use of renewable energy sources, such as low reliability. For example, solar energy is not available at all times, and wind does not blow at a reliably constant speed. One way to resolve this problem is to embed a hydrogen production unit into these renewable energy systems. This study investigated and compared the suitability of 15 cities in Fars province, Iran, for renewable hydrogen production. First, the evaluation criteria were determined through library research, review of previous studies, and consultation with experts. These criteria included solar energy potential, regional wind potential, population, air temperature, natural disasters, altitude, relative humidity, cost items, topography, and distance from main roads. After using a new method called BWM to weight the criteria, the cities were ranked using the ARAS method. SAW, CODAS, and TOPSIS methods were then used for validation. The results of these methods were aggregated by three methods, vis. rank averaging, Borda technique, and Copeland technique. Finally, the partially ordered set ranking technique was used to reach a final ranking based on the consensus approach. The cities of Izadkhast, Safashahr, and Bavanat were identified as the top three choices by the MCDM methods as well as the ranking aggregation methods. The final ranking also had the same cities in the top three places. It should be noted that the obtained ranking is dependent on the criteria weights obtained from BWM. In other words, the suitability of considered places for the construction of wind–solar hybrid power plants very much depend on solar and wind energy potential as well as population, air temperature, natural disasters, altitude, relative humidity, costs, topography, and distance from main roads. Overall, the results demonstrated that in any effort to build a wind–solar hybrid power plant in Fars province, higher priority should be given to the cities of Izadkhast, Safashahr, and Bavanat than other parts of this region. This study also considered the cost of land, labor, and infrastructure required for the construction of a hybrid power plant. In terms of this criterion, the cities of Shiraz and Izdakhast were found to be, respectively, the least and most suitable (most and least expensive) choices in the study area. Future studies are recommended to rank the regions of Fars province or other provinces in terms of suitability for the use of geothermal energy or bioethanol, use other new MCDM methods such as WASPAS, COPRAS, and MABAC for this purpose, and investigate the possibility of using the electricity generated by the incineration of municipal waste for renewable hydrogen production with the collaboration of individual experts in related fields including mechanical engineering, electrical engineering, and natural resource engineering or panels of these experts.

## Author Contributions

## Funding

## Institutional Review Board Statement

## Informed Consent Statement

## Data Availability Statement

## Conflicts of Interest

## Nomenclature

$N$ | Normalized decision matrix |

${n}_{ij}$ | The characteristic value of the i-th option relative to the j-th criterion |

${W}_{j}^{*}$ | Weight vector |

${w}_{j}$ | Criterion weight of j-th |

${r}_{ij}$ | Normalized weighted matrix |

${v}_{ij}$ | Normalized weighted matrix elements |

${S}_{i}$ | The Optimality Function value for the ith alternative in ARAS method |

${S}_{0}$ | The ideal mode in ARAS |

${K}_{i}$ | The degree of desirability of each option in ARAS |

${A}^{*}$ | The most suitable option in SAW |

${E}_{i}$ | Euclidean distance |

${T}_{i}$ | Taxicab distance |

$\tau $ | Threshold parameter in CODAS |

${h}_{ik}$ | Relative evaluation matrix |

${H}_{i}$ | Ranking |

${v}_{j}^{+}$ | The positive ideal solution |

${v}_{j}^{-}$ | The negative ideal solution |

${d}_{i}^{-}$ | Distance from the negative ideal solution |

${d}_{i}^{+}$ | Distance from the positive ideal solution |

$C{L}_{i}^{*}$ | The relative closeness of each alternative to the ideal solution |

$H$ | Average energy radiation reaching a horizontal surface |

${H}_{0}$ | Average energy radiation reaching a horizontal surface in clear, cloudless conditions |

$A$ | Angstrom coefficient |

$B$ | Angstrom coefficient |

$n$ | Average daily hours of sunshine |

$N$ | The average number of possible hours of sunshine per day |

$d$ | Julius number of the day of the year |

${I}_{SO}$ | Solar constant |

$\phi $ | Latitude of the place |

$\delta $ | The sun declination angle |

$\omega $ | The sunset hour angle |

${V}_{2}$ | Wind speed at the desired height |

${V}_{1}$ | Wind speed available |

${h}_{2}$ | The desired height |

${h}_{1}$ | Current height |

$\alpha $ | Power law index |

$f\left(v\right)$ | Probability density function |

V | Wind speed |

$\rho $ | Ambient air density |

$\overline{P}$ | Ambient air pressure in Pascal units |

$\overline{T}$ | Average air temperature in Kelvin |

${R}_{d}$ | Gas constant for dry air |

C | Scale parameter |

k | Shape parameter |

${M}_{{H}_{2}}$ | Weight of hydrogen gas produced in kilograms |

${E}_{{H}_{2}}$ | Energy produced by renewable energy sources |

$LH{V}_{{H}_{2}}$ | The low calorific value of hydrogen in kilowatt hours per kilogram |

${\eta}_{1}$ | Electrolyzer system efficiency |

${\eta}_{2}$ | Extra efficiency factor to take into account the energy lost in the electrolyzer |

${a}_{Bj}$ | Indication of the performance of the best criterion B compared to the criterion j-th |

${a}_{jw}$ | Indication of the performance of j relative to the worst criterion w |

${\epsilon}^{*}$ | Optimal value of BWM method |

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**Figure 1.**Production–consumption cycle of hydrogen from renewable energies [13].

No | Cities | Latitude N | Longitude E |
---|---|---|---|

1 | Shiraz | 29°37′ | 52°32′ |

2 | Kazerun | 29°38′ | 51°39′ |

3 | Fasa | 28°55′ | 53°39′ |

4 | Abadeh | 31°18′ | 52°67′ |

5 | Bavanat | 30°28′ | 53°27′ |

6 | Arsanjan | 29°92′ | 53°32′ |

7 | Sepidan | 30°15′ | 51°58′ |

8 | Nayriz | 29°12′ | 54°20′ |

9 | Eqlid | 30°53′ | 52°41′ |

10 | Firuzabad | 28°81′ | 52°55′ |

11 | Safa Shahr | 30°36′ | 53°11′ |

12 | Estahban | 29°12′ | 54°03′ |

13 | Izad Khast | 31°08′ | 52°40′ |

14 | Darab | 28°75′ | 54°55′ |

15 | Jahrom | 28°30′ | 53°33′ |

Constraint | Objective |
---|---|

A distance of 1000 m from residential areas | Protecting the safety of residents |

A distance of 1000 m from water reservoirs, streams, forests, and protected areas | Protecting natural resources |

A distance of 250 m from the road network A distance of at least 250 m from the power grid A distance of 500 m from the railway network A distance of 2000 m from airports | Infrastructure constraints |

No. | Criterion | Type |
---|---|---|

1 | Solar energy potential | Positive |

2 | Wind energy potential | Positive |

3 | Mountain-heights | Negative |

4 | Temperature | Negative |

5 | Distance from main roads | Negative |

6 | Costs | Negative |

7 | Natural disasters | Negative |

8 | Population | Positive |

9 | Topography (features) | Negative |

10 | Relative humidity | Positive |

No | Cities | ARAS | SAW | CODAS | TOPSIS | Average Ratings Ranking | Borda | Copland |
---|---|---|---|---|---|---|---|---|

1 | Shiraz | 3 | 13 | 5 | 6 | 6 | 6 | 6 |

2 | Kazerun | 13 | 14 | 12 | 13 | 13 | 12 | 12 |

3 | Fasa | 12 | 10 | 14 | 12 | 12 | 12 | 12 |

4 | Abadeh | 9 | 7 | 10 | 8 | 8 | 8 | 8 |

5 | Bavanat | 4 | 3 | 3 | 3 | 3 | 3 | 3 |

6 | Arsanjan | 5 | 5 | 4 | 4 | 4 | 4 | 4 |

7 | Sepidan | 7 | 6 | 7 | 7 | 7 | 7 | 7 |

8 | Nayriz | 8 | 8 | 11 | 10 | 9 | 11 | 11 |

9 | Eqlid | 6 | 4 | 6 | 5 | 5 | 5 | 5 |

10 | Firuzabad | 10 | 11 | 8 | 11 | 11 | 10 | 10 |

11 | Safa Shahr | 2 | 2 | 2 | 2 | 2 | 2 | 2 |

12 | Estahban | 11 | 9 | 9 | 9 | 10 | 9 | 9 |

13 | Izad Khast | 1 | 1 | 1 | 1 | 1 | 1 | 1 |

14 | Darab | 14 | 12 | 13 | 14 | 14 | 12 | 12 |

15 | Jahrom | 15 | 15 | 15 | 15 | 15 | 13 | 13 |

No | Cities | Ranking |
---|---|---|

1 | Izad Khast | 1 |

2 | Safa Shahr | 2 |

3 | Bavanat | 3 |

4 | Arsanjan | 4 |

5 | Eqlid | 5 |

6 | Shiraz | 6 |

7 | Sepidan | 7 |

8 | Abadeh | 8 |

9 | Estahban, Firuzabad, Nayriz | 9 |

10 | Fasa. Kazerun, Darab, Jahrom | 10 |

No | Cities | Available Wind Energy (kWh/m ^{2}.yr) | Produced Hydrogen from Wind (kg/m ^{2}.yr) | Available Solar Energy (kWh/m ^{2}.yr) | Produced Hydrogen from—Solar (kg/m ^{2}.yr) | Produced Hydrogen from Wind and Solar (kg/m ^{2}.yr) | Equivalent to Gasoline (Liter) | Equivalent to CO_{2} Emission (kg/yr) |
---|---|---|---|---|---|---|---|---|

1 | Shiraz | 2137.1 | 38.5 | 677.8 | 12.2 | 50.7 | 141.9 | 452.5 |

2 | Kazerun | 2091.7 | 37.7 | 782.5 | 14.1 | 51.7 | 144.9 | 462.0 |

3 | Fasa | 2172.9 | 39.1 | 599.9 | 10.8 | 49.9 | 139.7 | 445.7 |

4 | Abadeh | 2098.6 | 37.8 | 863.4 | 15.5 | 53.3 | 149.3 | 476.1 |

5 | Bavanat | 2161.2 | 38.9 | 1271.9 | 22.9 | 61.8 | 173.0 | 551.8 |

6 | Arsanjan | 2139.6 | 38.5 | 1303.3 | 23.5 | 62.0 | 173.5 | 553.4 |

7 | Sepidan | 2093.5 | 37.7 | 956.0 | 17.2 | 54.9 | 153.7 | 490.2 |

8 | Nayriz | 2185.0 | 39.3 | 904.8 | 16.3 | 55.6 | 155.7 | 496.6 |

9 | Eqlid | 2097.9 | 37.8 | 1059.2 | 19.1 | 56.8 | 159.1 | 507.5 |

10 | Firuzabad | 2113.7 | 38.0 | 913.9 | 16.5 | 54.5 | 152.6 | 486.6 |

11 | Safa Shahr | 2088.4 | 37.6 | 1257.2 | 22.6 | 60.2 | 168.6 | 537.8 |

12 | Estahban | 2149.9 | 38.7 | 952.3 | 17.1 | 55.8 | 156.4 | 498.6 |

13 | Izad Khast | 2089.1 | 37.6 | 1469.1 | 26.4 | 64.0 | 179.3 | 571.9 |

14 | Darab | 2208.4 | 39.8 | 541.3 | 9.7 | 49.5 | 138.6 | 442.0 |

15 | Jahrom | 2137.1 | 38.5 | 495.9 | 8.9 | 47.4 | 132.7 | 423.2 |

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## Share and Cite

**MDPI and ACS Style**

Almutairi, K.; Mostafaeipour, A.; Jahanshahi, E.; Jooyandeh, E.; Himri, Y.; Jahangiri, M.; Issakhov, A.; Chowdhury, S.; Hosseini Dehshiri, S.J.; Hosseini Dehshiri, S.S.;
et al. Ranking Locations for Hydrogen Production Using Hybrid Wind-Solar: A Case Study. *Sustainability* **2021**, *13*, 4524.
https://doi.org/10.3390/su13084524

**AMA Style**

Almutairi K, Mostafaeipour A, Jahanshahi E, Jooyandeh E, Himri Y, Jahangiri M, Issakhov A, Chowdhury S, Hosseini Dehshiri SJ, Hosseini Dehshiri SS,
et al. Ranking Locations for Hydrogen Production Using Hybrid Wind-Solar: A Case Study. *Sustainability*. 2021; 13(8):4524.
https://doi.org/10.3390/su13084524

**Chicago/Turabian Style**

Almutairi, Khalid, Ali Mostafaeipour, Ehsan Jahanshahi, Erfan Jooyandeh, Youcef Himri, Mehdi Jahangiri, Alibek Issakhov, Shahariar Chowdhury, Seyyed Jalaladdin Hosseini Dehshiri, Seyyed Shahabaddin Hosseini Dehshiri,
and et al. 2021. "Ranking Locations for Hydrogen Production Using Hybrid Wind-Solar: A Case Study" *Sustainability* 13, no. 8: 4524.
https://doi.org/10.3390/su13084524