Identification and Prioritization of Risk and Its Effect of the Renewable Energy Life Cycle Based on Performance and Risk Indicators

Risk management consists of two aspects of risk control and risk assessment in the electricity market. So, risk control should cover the risk and work out of the way of optimal investment portfolios. Thus, the aim of this research is producing solar electricity life cycle profitability. First to identify existing risks in the production of electricity using Delphi technique between 300 experts in 15 Powerhouse. Then, the grey ANP model was the adoption of the New Energy Organization of Iran. The number of risk factors were collected by subject literature in renewable energy in Iran that have analyzed and selected the high-risk factors by ANP GREY method. Finally, to examine the life cycle of solar power, the authors analyzed financial indicators and the life cycle’s factors which relates to performance and risk variables, then, the Regression model used in three stages of life cycle. Finally, the result provides incentives for the energy system to support production renewable electricity and aid to increase the profitability of the renewable energy cycle.


Introduction
Managing risk can be a challenging section in renewable energies due to its compound and dynamic structure (Jaberidoost et al., 2015). Risk is illustrated in terms of uncertain event, which dominates the probability of occurrence of unfavorable outcomes like interruption in power plant production, financial burdens, waste generation and emission of pollutants, etc. (Moktadir et al., 2018). Regarding the crises caused by excessive use of fossil fuels, such as environmental pollution, which has sometimes led to irreparable damage to the ecosystem, as well as approaching the time of completion of fossil fuels, attention to the issue of new energies has become a necessity (Chang, 2011). So, financial engineering is one of the advanced methods in financial affairs, which is effectively based on practical mathematical assumptions or through the application of scientific principles in solving decision-making problems. In fact, financial engineering is defined as any innovation in providing investment and risk management tools for this investment (Assa, 2016).
If in the past new energies were considered as an arbitrary option along with other resources, today this choice has become compulsory, which requires more attention to this case than ever before. Reducing environmental costs, economic savings, creating a suitable environment for energy generation and sustainable development, creating suitable business platforms and investing will be the result of planning and moving towards renewable energies (Chum, 2011). Life Cycle Assessment (LCA) is a structured and comprehensive method for measuring energy flows and energy materials and related publications in the life cycle of products. The ISO 14040 and 14044 standards provide a framework for LCA. However, this framework will provide specialist expertise with a range of options that can influence the credibility of LCA research results and will aid them. IEA (international energy agency) guidelines for coherence, balance, and quality and increasing the credibility of photovoltaic LCAs currently provide guidance (Fthenakis et al., 2011). Actually, renewable resource-based production of sustainable energy is a challenging task to replace fossil fuels, to achieve clean environments, to cut down dependence on other countries, and to challenge the uncertainty about fuel prices. A disturbing statistic is that global oil production is nearing its maximum. The world now consumes four barrels of oil to find a new barrel of it (Singh, 2012). Initially, the analysis and study of the life cycle begin from the extraction of resources; through the production of materials, product parts and the product itself, which is then managed through the subsequent management of "recycling of energy", or discarded through final disposal (Davidsson, Höök, & Wall, 2012).
Today, global organizations are seeking to gain a competitive advantage through the creation of new methods. Some of these organizations gain this advantage by improving environmental performance by complying with the laws and standards and increasing the level of customers' knowledge about environmental laws and standards and reducing the negative environmental effects of products and services. Dimensions such as globalization increased regulation of governmental and non-governmental organizations, and pressure from customers to adhere to environmental issues Well, one of the solar power generation equipment is the photovoltaic system. The importance of using photovoltaic technology is that it directly transforms sunlight into electricity without the use of mechanical and chemical mechanisms. So, some features of photovoltaic systems, which are the main reason for their application, are: (A) Having a very wide range of production potential that provides generating and using various types of systems from MWh feed source of watches to MW power supplies.
(B) Lack of need to fossil fuels and non-production of any environmental pollutants in the process of energy exchange.
(C) Ease of operation and maintenance of photovoltaic systems without the need for complex equipment, specialist human resource, and additional costs.
Actually, researchers and studies in the field of solar energy are at least divided into four categories, then, economic and financial assessments, marketing, and the assessment of the possible size of the market, the examination of economic parameters (such as employment, environment and energy storage) and the formulation of government policies and programs on energy, (including incentives and fines, rules and regulations, and allocating research credits and research budgets), are the four main axes that are discussed in the solar energy economy, which this paper will deal with an economic and financial assessment of the solar energy life cycle; so that using one of the financial engineering tools, called risk management. First, we will identify and prioritize risks in the life cycle of solar energy, then we will look at the economic analysis of investment in renewable energy.

Financial Engineering
Actually, Financial affairs are a set of facts, principles, and theories that deal with the collection and equipping of funds and the application and consume of funds by individuals, companies, and governments (Assa, 2016). Also, the financial field in Iran is still considered to be a part of the accounting body, while this area is completely independent and in a different area of science. So, focusing on financial processes and the type of financial decisions that are often related to the future has contrived the risk has an inevitable relationship with financial decision-making. In other words, financial decisions are often related to risk and, consequently, to efficiency. Well, in the definitions of financial engineering, this is also considered as necessary or crucial. In simple terms, financial engineering is a risk management tool (Abdmouleh, Gastli, & Ben-Brahim, 2018).

Concepts and Definitions of Risk
In 2003, the Committee set up a risk management mission in the electricity market focused on managing congestion and risk management strategies. This workgroup was sent some questionnaires to various companies around the world, which received 37 replies from companies from 18 countries. The framework of working Group examined risk search and risk management methods for market participants (Luthra, Govindan, Kharb, & Mangla, 2016). The committee, whose uncertainties include both market prices and regulatory risks in the short and long term. For these companies, there are uncertainties in the electricity market such as market prices, demand, density, fuel costs, investment, clean production permits, regulatory risk and the cost of pollution. Each of these uncertainties can lead to various market risks (Zhang, Qiu, & Zhang, 2017). It should be kept in mind that each of these risks can have a different impact on any type of company (production, transmission, distribution, wholesale and retail), therefore, to manage each of these risks, all its angles must be first, and each of them has a different risk management strategy for each one (Dinarvand, 2015). So, they are elaborated in the following in Figures 1 and 2.

Figure 2. Uncertainties in the Long Term (Less than 2 Years)
Source: www.ises.org. According to Figure 3, it can be seen that each of the risks of the type of ownership (private or public) can have vary degrees of importance (Number 5 for the highest degree of importance and number 1 for the least degree of importance) for the risk manager of that set (Mangla, Kumar, & Barua, 2016).  Renewable energy development in Iran has progressed data rapid pace especially from 2010, while continued strong growth over the next decades desired, there is as significant need to identify and analyze enablers that allow for the design of numerous policy initiatives. Consequently, several definitions of risk can be found in various scientific sources, each of which, depending on the dimension or angle of view, has provided a different definition for risk, which elaboradted in the following (Salm, 2018):  The risk is anything that threatens the present or future of the asset or the ability of the solar power station, institution or organization for earning money.
 The risk of an asset is the probable change in the future return from that asset.
 The term "risk" refers to the possible loss, the degree of probable loss, and the probability of loss. In this regard, risk involves both the probability of profit and the probability of loss.
 The risk is the likelihood of a change in the advantages and benefits foreseen for a decision, an event, or a future state.
 The probability is that there is no assurance to change. Ordinarily, if there was sufficient assurance of change, the assured changes would be secured within the framework of the foreseen advantages and benefits.

Research Goals and Questions
The objectives to be discussed in this article will be defined as follows: 1) Identification of risks in the solar energy life cycle.
2) Risk ranking for the solar energy life cycle.
3) Investigating the effect of financial indicators on increasing the profitability of solar energy lifecycle based on performance and risk indicators.

Theoretical Background
Actually, the study of the literature and history of the subject illustrates that research has not been done with the present study. So the innovation had confirmed by the author. It provides in Table1:

Method
Since the main goal of this research is to identify and assess some risks inherent in the renewable energy life cycle in a 15 MW solar power plant, it can be said that the present research is applied research for an objective. This research is descriptive-non-experimental in data collection method and is a case study among the various descriptive research methods. In this article, due to the research approach adopted in the operation, the survey community is composed of managers, experts and senior experts of new energy organizations in Iran. Riza and Vazilis (2015), given that the number of experts as interviewees should not be high, will suggest a total of 5 to 15 people. The experts have a minimum undergraduate degree, a minimum of 5 years' job experience, and experience of energy-related risks. In this study, 20 questionnaires were distributed and 20 questionnaires were finally collected from 15 solar power stations of Iran, that we collected 300 questionnaires. Therefore, it is important to use decision making approach or model that make possible to revalue the success contingent probability (Yang & Hsieh, 2009). There is a reason that why these methods like Analytic Hierarchy Process (AHP (Note 1)), Analytic Network Process (ANP (Note 2)), Goal

Programming, Delphi, Decision Making Trial and Evaluation Laboratory (DEMATEL) and Fuzzy
Logic have been used for this kinds of intention (Satty, 2004). One of the drawback of AHP is that it does not allow to evaluating interrelations and influences between the factors that compose the decision making process. Hence, Saaty expanded a general structure called Analytic Network Process (ANP) (Wang & Xing, 2008). This method is a induction of AHP and is used in decision making processes in which it is identified that decision factors and criteria may have so strong interrelations and effects generating a high impact on the decision (Raisinghani, Meade, & Schkade, 2007). So, in this paper, one of the MCDM (Note 3) approach is used to select the best risk out of 10 alternatives. Then, the grey ANP technique was proposed by Laarhoven and Pedrych (1983). In the first stage, ANP is used to rank the risk of life-cycle of renewable energy according to the criteria. So, ANP is an important method for analysing and solving multi-criteria decision-making problems. It uses all the factors and criteria for making the best decision, as well as inner and outer dependencies. As in ANP is quite successful in decision-making problems that have uncertainty and interaction between decision criteria.
Firstly, reviewing the literature of studies and the opinions of the experts of the New Energy Organization in Iran, identifying risks and using the Delphi technique, the final risks were screened and selected. Then the grey ANP model is adopted for the relative importance of the risks.
It should be noted that regard to the selection of economic risk, the risk, and performance index was collected from the research literature was selected based on the studies of (Davidsson, Höök, & Wall, 2012), (Cucchiella, D'Adamo, & Gastaldi, 2014) and (Xu, 2007), after ranking the identified risks, taking into account the dangerous risks in the solar energy life cycle, and taking into account the indicators collected from the background of the issue, the impact of each indicator on the profitability of the life cycle of 15 megawatts solar energy will be dealing with using the Regression model, and we will select the indicators that alter the profitability. Finally, we look at the role of financial engineering in boosting profits, as well as reducing the life cycle risk of solar energy. The power of the test of the life cycle effect can depend on the ability to appropriately classify firms-years into life cycle stages.
The life cycle classification variables include dividend, sales growth, capital expenditure as a percentage of the total value of the firm and firm size for both risk and performance measures. Also, based on equation (1), I incorporate performance measures and risk measures, examined by prior studies under the topic, and specify the following empirical model: relationship is modeled through an interruption term or error variable εit an unobserved random variable that adds "noise" to the linear relationship between the dependent variable and regressors. This hypothesis has analyzed for each stage of growth, maturity and decline and stagnant life-cycle groups.
In addition, all financial information in this research has been collected from the Power Research Institute in Iran. Therefore, all relevant financial information has been approved by the researchers and the relevant organization.

Result
First of all, the intended risks were collected by studying the theoretical foundations and opinions of the experts of the Renewable Energy Organization. The first step was to find out the content validity of the opinions of the experts. Then all questionnaires were distributed among managers and experts of the intended organization. After reviewing and selecting crucial indicators from their point of view, the questionnaires were distributed for the second time. Finally, the analysis was conducted after reaching the ideal answer. Reliability of the tool was calculated using incompatibility rate, which all of them are less than 0.1, indicating the reliability of the tool. Since the reliability of the results of the questionnaire "Determining the weights and importance of criteria and sub-criteria of life cycle risk using the network analysis process" is closely related to the compatibility of the respondents' judgment.
Therefore, calculating the incompatibility rate of decision matrices from the judgment of each respondent ensured the reliability of the results of the questionnaire. In the case of an inconsistency rate of less than or equal to 0.1, there is compatibility in the paired comparison, and it can be continued.
Otherwise, the decision maker should review the paired comparison (Vivas, De Las Heras, Segura, & Andújar, 2018). In this study, all rates are less than 0.1, which is confirmed. Table 2 summarizes the risks collected from the research literature.  × Regulatory risk  Initially, Delphi questionnaires were carried out during four stages which are elaborated in the following: Stage 1: 10 criteria which were selected from the literature of this research took place at the discretion of the assembly and it was asked to rank from 1 to 10. Some of the experts, no comment about some of the criteria and those criteria omitted from the list of factors. Finally, 8 factors were selected for the rest of the research.

Stage 2:
The authors designed the second questionnaire for the remaining 8 main criteria and some sub-criteria.
Stage 3: After the second questionnaire analysis, the authors calculated the rate of pay conflicts, following that, the third questionnaire was designed by the authors. In this stage, we analyzed 8 main criteria. Finally, four of them were selected by those experts.

Stage 4:
In the last stage we examined all sub-criteria which belongs to those four main criteria which were selected.
So, the background of those expertise people who were participating in this research are elaborated in the following in Table 3:

Grey Model (ANP) Based on Results from Delphi Model Questionnaire
So, the steps of the grey ANP method in this study have computed into six steps, which we will discuss each one I the following.
Step One: This step is to identify the criterias and sub-criterias to be used in the model.
Step Two: Then structure the ANP model hierarchically (goal, criterias and sub-criterias).
Ordinarily Step Three: Pairwise comparison and local weights estimation In this step, on the basis of the inner and the outer dependencies, criterias of each cluster and clusters themselves are compared pairwise. As Saaty's scale examined, it allows the decision maker to give out relative ratings, by expressing his preference between each pair of criterias verbally as equally important, moderately more important, strongly more important, very strongly more important, and extremely more important. These descriptive disposition would then be translated into numerical values 1, 3, 5, 7, 9, respectively with 2, 4, 6, and 8 as intervening values for comparisons between two successive judgments. After reviewing each of the pair comparison tables, we will compare the calculation and control of their compatibility which is shown in Table 3. So, the purpose of each of the following steps is to obtain special vectors (W21, W22, W32, and W33) to complete the primary super-matrix (imbalanced). It is shown that the most substantial risk factor is Economic Risk which has toched the highest weight in comparision other risks, about 0.298 also the next important risk is Investment Risk in the Renewable Energy Life Cycle, see Table 4, for more information.  Step Four: Supermatrix formation and analysis As summarised above, a local weights vector acquired from the paired comparisons represents the impact of a given set of criterias in a component on another criteria in the system (Saaty, 2004). In this section, the local weights vectors are each entered as a part of some column of a matrix, known as a supermatrix, as presented in Table 5

Step Five: Calculate the global weight of Super Matrix
To concede the cumulative influence of each criteria on every other criteria with which it interacts, the supermatrix is elevate to limiting powers (Saaty & Vargas, 1998). So before taking the limit, it must first be declined to a column stochastic matrix. Then Via normalization, the normalized weight arrays can be found in the appropriate rows of the normalized limit supermatrix. They are the global weights for all the criterias. In this way, each element of the imbalanced super-matrix column cluster is multiplied by the relative importance vector of that cluster (from the cluster matrix) which is presented in   C45  C44  C43  C42  C41  C32  C31  C24  C23  C22  C21  C12  C11  C4  C3  C2

Step Six: Calculation of the Limit Super-Matrix
Accordingly, the results from determining the weights of criteria and sub-criteria have shown in Table 6 and the final weight of the criteria is an economic risk (0.393), investment risk (0.238), regulatory risk www.scholink.org/ojs/index.php/ijafs (0.164), political risk (0.127), respectively, were the highest importance among the main criteria. The limit matrix in Table 8 shows the final weights.

Solar Power Life Cycle Model
Accordingly, Anthony and Ramesh (1992) adopted four variables to divide firms into life cycle stages: sales growth, capital expenditures, divided profits ratio, and solar power station's age. So, in this research, determining the power generation position in the life cycle curve, regardless of the stage of introduction, is considered only in the stages of growth, maturity, and decline, which using these four variables and according to Chen and Park (2006) methodology, it is elaborated in the following: • First, the amount of each of the variables of sales growth, capital expenditures, dividend profits ratio, and a solar power station's age for each year is calculated individually for each solar power station. • Years of companies are divided into nine classes based on each of the four variables and using statistical classes in each industry, as it is categorized in the intended class, according to Table 9, its score is between 1 to 9.
• Then, in each solar power station, a combined grade is obtained for each year that is classified to the following conditions in one of the stages of growth, maturity, and decline: A. If the total score is between 16 and 20, it is in the growth stage.
B. If the total score is between 9 and 15, it is in the maturity stage.
C. If the total score is between 4 and 8, it is in a phase of decline (Chen & Park, 2006).

Regression Results
The results of fitting the regression model for the total statistical sample as well as statistical samples of each the stages of growth, Maturity, and decline are shown in Table 11. It is indicating the overall significance of the model. According to the result, lack of multicollinearity between independent variables, the remaining independence and the adequacy of the models also are confirmed. As you can see in Table 11, all coefficients of estimation of risk criteria and their significance are supported except ROS, but these coefficients are significantly different in terms of growth, maturity and decline stage.
For instance, LEV significant in growth, maturity, and decline stages are respectively, 0.009, 0.000 and 0.000. while, SIZE significant in growth, maturity, and decline stages are containing, 0.451, 0.000 and 0.000. Also ROS is respectively, 0.690, 0.000 and 0.000. In general, the results of Table 11 illustrate that the relevance of risk and performance criteria in growth stages, Maturity and decline have a significant difference with each other.

Discussion
Actually, renewable energy arranges these technologies more affordable by playing a vital role in providing global energy demand. For this reason, conducting such research seems to be necessary. In this study, the risk of the investment was ranked after identifying the risks of the solar energy life cycle.
Also, the basic barriers to the market, as well as the perception of the high risk of investment in this sector, have contrived it possible to limit the development and financing of renewable energy projects, in particular, solar power. Although, reducing solar energy technology costs have significantly decreased their initial investment costs, renewable energy projects continue to face a lot of hardships in many parts of the world, which has led to an increase in the initial investment costs, market risks, and barriers. Actually, another crucial point to be noted is the regulatory risk that ranked third. This risk includes the delay or non-provision of the essential permission to carry out the project, terminate the project contract and change the tax laws. This is one of the things that will alter investment risks and increase production costs. In prioritizing life cycle risks, the political risk was ranked fourth, with the following risks: confiscation, expropriation, nationalization; the risk of foreign exchange; war; political stability; sanctions and other political violence; termination of the contract; Legal and bureaucratic risks. It can be said that if the organization funds and government institutions at the beginning of investing in renewable energy, and especially in developing countries, would aid private sector investors to finance their projects, it would significantly decrease the risk of investment in this field and the trust of the non-governmental sector will be increased to finance these types of projects and in the future it will be fully developed by the private sector.

Conclusion and Recommendations
The results contribute to the importance in three stages of the life cycle (growth, maturity, decline) on the relevance of risk and performance criteria, as well as explanatory power, increase the risk criteria.
So, the risk factors in the growth stage have the highest, and the stage of puberty illustrates the lowest level. Initially, it should be noted that government agencies that initially funded projects will need simple methods such as reducing transaction costs, utilizing domestic incentive prices and, most crucially having long-term goals, to cut down risk. To increase the amount of investment in the Consequently, the total cost of solar power is lower than other sources of energy for electricity generation. Thus, governments can invest more in solar power plants, in addition to aid with the socio-economic development, they can reach to major goals such as sustainable development, reduction of environmental pollution, preservation of part of fossil fuels for future generations, saving in the long run and utilization of free solar energy.
According to the analysis and the results of this study, solar energy development requires incentives to change the energy system. At least 95 countries adoption different incentives to support renewable energy production. Given the fact that Iran has just entered the field of renewable energy, it is essential that these incentives are also taken into consideration in Iran. One of the incentives in this study is to encourage tax credit for investment. According to this incentive, it is allowed to provide a tax credit to private investors in the renewable resource sector to compensate for their tax by investing in their activities. Initially, another incentive is the increase in the profitability of renewable energy. Plus, Renewable energy, encouraging financing facilities and equity bonds, which includes financing facilities such as low interest loans, interest-free loans, loan guarantees and equity bonds, which the financial projects and alter the financial indicators of the projects through changes in the composition of financing sources. In fact, this variable can be profit-driven through input into the variables of the financing of the project and has no effect on the costs and revenues of the project. It does not arrange a significant change in financial indicators, and the facilities in the financial resources' sector have replaced the shareholders and cut down it.
In the incentive for producer tax credits, as suggested in this research, the mechanism is that if a person is subject to the tax credit, the amount of his tax decreases and his power production becomes justifiable. This exemption does not relate to the taxable income of a person but depends on the amount of its production. Incentive tax deductions will alter exploitation phase costs of income tax. The tax deduction incentive is applied in two situations: First: Definition of the revenue bracket; second: As a result of a 50 percent reduction in the tax rate; these incentives have a positive effect and, of course, a low impact on all.
Finally, it is recommended that all solar power plant managers, decision makers, financial analysts, potential investors and investors in the Solar power plants should be made aware of the need for financial analysis. So, analysis of investment projects in financial assets and securities to assess the level of risk, timing and a net present value of future cash flows and the capital liabilities, due to www.scholink.org/ojs/index.php/ijafs different levels and heterogeneous degree of risk perception to the critical life cycle company is attentive and important. Also, optimal placement results are at minimal risk and maximum returns, so making the environment more transparent and making the results even more effective.

Limitations
Firstly, the results of this research can be extensible for other organizations and industries. Certainly, those companies which are managed by private organizations are not extensible. Because risk factors are different in each industry.
Next, one of the most important factors and basic research projects of the existence of adequate information resources are timely and accessible in most countries. But in developing countries, such as Iran, lack comprehensive and complete information centers on the one hand and the confidentiality of the information, on the other hand, an obstacle to the flow of information from the falling information resources to the research centers and researchers.
Also in this study, a fuzzy ANP based structural model has presented with four critical risks in order to assesss ustainability in energy planning and risk management. Other indicators have not been documented and classified and the findings are based on expert's opinion, thus, the evaluation procedure need to be carried out carefully.
In addition, lack a comprehensive database is the most important issues and problems of this industry which is examined. Therefore, the establishment of the advanced systems software and hardware to ensure the correct and transparent flow of information is very important.