Risk assessment using the analytic hierarchy process while planning and prior to constructing wind projects in Jordan

Abstract

The exploitation of wind energy for generating power is taking a major role in the electricity consumption globally. Proper utilization of wind resource could maximize its capacity factor and minimize electricity costs. This article will provide a tool for minimizing wind energy project risks for further investment to consider and choosing the best location in Jordan. For this process, the following risk factors were first identified: strategic management errors, transport, construction, operation and maintenance, marketing and policy risks. Then, using analytic hierarchy process, a complete risk assessment model was built and applied to two alternatives, Ras Moneef and Al-Fujaij. The results showed Al-Fujaij as a better potential location with relative importance to the factors compared with Ras Moneef. Among these risk factors, strategic and business risk have the highest impact, which can be mitigated by performing effective management, accurate contracting, and conducting emergency plans. It is believed that such results could benefit the project stakeholders to be aware of the risk items to be invested in while considering the project funding limits. The proposed framework also enables decision makers to create a reasonable fund and set achievable objectives for the project.

Keywords

Wind energy risk assessment analytic hierarchy process Jordan occupational health and safety

Introduction

Generally, the primary source of energy considered are fossil fuels, which is accompanied by carbon dioxide (CO₂) emissions, which lead to global warming—the number one threat to climate change. Taking renewable energy sources into account, they definitely will decrease (CO₂) emissions and reliability on fossil fuel (Lind et al., 2013). Thus, there is a global concern to encourage the necessity of using renewable energy sources. World’s new exploitation of renewables eliminating large hydro retreated by 23% to $241.6 billion, the lowest overall value since 2013, but records registered investments in renewables capacity was twice of fossil fuel production in 2016. The rate of world’s electricity generated by these renewable sources raised from being 10.3% in 2015 to reach 11.3% in 2016, this increase in renewable energy contribution reduced the CO₂ emission by 1.7 gigatonnes. The 2017 Global Trends record an addition of 138.5 GW, up from 127.5 GW in 2016. These reports showed global concentration on renewable power and fuels: wind, solar, biomass and waste, biofuels, geothermal and marine projects (McCrone et al., 2017).

Wind energy has a crucial role in human being through decades from driving ships in the sea, driving turbines to grind grains, and to pump water for daily use. Recently, renewable energy sources are strongly recommended, especially wind energy (Chen and Blaabjerg, 2009). Despite high cost of generating power by wind rather than traditional sources of energy, it is able to provide electricity for plants, lodging, houses, poultry farms, and so on, in the upcoming years. It is also considered as clean, productive, and fast-growing energy source. While wind turbines are operated, there is no need for fuel consumption, thereby no more emissions; hence, it prevents air pollution and global warming (Baek, 2016). A site is considered ideal for wind energy production, if there are winds of over 6.9 m/s at a height of 80 m above ground.

Global wind power production capacity has been duplicated around 3 times from 147 GW at the end of 2008 to reach 435 GW at the end of 2015 (World Energy Council, n.d.). Wind energy has been deployed by many countries such as the United States, Germany, Spain, China, India, the United Kingdom, and Egypt. China is considered to be the dominant country in this field. The largest wind energy (China) installed has a capacity of 145 GW, followed by the United States with 73 GW, Germany 45 GW, India 25 GW, Spain 23 GW and the United Kingdom 14 GW (Sahu, 2018). Two decades later, research studies were conducted for estimating wind energy potential in Jordan that showed wind energy potential at various sites (Anani et al., 1988; Habali et al., 2001).

Jordan’s primary source of commercial energy is the imported oil in order to meet its requirement. The government was obligated to address its energy consumption policies due to the high importing cost of gas and oil. Today, 97% of the energy is generated from fossil sources and 96% is imported, and only 2% is renewable. By 2020, 10% of the energy mixture should be contributed by renewable energy resources (Rahim, 2014). In addition to the high cost of importation, the concern for reducing emission of greenhouse gases, which affect global warming, leads to a new policy for deployment of renewable energy (Tokgözlü et al., 1987). Solar energy farms, such as photovoltaic (PV), have been addressed in Jordan. Two large PV farms of 100 MW total capacity were established in the south of Jordan, particularly in Maan and Aqaba. These results agreed with Alkhalidi et al. (2018)’s finding in investigation of energy and water indictors for sustainable city. There is a need for wide areas for establishing solar PV farms compared with wind energy farms for generating same output of energy. Higher efficiency and capacity factor can be obtained from WECS (wind energy conversion system) compared with the PV system. Research studies were held spotting the light on the potential of wind energy in Jordan. Bataineh and Dalalah (2013) presented a technical assessment of power generation in seven potential sites located in Jordan. They found the annual energy output for Ras Moneef and Tafilah was almost the same and at the best (Bataineh and Dalalah, 2013). Effect of wind turbine blade design on energy generation was investigated by Alkhalidi et al. (2017) to find the best performance for wind turbine (Amano et al., 2010).

Ammari et al. (2015) studied variation of Weibull parameters seasonally for five different Jordanian locations. They showed the energy output was depended on the seasons’ weather, wind turbine characteristics (efficiency, area swept, height, mechanical and electrical performance and manufacture type), location, and wind speed. Mamlook et al. (2009) studied the exploitation of wind using the fuzzy logic methodology to evaluate wind locations in Jordan in terms of benefits and costs. Enevoldsen and Savacool (2016) addressed methods to achieve public acceptance of wind projects. They suggest that acceptance can be improved through community involvement and ownership. They also stated that local’s attitude toward wind power, whether it was acceptance or opposition, can still be managed. Suškevičs et al. (2019) stated that public acceptance varies among European countries and by region. They recommend to have good communications channels and participation activities, which should be planned at the strategic level.

Participants from the Middle East North Africa Sustainable Electricity Trajectories workshop mentioned that wind energy is already the cheapest way to produce energy in Jordan’s present system due to high wind speeds especially in the Dead Sea Valley. Furthermore, participants agreed that the population and political decision makers generally accept both wind and solar energy. Based on this, authors concluded that public in Jordan has high awareness of renewable energy. In addition, in the 1990s, local experts have utilized several analytical tools to investigate renewable energy application in Jordan. For example, Elkarmi and Mustafa (1993) have used analytic hierarchy process (AHP) to make decisions to choose certain mechanisms (policy instruments) in Jordan, more specifically in solar energy.

The world is full of uncertainty, and this makes risk a base structure implemented in the design of any system. Risk is a term that describes potential harm or loss associated with an activity executed in an uncertain environment. Research on risk sources indicates that policy risk, technology risk, economic risk, security risk, and various types of other factors exist in renewable energy (Deng and Oren, 2006; Li et al., 2013). The evolution of renewable energy plays a crucial role in generating sustainable energy, preventing global warming, pollution, providing job vacancies, and so on. However, despite the benefits, managing renewable energy projects’ threats still acts as a barrier and is very challenging (Lee and Zhong, 2015). Risk assessment studies were held in the same field by many researchers using SWOT analysis and Mckinsey Matrix (Rolik, 2017), introducing a new strategic method for financing and managing risks for renewable energy projects (Lee and Zhong, 2015) and implying scorecard concept of evaluating such risks (Kucukali, 2016).

Decision makers are commonly faced with complex decisions dealing with multicriteria problems. In addition, in many cases, subjective judgment plays an important role in the risk assessment process. AHP is considered as one of the popular multicriterion decision making (MCDM) approaches. AHP can mathematically assess the risk and at the same time, consistency of the judgment is taken into consideration.

AHP method was selected in this study because it is both intuitive and adaptable compared with other MCDM approaches. The ability to capture both actual numerical data and subjective opinion makes it a desirable approach to decision makers. The method also takes into consideration the inconsistencies in human opinions, while mathematically assessing the degree of these inconsistencies. In addition, the method organizes the decision criteria into hierarchical structure and is capable of supporting group decision making through calculating the geometric mean of risk factor assessments.

On the contrary, the more complex the problem is and the larger the number of factors that the method will have to consider, the more comparisons will be created and the more time-consuming it will become. Another disadvantage is that individuals will interpret the 9-point scale differently, leading to greater inconsistencies.

Having a good perception of a wind project risk factors reduces time and costs. The previous studies did not consider risk evaluation in implementing wind energy projects in Jordan using AHP. The purpose of this study presents a quantitative risk assessment methodology that considers the risk factors of wind energy projects. This study incorporates project stakeholders to resolve business, economic, societal and environmental risks for further investment of wind farm has not been carried out in this field in Jordan.

Methodology and application

Sometimes decision making is more than an idea to implement and requires insight into the problem, but not superficial analysis. The AHP, proposed by Saaty, is a simple mathematically based multicriteria decision-making tool. This process is designed to deal with complex, unstructured, and multiattribute problems. It generates a weight for each evaluation criterion by assigning a score according to the decision maker’s pairwise comparisons of the criteria. This was done based on that criterion, the higher the weight, the more important the corresponding criterion, and then synthesizing the results (Saaty, 2004). However, AHP has been adopted in different areas of application fields. For example, Karaman and Akman (2018) used AHP in Turkish airline industry to evaluate the notion of corporate social responsibility (CSR) program criteria on many alternatives. Authors have found that CSR model in the airline industry is framed by a restricted economic domain, which is blocked by social and environmental dimensions (Karaman and Akman, 2018). Kokangül et al. (2017) performed a risk assessment study in a manufacturing company and found that the AHP method can determine the importance levels and risk classes of the hazards together. Also Zhang et al. (2018) investigated the performance of rural houses space heating systems and specified the best system among candidate systems. Ho and Ma (2018) introduced a review of literature on the integrated AHP approaches and applications published between 2007 and 2016. In this review, they show that AHP was applied to large number of topics including manufacturing, logistics, supplier evaluation and selection, replaced distribution network, and many others. In their review, one article related to power generation was introduced by Meza et al. (2009). They used AHP to determine the number of new power generating units. In this article, a proposed risk assessment using the AHP framework will be illustrated using a real-life wind project. This work is on one hand, the first to apply AHP approach to wind energy–related projects. In addition, it is applied to Jordan, which has unique strategic, political, operational, and constructional conditions that were taken into consideration.

In this article, risk factors were extracted from a published literature review (Gatzert and Kosub, 2016; Table 1). An AHP-based risk assessment tool is presented in this article for an efficient approach for managing risks of wind energy projects. This approach integrates preventing revenue losses and ensuring safety into project risk evaluation using a multiattribute decision-making technique. The proposed framework employs the AHP technique for making the pairwise comparison of the risk criteria presenting a credible ranking of these risks by examining reliable judgments.

Table 1.

Criteria and subcriteria used to perform a comparison between alternative locations.

Main criteria	Subcriteria	Definition
Strategic and business risks (R1)	Financing risks/insufficient expertise (R11)	This criterion measures imperfect access to capital or no cooperation between partners to have fair consulting information of technical expertise, financing, and market access.
	Insufficient public acceptance (R12)	A lack of public or residents (and political) acceptance.
	Complex approval processes (R13)	Complex and long approval procedures.
Transport and construction risks (R2)	–	Mainly focuses on the first phase of the wind project and the construction period considered as the riskiest project phase.Risks associated with the loss of revenue due to start-up delays and damage during transportation or construction of the wind park.
Operation and maintenance risks (R3)	General operational and maintenance risks (R31)	This criterion associated with damages to physical assets due to accident, negligence, or wear and tear.
	Natural hazards (R32)	This criterion represents a special risk for wind parks and can cause intense losses in case wind turbines damaged.
	Serial losses of defective turbines or components (R33)	Losses obtained from defect components.
Political, policy, and regulatory risks (R4)	–	Changes in governmental priorities, resulting in reversed, modified, or abandoned renewable energy support schemes as happened to Al Khamsah and Fujaij plants.
Market and sales risks (R5)	Variability of revenue due to weather (R51)	As revenues of wind, parks can vary due to different wind speeds with a year-to-year variability depending on the region.
	Variability of revenue due to grid availability (R52)	Which represents another bottleneck risk and implies that power output cannot be sold, leading to losses in revenue in countries without fixed support schemes.
	Variability of revenue due to price volatility (R53)	A fall in energy prices can imply a considerable revenue risk if the output is sold at a lower price than the reference value.

The proposed tool divides the problem into subproblems in a hierarchy. Hence, the analysis of each subproblem was studied and analysis made on independently. After making the hierarchy shape illustrating the problem, experts asked to evaluate these risks by a survey and scores being assigned converting these words into numbers for every pair of objectives based on the comparison made as pairwise with relative to its level of significance on the upper level of the hierarchy. Then, using the expert choice (EC) program, the final weights are evaluated, then risk factors are ranked from a high significance of impact to low significance of impact and alternatives are ranked similarly. Thus, this ranking gives more insight to the risks faced while implementing projects, gives more awareness to the stakeholders, decision makers, and investors to recognize such risks. Finally, these decision makers will be able to determine the wind risk elements to be focused on while taking in consideration funding wind projects and which alternative is best.

The AHP

AHP can be summarized into the following six main steps:

Identifying the input criteria using actual data and/or subjective opinion;

Develop hierarchical structure;

Creating the pairwise comparison matrix (PCM) for all levels;

Computing priority vector scores;

Checking for consistency; and

Develop overall ranking of the options.

A description of these steps as it relates to our case study of planning wind projects in Jordan is as follows; in step (1), risk factors were extracted as shown in Table 1. These risk factors include four main criteria and a number of subcriteria for each. Consequently, in step (2), a risk-based hierarchy consisting of the potential risk items threatening the wind project was prepared as shown in Figure 1, and each risk independently analyzed.

Figure 1.

Hierarchy of risks affecting wind energy project.

Afterward, a priority index for expert’s judgment is determined by converting evaluations of risks from verbal into numerical values (Table 2) taken from a questionnaire. These values were assigned while executing the pairwise comparisons regarding alternatives with relative importance to the risk criteria and calculation was made by EC program (Saaty and Forman, 1982–1983).

Table 2.

AHP scale for combinations.

Numerical scale	Definition
1	Equal risk to the two criteria
3	Low risk of one criterion compared with another
5	High risk of one criterion compared with another
7	Very high risk of one criterion over another
9	Extremely high risk of one criterion over another

AHP: analytic hierarchy process.

Step (3) includes performing pairwise comparison, two factors at a time. So comparing factors i and j will produce a_ij, which represents the importance of the ith factor relative to the jth factor. In EC equations, used after assigning the values, we have more than one expert’s judgments. A geometrical mean equation (1) is used for calculation

a i j = \sqrt[n]{a i j 1 \times a i j 2 \times a i j n}

(1)

Results are then entered in the PCMs; this is a square matrix of size m × m, where m is the number of factors considered for evaluation. PCM entries are $a_{i j}$ are greater than zero and, for example, if A is riskier than B by 3 and B is riskier than C by 3 then A is riskier than C by $3 \times 3 = 9$ (equation (2))

PCM = [\begin{matrix} a 11 & a 12 \\ a i 1 & a 2 j \end{matrix}]

(2)

Such that

a_{i j} . a_{j i} = 1

In step (4), priority vector is calculated as follows. First the normalizing weights held by

1. summing the values in each column in equation (3)

{\bar{a}}_{i j} = \sum_{i = 1}^{n} a i j

(3)

2. dividing each element in the matrix by its column total to calculate its normalized pairwise (normalized relative weight) matrix by equation (4)

N_{i j} = \frac{a_{i j}}{\sum_{i = 1}^{n} a_{j i}}

(4)

3. Then the weight of the attribute (the normalized principal Eigen vector) computed by summing the normalized weights of the rows using equation (5)

W i = \frac{\sum_{j = 1}^{n} N i j}{n}

(5)

Where n is the number of the attributes. The resulting priority vector shows relative weights of different risk factors. In step (5), consistency is checked. We shall not force the consistency in some cases because we are dealing with human judgment. The matrix is consistent if transitivity is existed and can be checked by equation (6)

I n c o n s i s t e n c y i n d e x = \frac{A v e r a g e r a t i o - n}{n - 1}

(6)

By dividing the inconsistency index by the random indices for checking consistency, taken from Table 3, we obtain the inconsistency ratio, and it should be below 0.1 in order to have no concern about the inconsistency of the judgments. After normalizing weights afterward, the overall score of alternatives is computed.

Table 3.

Random consistency (RC) index.

n	2	3	4	5	6	7	8	9	10
Random index	0	0.58	0.90	1.12	1.24	1.32	1.41	1.45	1.49

n = size of the reciprocal matrix.

In step (6), the overall composite weight is calculated for each alternative. In order to obtain reliable judgments, the questionnaire tends to measure its reliability by education, position, and number of years of experience as total and in wind specifically in Table 4.

Table 4.

Experts’ information on who completed the questionnaire.

	Expert 1	Expert 2	Expert 3	Expert 4
Education	Mechanical engineering	Fire and safety engineering	Mechatronics engineering	Mechanical engineering
Position	Team leader	HSE manager	Technical site	Designer
Years of experience	Total (9), Wind (3)	Wind (3)	Wind (4)	Wind (2)

HSE manager: health, safety and environmental manager.

AHP for risk assessment

Managing a project in the early phase may reduce hazards and revenue losses resulted in the lack of knowledge in mitigating risks. A few research studies used AHP along with risk assessment. Previous researches employed AHP for selection in manufacturing, shipping, and oil industry (Amiri, 2010; Celik et al., 2009; Durán and Aguilo, 2008) or ranking indicators in healthcare, electronic, and entertainment industry (Podgórski, 2015; Şen and Çinar, 2010; Vidal et al., 2011). There are many researches about using AHP method in risk assessment project. Mustafa and Al-Bahar (1991) applied AHP method to assess project risks during the bidding stage of a construction project and to overcome the limitations of the traditional approaches currently used by contractors of constructing the Jamuna multipurpose bridge in Bangladesh also Wang et al. (2012) proposed a new risk assessment approach to perform a structured analysis of aggregative food safety risk in the food supply chain by using the AHP. Also, Aminbakhsh et al. (2013) presented a robust method for prioritization of safety risks in construction projects to create a rational budget and to set realistic goals without compromising safety based on AHP method. Zavadskas et al. (2010) presented a risk assessment of construction projects. The risk evaluation attributes are selected taking into consideration the interests and goals of the stakeholders as well as factors that have an influence on the construction process efficiency and real estate value using AHP. Finally, Zhang and Zou (2007) applied AHP tool in three-stage risk assessment includes preliminary, secondary, and dynamic assessment of water or mud inrush risk to control the construction risk of karst tunnels.

Application and results

The Hashemite Kingdom of Jordan, located in the Middle East, has an area of about 89,341 km² and a population of about 10 million. With its population, there is an increasing demand for energy per person. The demand on the primary source has grown of about 5% during the last decade. Thus, Jordan has already established four wind farms as mentioned previously and the ongoing project at Tafilah. There are potential locations for generating energy by wind energy across the country, as projects worked in invested locations, Ras Moneef showed a good potential of average wind speed about 7 m/s with 1150 m elevation. Based on the Jordanian meteorological department (JMD) measures of wind speed as a mean of three readings in each day, it was showed the potential of Ras Moneef of annual energy production is almost 450 MWh. In addition, for Al-Fujaij with 1274 m elevation, average wind speed was measured at 10 m as 5.99 and at 40 m as 6.88.

The presented AHP-based risk assessment tool was applied to a real wind project at Tafilah. First, a hierarchy was built including the risk elements considered a threat to the current wind project as shown in Figure 1 and then investigated at two locations, Ras Moneef and Al-Fujaij. The hierarchy was built for making a comparison of five criteria, three criteria were subdivided into subcriteria and a pairwise comparison was held for these two locations. A questionnaire was conducted to guide the experts in judging. As more than one expert filled the questionnaire, a geometric mean of the experts’ judgments calculated and filled in 15 reciprocal matrices.

A pairwise comparison was first made between the risk elements of the first and second level and four reciprocal matrices built as shown in Table 5 with its inconsistency ratio computed for each.

Table 5.

Values of pairwise comparison for selected criteria.

Main criteria comparison
	Strategic/business risks	Transport/construction	Operation/maintenance	Political, policy, regulatory risks	Market/sales risks
Strategic/business risks	1	5	5	5	7
Transport/construction		1	3	3	5
Operation/maintenance			1	3	1
Political, policy, regulatory risks				1	1
Market/sales risks	Incon = 0.09				1
Strategic and business subcriteria risks
	Financing risks/insufficient expertise	Insufficient public acceptance		Complex approval processes
Financing risks/insufficient expertise	1	3		7
Insufficient public acceptance		1		5
Complex approval processes	Incon = 0.06			1
Operating and maintenance subcriteria risks
	General operation and maintenance risks	Damage due to natural hazards		Serial losses risk arising from defective component
General operation and maintenance risks	1	1/5		1
Damage due to natural hazards		1		3
Serial losses risk arising from defective component	Incon = 0.03			1
Market and sales subcriteria risks
	Variability of revenue due to weather	Variability of revenue due to grid availability		Variability of revenue due to price volatility
Variability of revenue due to weather	1	5		5
Variability of revenue due to grid availability		1		1
Variability of revenue due to price volatility	Incon = 0.00			1

Another pairwise comparison held for the alternative location is shown in Table 6 and another 10 reciprocal comparison for each element with its inconsistency ratio computed in Appendix 1.

Table 6.

Values of pairwise comparison for selected criteria and alternatives.

Complex approval processes
	Tafilah	Al-Fujaij
Tafilah	1	3
Al-Fujaij	Incon = 0.00	1

As shown in Table 7, “strategic and business risk” was perceived as the highest level of significant impact followed by “transportation and construction risk,” “operation and maintenance risk,” “market and sales risk,” and “political, policy and regulatory risk,” respectively. The same steps were used for computation to each subcriteria to specify its effect on the main risk criteria. Pairwise comparisons were made to prioritize subcriteria. For the “strategic and business risk,” “financing risk and insufficient expertise” was found to have more impact compared with the other two subcriteria. “Insufficient public acceptance” was assessed to be riskier than “complex approval processes.” In the “operation and maintenance risks,” its subcriteria were prioritized from most significant to least significant as “natural hazards,” “serial losses of defective turbines or components” and “general operational and maintenance risks.” Finally, in the “market and sales risks,” the subcriteria “variability of revenue due to weather” showed a high impact followed by “variability of revenue due to grid availability” and “variability of revenue due to price volatility,” respectively.

Table 7.

Risk factor weights.

Risk factor	Assigned value	Normalized
R1	1.00	0.533
R2	0.420	0.224
R3	0.194	0.103
R5	0.137	0.073
R4	0.124	0.066
	Total = 1.875	Total = 1
R11	1.00	0.649
R12	0.430	0.279
R13	0.111	0.072
	Total = 1.541	Total = 1
R32	1.00	0.659
R33	0.281	0.185
R31	0.237	0.156
	Total = 1.518	Total = 1
R51	1.00	0.714
R52	0.200	0.143
R53	0.200	0.143
	Total = 1.400	Total = 1

The weight that is normalized for each criterion was taken to specify how severe the risk is. The highest normalized weight of a risk, the more severe is. Figure 2 shows the severity of these risk elements on Ras Moneef and Al-Fujaij.

Figure 2.

Alternatives’ behavior on each risk factor by EC.

As it is shown, both locations almost act the same on the “political, policy and regulatory risk” as well as the same was concluded for Tafilah. For “transport and construction risk,” Ras Moneef present impact that is more significant compared with Al-Fujaij due to its environment. “strategic and business risk” has its highest impact as mentioned before, and it is clear on the figure and more significant for Ras Moneef. More severity for “operation and maintenance risk” as illustrated in the figure at Ras Moneef. Finally, “Market and sales risk” at Ras Moneef has a higher impact due to variability of revenue due to weather in this location. In general, the overall final value for Ras Moneef compared with Al Fujaij is shown in Figure 3. Al-fujaij is nominated as the best location for investment.

Figure 3.

Overall weights of alternatives regarding risk factors investigated by EC.

Based on the prioritized risk elements, for the assessment based on experts to reduce business and strategic risk, more concern for financing risk and more cooperation with partners to share technical expertise and marketing access should be given. For public acceptance risk, it is better to work on holding a strategy for communication with society to gain their acceptance. For transportation and construction, there is a potential loss because of start-up delays and damage while carrying wind parts and assists or installing them and in order to mitigate this risk, the best solution is providing insurance; thus, it is recommended to make mitigation effective to perform better management of the project and assign good contracting. The operation and maintenance risk showed the lower significance of risk; damage due to natural hazards is almost certain to occur and may result in a severe risk of destroying wind turbines, and also coldness and accumulation of snow may affect wind turbine performance. Thus, insurance should cover these issues too. General operational and maintenance risks and serial losses of defective turbines or components presented low significance of risk due to coverage of insurance that is available. Although market and sales risk, appeared as prior to least significance of risk compared with all main risk factors, it presented a high level of risk and cause revenue losses once variability of revenue due to weather occurred. A noticeable change in revenues of wind farms can result in different wind speeds within a time-in-a year and ineffectiveness to mitigate. Variability of revenue due to grid availability and price volatility that presented a same level of risk that power output could not be sold is still stable in Jordan. Political, policy, and regulatory risks showed a low level of risk impact where political risks are stable so far, and policy and regulatory risks of changes in governmental priorities have the low impact that does not need mitigation.

Conclusion

In this article, AHP-based risk assessment tool was proposed to evaluate risk elements in wind energy projects in Jordan preventing revenue losses and hazards; a tool that reduces wrong estimation or biased decision making while investing in the wind by a group of decision makers. The proposed tool was applied to an existing wind project in Jordan and evaluated these risk elements at two different locations to explain how this framework able to direct stakeholders and investors through the risk assessment that associated while implementing wind projects in Jordan. The AHP approach availed as a method for screening and reducing the inconsistency of wind risk factors’ riskiness assigned by experts. Also, presented a robust method in order to rank wind risk items and a valid investigation for making a reasonable fund along with offering achievable objectives without exposing revenue loss or hazards obtained through highlighting the major impacts of risk elements before constructing a wind project. Finally, mitigation of such high impact risks can be considered while financing in accordance with their comparative prioritizing. For the case study, more provisions can be made to eliminate risks’ effect related to more elements that are significant. For example “transport and construction risk,” “financing risk and insufficient expertise,” “insufficient public acceptance,” “natural hazards,” and “variability of revenue due to weather” compared with less level of impact such as “variability of revenue due to grid availability,” “variability of revenue due to price volatility,” and “political, policy and regulatory risk,” in general. In addition, Al-Fujaij showed a good potential for further investments and higher revenues compared with Ras Moneef.

Footnotes

Appendix 1

Table 8.

Pairwise comparisons for the available alternatives.

Financing and insufficient expertise
	Tafilah	Al-Fujaij
Tafilah	1	1
Al-Fujaij	Incon = 0.00	1
Insufficient public acceptance
	Tafilah	Al-Fujaij
Tafilah	1	3
Al-Fujaij	Incon = 0.00	1
Complex approval processes
	Tafilah	Al-Fujaij
Tafilah	1	3
Al-Fujaij	Incon = 0.00	1
General operation and maintenance risks
	Tafilah	Al-Fujaij
Tafilah	1	1
Al-Fujaij	Incon = 0.00	1
Damage due to natural hazards
	Tafilah	Al-Fujaij
Tafilah	1	5
Al-Fujaij	Incon = 0.00	1
Serial losses: risk arising from defective component
	Tafilah	Al-Fujaij
Tafilah	1	1
Al-Fujaij	Incon = 0.00	1
Variability of revenue due to weather
	Tafilah	Al-Fujaij
Tafilah	1	5
Al-Fujaij	Incon = 0.00	1
Variability of revenue due to grid availability
	Tafilah	Al-Fujaij
Tafilah	1	5
Al-Fujaij	Incon = 0.00	1
Variability of revenue due to price volatility
	Tafilah	Al-Fujaij
Tafilah	1	1
Al-Fujaij	Incon = 0.00	1
Transport/construction
	Tafilah	Al-Fujaij
Tafilah	1	9
Al-Fujaij	Incon = 0.00	1
Political, policy, regulatory risks
	Tafilah	Al-Fujaij
Tafilah	1	1
Al-Fujaij	Incon = 0.00	1

Acknowledgements

The research leading to these results has received a full support from Tafilah Wind Farm staff particularly.

Declaration of conflicting interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) received no financial support for the research, authorship, and/or publication of this article.

ORCID iD

Ammar Alkhalidi

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