**2. Literature Review**

GIS is widely applied in site selection issues. Xu et al. [41] used GIS, Interval Analytic Hierarchy Process (IAHP), and stochastic VIKOR to find the best area for wind farms in the Wafangdian region, China. Colak et al. [42] also employed GIS and AHP to find an optimal area for a photovoltaic farm in Malatya, Turkey. Castro-Santos et al. [43] studied Galicia coastal area in Spain for floating offshore wind farm site selection via GIS. In addition to its application in selecting optimal areas for renewable energy farms, GIS is used for rainwater harvesting [44], power plants [45], landfills [46], pressurized irrigation [47], and electric vehicle charging stations [48] site selection.

Many articles have been published related to wind farm site selection using GIS in Iran. Moradi et al. [38] measured wind energy potential in Alborz province (central regions of Iran) through MCDM and GIS. They used AHP to weigh the criteria, and according to their results, 20% of the area was suitable for wind farms. Noorollahi et al. [6] conducted the same work for Markazi province (west of Iran), and based on the results, 28% of the area was suitable for wind farms. In 2020, in a study by Ahmadi et al. [49], different parts of Iran were reviewed to build a wind-powered pump storage plant, and according to the results, the Gilane-Gharb dam was the best area and the capacity of which was estimated at 31 MW.

Table 1 represents several other studies on wind farm site selection in Iran and other countries.


**Table 1.** Other renewable resources site selection research in Iran and other countries.

One of the main practical uses of the decision-making systems is to improve risk assessment ability and overcome the adverse effects [58] of the site-selection projects. Multidimensional assessment and making accurate decisions are vital prerequisites to starting a business. Moreover, location selection is one of the essential parts of the business due to the long-term impacts on risks and costs of the projects [59]. Furthermore, as another practical benefit of decision-making systems, the simultaneous use of GIS and MCDM reduce the cost and time of the site-selection problems and increases the accuracy. At the same time, GIS-MCDM-based decision-making systems take multiple environmental, social, economic, and sustainability parameters to account to make the best decision among the various alternatives [60].

This research aimed to determine the suitable area for wind farms in Semnan province, Iran. Semnan has the lowest population density among the provinces of Iran and is also the seventh province in terms of area. Moreover, due to unfavorable climatic conditions, the possibility of agriculture is less in many parts of Semnan province, such as the southern areas. Therefore, there is much usable land in many parts of the province. Semnan province is also one of the central provinces of Iran, located near the capital (Tehran) and other large provinces, such as Khorasan and Isfahan. The proximity of the province to energy highways and energy consumption centers increases the importance of this strategic province for energy production. The construction of fossil power plants, such as steam, combined cycle power, and plants, seems very irrational due to high water consumption, pollution, and contradiction with the hot and dry climate of the province. For the above reasons, renewable sources, namely wind energy, seem a very reasonable and justifiable option. Criteria and sub-criteria were specified to evaluate the wind farm potential in the province. To solve this decision-making problem, AHP will be applied to weigh and compare the criteria. The main selected criteria for this study are wind speed, slope, power lines, power stations, urban areas, highways, and roads. These criteria were chosen according to similar previous studies and the opinion of the experts. Finally, areas with the most potential for the wind farm will be specified separately. In Section 3, the study area, electricity consumption, and social information of the Semnan are described. In Section 4, the AHP method is presented, and the weights of the criteria will be calculated. In Section 5, the weighted criteria and multiple restrictions will be applied. In Section 5, the final categorized map will be presented using the data of previous sections, and the results will be discussed and compared with other papers.

#### **3. Study Area**

Semnan is one of the central provinces of Iran, located in the east of Tehran province, south of the Alborz mountains, and north of Dashte-e kavir. This province is centered in Semnan city, and Shahrood, Garmsaar, and Damqaan are the other important cities

of Semnan province (Figure 2). The province covers an area of 97,491 square kilometers, which is 5.9% of the country's total area. This province is the seventh province in Iran in terms of area. In the last official census in 2016, the province's population was 702,000, and the relative population density was 2.7 people per km2. This vast province is home to less than one percent of the country's population [61].

**Figure 2.** Location of Semnan in Iran map.

With an average daily temperature of 24 ◦C and a maximum temperature of 39 ◦C, Semnan is considered a warm province. The best months to travel to Semnan are June to September, and the worst months are November to March [62].

Iran's electricity grid has expanded considerably in recent decades, and most of the electricity generation in this grid is provided by fossil thermal power plants. According to the pattern obtained in the last decade, electricity consumption in Iran is increasing by 6% annually. Figure 3 depicts the trend of the increase in electricity production and consumption in Iran's electricity grid from 1980 to the last decade. According to this figure, the electricity network will face several problems in supplying electricity in the near future.

Semnan has two large power plants named Shahid Bakeri and Shahid Bastani, whose net production in 2018 was equal to 2,433,779 MWh. In 2018, out of 377,050 electricity subscribers in Semnan province, 76.88% were household, 13.69% commercial, 1.62% agricultural, 5.88% general, 1.32% industrial, and 0.58% street lighting. Moreover, 20.19% of the total electricity sold was for household consumption, 8.69% for general consumption, 23.28% for agricultural consumption, 40.3% for industrial consumption, 5.21% for commercial consumption, and 2.33% was allocated to street lighting. Figure 4 demonstrates the amount of electricity sold to various subscribers in 2018 [64,65].

Based on the annual consumption pattern, electricity consumption in Semnan is growing every year, and wind resources can be a key to meeting this demand.

**Figure 3.** Electricity production and consumption growth in Iran (based on the data from [38,63]).

**Figure 4.** Electricity consumption of different groups of subscribers in Semnan [64,65].
