Elsevier

Renewable Energy

Volume 136, June 2019, Pages 1130-1146
Renewable Energy

An integrated GIS-based Ordered Weighted Averaging analysis for solar energy evaluation in Iran: Current conditions and future planning

https://doi.org/10.1016/j.renene.2018.09.090Get rights and content

Highlights

  • A new model is used to evaluate the solar energy in Iran.

  • To evaluate solar energy potential a risk analysis is added to GIS-based method.

  • An Order Weight Averaging is utilized for first time in solar energy assessment.

  • A location analysis is done for currently working solar plants.

  • A maximum of 498 W/m2 solar radiation is achievable yearly.

Abstract

Solar energy is one of the important energy sources and countries have realized the important role of renewable energies due to the depletion of conventional energy sources. In this study, a GIS-based analysis is utilized for investigating the feasibility of solar energy in Iran. To evaluate the concept of risk into the GIS-based analysis for determining optimal areas for installation of solar power plants an Ordered Weighted Averaging (OWA) approach is used for the first time. Integration of OWA-based approach and GIS analysis provide models that determine the priority of regions from risk-free decision to risky decision strategies. The results show that Kerman, Yazd, Fars, Khuzestan, Sistan and Baluchistan, South Khorasan and Isfahan provinces have a good capacity to invest in solar energy projects. The GIS-based analysis indicates that the values of installed solar power plants percentages in high chosen areas for the most pessimistic and optimistic strategies are 7% and 64%, respectively.

Introduction

Consuming Energy is crucial for economic development. The life in the developed countries is associated with burning fuels that produce the pollution. Therefore, one of the most important effects of fossil fuels is the pollution and environmental problems [1]. Recent studies show that the overall CO2 level has increased by 31% in a couple of centuries ago (Fig. 1a.). Furthermore, the mean surface temperature has increased 0.8-degree Celsius in the past century (Fig. 1b.). Consequently, this effect will increase the sea level. For example, Arctic sea ice is thinned by 40% since the 1950s [2,3].

Renewable energies are clean, sustainable and without any pollution, however, there are some co2 productive processes in the preparing the renewable energies systems [4]. Therefore, a big movement is raised by the governments to replace the fossil fuels with renewable energies [5]. Currently, 14% of the world energy request has been prepared by renewable energies [6,7]. It is estimated that 47.7% of total world energy demand will be produced by renewable energies. The rate of renewable energies contribution is shown in Fig. 2 [8].

Solar energy is one of the most interesting renewable sources [4,5,9,10]. Solar energy usage is increasing to use more renewable energies in the world [11]. Total solar power installation in 2010 was equal to 15 Gigawatts while this total installation is 2.7 Gigawatts in 2006 [12]. After a 44% increase in PV installation in 2011, global total installation reached about 106 GW [13]. The top countries in PV based power are Japan, Germany, the UK, China, Spain and Italy [14]. The best locations with the high possibility of installing solar power systems are in Mediterranean regions, Australia, Middle East, south-west of USA, China, and Indian desert parts [15]. Specifically for Iran a country that is located in the Middle East region, there is a good solar energy possibility with 280 sunny days annually [9,15,16]. The first photovoltaic power plant of Iran has been built in Shiraz in 2008, with the capacity of 250 kW. The total capacity of the photovoltaic power plants until 2015 was less than 5 MW. Following the new supportive policies by the government in 2016 to guarantee solar power production purchase, the installed capacity increased to more than 45 MW in 2018. A total of 30 MW of this capacity have been exploited in 2017, indicating Iran's welcome to the construction of photovoltaic (PV) systems [17,18]. However, the installed capacity has not reached its ideal level. Therefore, there are some plans to increase the amount of electricity generated from PV systems. Considering the vast area of Iran and the different environmental conditions, one of the most important challenges for investors in the field of solar energy is determining the suitable location for the construction of the power plant.

One of the useful tools to model and forecast the renewable energies sources over a region is Geographic Information Systems (GIS) based methods. Among the GIS related techniques, GIS-based Multi-Criteria Decision Analysis (MCDA) integrated methods contain physical information, criteria weights, and an MCDA operator. This operator mix spatial information and criteria weights together [19,20]. A number of studies have been focused on the application of MCDA in the renewable energy resource assessment. Huang et al. presented different MCDA models such as Analytical Hierarchy Process (AHP), ELimination and choice Translating Reality (ELECTRE), The Technique for Order Preference by Similarity to Ideal Solutions (TOPSIS) to conduct an analysis of the energy applications [21]. Beccali et al. used the MCDA to assess the different energy alternatives in Italy [22]. Haralambopoulos and Polatidis applied the Preference Ranking Organization METHod for Enrichment of Evaluations (PROMETHEE) II to investigate and assess the exploitation of a geothermal energy source in Greece [23]. Pohekar and Ramachandran presented various MCDA model such as PROMETHEE, ELECTRE, TOPSIS, Multi-Attribute Utility Theory (MAUT) to energy planning [24]. Eunnyeong et al. used the fuzzy AHP model to study a renewable energy program [25]. Cavallaro (2010) used the Fuzzy TOPSIS model for assessing thermal-energy storage in a concentrated solar power system [26].

Recently, many researchers have used GIS-MCDA methods to select the suitable place for renewable energy-based plant [[26], [27], [28], [29], [30], [31], [32], [33], [34], [35]]. Particularly for solar energy, Beccali et al. used the ELECTRE III technique to evaluate the active program for the development of solar energy [36]. Carrion et al. presented an environmental decision support system to select the suitable site of grid-connected PV power plants. MCA and AHP were used to select the suitable sites in the GIS environment. In this study, Environmental, Geology, proximity, accessibility, and climate criteria are used to select the suitable PV sites [37]. Uyan integrated the MCA and AHP to find the good location in Turkey. As a result, 13.92% of the study area was in the very suitable class and 40.34% of the area was not suitable [38]. Sánchez-Lozano et al. developed a framework, which integrated GIS, AHP and TOPSIS models, to select the suitable PV sites. The weights of the different criteria were calculated by the AHP, and the ranking of the PV sites was obtained by applying the TOPSIS model [39]. Aragonés-Beltrán et al. presented a three-stage MCDA model to select the suitable PV sites. In this model, the capabilities are AHP and Analytic Network Process (ANP) combined with each other [40]. Wu et al. proposed a three-phase framework to select the suitable PV sites. In the first phase, potentially feasible sites were identified based on different criteria such as energy, infrastructure, land, and environmental and social factors. In the second phase, the weights of the different criteria were calculated by fuzzy measures. In the third phase, the PV sites were ranking with Linguistic Choquet Integral (LCI) [41]. Sánchez-Lozano et al. developed a hybrid model to select the suitable PV sites. GIS was applied to limit the location alternatives, and the AHP was used to calculate the weights of the effective criteria. Then, the PV sites were ranking with the fuzzy TOPSIS model [42]. Sindhu et al. prepared the combination of AHP and fuzzy TOPSIS to select the suitable place in India [43]. There are a number of studies for site selection in Iran based on different techniques. However, all these studies have been done for a specific region in Iran or provincial focused [[44], [45], [46], [47]].

In the Middle East region, Iran has the second largest energy resources [15]. However, the latest Iranian energy balance sheets showed a balance between supply and demand of electric power, but this success in power generation was mainly based on the fossil energy sources. Thereafter, the Iranian government has planned to replace the fossil fuels contribution with renewable sources [48,49]. The current status and prospects of exploiting solar energy in Iran are discussed in Ref. [15]. However, the potential of solar energy in Iran is high but due to economical and technical constraints, the renewable energy market in Iran is still in its early stages, and Iran energy supply is heavily dependent on fossil fuels. One of the most important limitations in determining the appropriate location is environmental parameters.

Therefore, a comprehensive study on a country scale by considering all the parameters is very necessary. In addition, in previous studies, various models have been used to determine the optimal locations, but they did not integrate a risk-based approach for decision-making. In many cases, decision-making process affected by risk due to the lack of prediction of future events, access to accurate and definitive information and accurate evaluation of criteria. In a MCDA problem, risk-taking decision-makers emphasize desirable properties and risk aversion decision makers focus on undesirable properties. In previous studies, this case has not been considered in determining the optimal location for solar power plants.

OWA is one of the multivariate combinational methods [50]. In this method, changing the criteria and parameters can predict different maps and scenarios. Contrary to the Boolean overlay, which the intersection (AND) operator of the least risk and union (OR) shows the most risk in decision making [51], Which includes Weighted Linear Combination (WLC) hybrid models and Boolean overlay operators such as intersection (AND) and union (OR) [52]. The OWA model has a lot of flexibility to meet the needs of decision makers and prioritizes by presenting different results based on different levels of risk [[53], [54], [55], [56]].

In the last decades, the OWA-based GIS-based approach has been used to analyze land-use utilization [53,54]. The use of GIS-OWA has been proven in various socio-economic applications, including residential-quantity assessment [54], health care [57] and tourism [58]. OWA has been used in a variety of fields within environmental studies such as environmental monitoring [59,60], conservation planning [55], landslide risk management [61], earthquake hazard vulnerability [62], water resources management [63], seismic risk assessment [64], geology [65], and landfill location [51]. But there are a few studies on solar energy applications [66]. However, the concept of risk in decision-making has not been taken into account in these studies.

It is necessary to consider the different aspects and factors that are influenced by the location of solar power plants due to the high investment costs of such plants. Therefore, it is important for the investor to determine the optimum locations for the construction of a solar power plant. Choosing the appropriate location for solar projects is one of the strategic decisions of solar projects. Various factors are involved in determining the appropriate location prioritized based on the various alternatives. The amount of incident solar radiation is not the unique factor in the site selection and other parameters such as environmental, economical and technological factors should be considered [[67], [68], [69]]. Some of this effective factors are: land use [38,70], distance from road [66,69], distance from city [71,72], Slope [38,39] and etc. Combining the GIS and MCDA propose a powerful approach with both visualization and suitable decision-making ability to find the best location [[73], [74], [75], [76]].

When all the factors affecting solar power plant designs are considered, the policy recommendations and strategic suggestions on determining appropriate locations will be more accurate and the decision error will be reduced. Based on these factors, the proposed sites are prioritized for construction of solar power plants. Generally, the optimum location will be an option in which all the effective criteria (environmental, economic, etc.) are in the best status. Applying this option reduces the number of optimum locations (with a low risk) and, as a result, the high cost of access to these locations. Investors in the field of solar projects will decide on the different levels of investment risk. Some of these Investors are risk-taking, some of them are risk-neutral and some are risk aversion.

The main objective of this study is applying an OWA method for integration the risk evaluation into the GIS-based analysis. Then this integrated method is utilized to investigate the optimal location for solar power plants installation in Iran. This method presents a comprehensive comparison of the strategies based on the different risk levels that give robust recommendations to investors for choosing the best location for installing solar power plants. As previously discussed, to the best of our knowledge, there is no study of this integration for solar energy applications.

Section snippets

Data and methodology

Iran is placed in the Middle East region which shown in Fig. 3 and has a widespread climate condition, for example in the northwest, in the winters the weather is very cold and snow covered all the surface especially in December until January while in South regions weather is like summer season. In summers, some parts are humid while another part is very dry and hot. Iran is to be found in north latitude from 25° to 40°. Regarding previous assessments, Iran is placed in a very good place in

Solar radiation maps

Using the inverse distance weighting the seasonal incidence of global solar radiation in Iran's area from spring to winter are presented in Fig. 8. Fig. 9 shows Iran's yearly average of seasonal global solar radiation.

As shown in Fig. 7, Fig. 8 for all seasons, the solar radiation increase as moving from north to south. Along with this moving, the latitude decrease, therefore, the south part that is near to the equator have more energy. The northern area of Iran near the coastal region in most

Discussions

The OWA operations make it possible to develop a variety of strategies ranging from an extremity pessimistic (the minimum-type strategy based of the logical AND combination) through all intermediate neutral-towards-risk strategy to an extremely pessimistic strategy (the maximum-type strategy based on the logical OR combination) [39,42,44,45].

The decision maker risk percentage is a major component of any decision-making process. A decision maker with low risk-taking attitude will typically weigh

Conclusion

In this study, an Ordered Weighted Averaging (OWA) method was utilized to integrate the idea of risk into the GIS base investigation to specify the optimal areas. An OWA-base analysis was used due to its capability to have different strategies from extremely pessimistic to extremely optimistic. The results show that Northern and Western parts present lower solar radiation level while eastern and southern areas have more energy. The GIS-based analysis indicates that the values of currently

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