# Risk in solar parks: a parametric approach of comparing AHP and TOPSIS methods

N. Ranganath1*, Debasis Sarkar2, Vinaykumar S. Mathad3, Saurav Kumar Ghosh4

1*EI Technologies, Bangalore, India

3E I Technologies Pvt. Ltd. Bangalore, India

4HKBK College of Engineering, Bangalore, India

Corresponding author: N. Ranganath, Chairman & amp; Managing Director, EI Technologies, Bangalore, India, E-mail: n.ranganath@eitech.in

Citation: Ranganath N, Debasis S, Vinaykumar SM, Saurav Kumar G (2021) Risk in solar parks: a parametric approach of comparing AHP and TOPSIS methods. J Civil Engg ID 2(1):29-45.

Received Date:December 15, 2020; Accepted Date: January 16, 2021; Published Date: April 05, 2021

## Abstract

Renewable energy sector projects like development and implementation of solar power plants are crucial in the present era to suffice the target for generation of green and clean energy. Just like any complex infrastructure projects, the solar power projects face risks and uncertainties throughout its many phases. The risk assessment for projects remains a multi-variable problem as a lot depends on human expertise. The present work identifies the risks involved in its various phases and employs two methodologies of risk analysis while comparing between the two. It has been observed that the TOPSIS approach produces more coherent interpretation than the AHP approach. This is the first study where TOPSIS approach is employed for the case of risk assessment of solar park and then subsequently compared with the AHP analysis of the same. It has been inferred that for niche and isolated projects AHP is more suitable however for more general and multiple source data TOPSIS is the superior approach. The risk assessment is broken down into 5 phases and it has been observed that based on the risk indexing of those phases, the project authorities cannot afford to ignore any of the phases.

Keywords: Solar Parks; Feasibility Study; Risk Management; TOPSIS; AHP

## 1.0 Introduction

Complex multidisciplinary infrastructure projects suffer huge risks starting from the inception of the idea to its feasibility, design, development, implementation and operation [1]. If these risks are not properly addressed by the project authorities and mitigated priory by adequate mitigation measures, then the project runs the likelihood of collapses due to time and cost over- run. Risk analysis thereby becomes a crucial activity to be carried out by the project authorities during the feasibility phase of the project. Risk analysis determines the severity of the risk in a quantitative manner by formulating risk maps. Based on the scale of risk maps which indicate low, medium, high, very high and critical risk zones, the corresponding mitigation measures can be adopted. [1] Carried out risk analysis of a complex infrastructure project like construction of elevated corridor for metro rail operations through Expected Value Method (EVM) which was later implemented by [2], [3, 4] carried out risk analysis and developed risk index through Fuzzy Analytical Hierarchy Process (FAHP) for an elevated corridor metro rail project in India. Risk identification, risk analysis and development of risk mitigation measures are the three basic steps for carrying out the risk management process [2]. Risk analysis can be carried out through various Multi Criteria Decision Making (MCDM) methods. [5] Introduced the fuzzy set theory within MCDM which was used by many researchers working in decision making.

In the risk analysis concept, identifying and assessing risk variables is an important step that should be conducted by a project manager to get an early warning about the possible risk variable using statistics that can occur in the project. Many techniques exist in order to quantify and assess such risk variables into formulating decision making parameters. Other approach is applying fuzzy logic as an algorithm to capture the disguises of perceptible perceptions combined with the Technique for Order Preference by Similarities to Ideal Solution (TOPSIS) method. Furthermore the evaluation of the risk priority number is based on fuzzy TOPSIS to the ideal solution to solve multi criteria problems.

In the recent years, intensive research and development has been carried out in the area of Project Risk Management (PRM) [1, 3, 4]. It is widely recognized as one of the most critical procedures & capability areas in the field of project management. The construction industry, perhaps more than the rest, has been plagued by risk, resulting in poor performance with enhanced costs and time delays. Every part of project life cycle is subject to risks, which have to be treated adequately to stay in control of the project and to achieve its goals in an optimal way[6] formulated the probabilistic infrastructure risk analysis model, presenting a holistic approach for modeling the water distributions infrastructure systems dynamics. Further work of [7] presents the application part of such risk analysis model by characterizing the water system along the parameters of function, structure, component, state, and vulnerability, while keeping in view of other political, temporal and economic perspectives. Expected and extreme risks are evaluated using probability, while efficient alternatives are generated and presented in a multi-objective framework. The methodological framework can be easily applied to other critical infrastructure elements and networks. (Author?) [8] Defines vulnerability as a measure of any system susceptibility to threat scenarios while demonstrating that vulnerability is a condition of the system which can be quantified using the Infrastructure Vulnerability Assessment Model (I-VAM). Such a model requires establishing value functions and weights to various protection parameters of the system. Additionally the un- certainty in measurements is taken into account by suitable simulations along with expert’s feedback depending on the particular field, eventually providing a vulnerability density function (Ω).

[9] carried out risk assessment primarily for construction industry and concluded that in construction industry things do not always turn as planned and thereby detailed risk management is must. [10] Suggested in developing methodologies which can put risk management into practice. Furthermore, [11] claimed that all the undertaken risk management practices focuses on project uncertainty. However, project risks are all about project cost and unscheduled uncertainties [2]. Thereby, the risk management unarguably should be focused on project uncertainty and complexity management.

Recent trends in the construction industry indicate continued use of alter- native procurement methods such as design-build, construction management, build-operate-transfer, and privatization [12]. Increased use of these evolving methods produces higher levels of uncertainty with respect to long term performance and portability. The uncertainties inherent in implementing new procurement methods necessitate investigation of enhanced methods of pre- project planning and analysis. This aspect is particularly true for revenue de- pendent projects such as toll tax on roads/highways. Enhanced risk analysis tools provide improved information for pre-project decision making and performance outcome. One such risk analysis method is the Monte Carlo [12] for revenue dependent infrastructure projects. Mathematical analysis is limited for some studies available in the literature due to constraints in data about the overall reliability of a system. This issues leads to shifting the domain to input set of parameters from expert knowledge in the field. Thus, a lot of crucial parameters that are identified before they are put to any mathematical modeling or simulation are provided by the field experts or by statistically obtained opinion about the inherent parameters. This problem usually continues due to the lack of hard quantifiable data in most of the cases as shown by [13] leading to the use of probabilistic risk analysis.

The emergence of information technology has transformed the situation from one characterized by little data to one characterized by data over-abundance [13]. Critical infrastructure systems such as electric power distribution systems, transportation systems, water supply systems, and natural gas supply systems are important examples of problems characterized by data over-abundance. There are often substantial amounts of information collected and archived about the behavior of these systems over time. Yet it can be very difficult to effectively utilize these large data sets for risk assessment due to the long list of variables and trickier limitation to assigning weight age values to these parameters. One of the unforeseen and unpredictable parameters in the risk assessment of any infrastructure system comes from natural disasters, the impact and the scale of which is very unpredictable depending upon the kind of infrastructure in consideration. Other industrial risk and contingencies can be well designed and streamlined through a wellstructured organization and management system. Sometimes, the terrorist activities due to their unforeseen nature are clubbed along the natural disasters and are sometimes considered as a separate parameter in the risk management studies [14, 15]. Compared to other infrastructure industries, construction industry is subjected to greater risk due to its unique features in various project phases like planning, investigation, collection of data, feasibility, design and development, implementation and execution as well as operation & maintenance. Many complex mega infrastructure projects like setting up of solar park, power projects, refineries, construction of elevated and underground corridors for metro rail, etc. have experienced large variations in cost & scheduling leading to enormous load on manpower, longer delays in the execution & commissioning of these projects.

In most of the cases, the economic viability itself ends up being questioned due to delay in project completion on account of various risks encountered during implementation stage. It is a wellestablished fact that due to increase in project size & complexity, higher levels of risk & uncertainty are inevitable. Hence, a systematic process of risk analysis is imperative to classify, identify and analyze these risks, for the corresponding formulation of risk response strategies [16].

Substantial work has be carried out in risk assessment and management of the same [17, 18]. [19] studied the relationship between management support for risk management processes and the reported project success extensively complimenting with the identification of shortcomings and possible improvement opportunities.

[1] argues that one needs to identify the various stages of projects such that, the entire work of project implementation from concept to commissioning can be divided appropriately in different phases such that, broad activities can be grouped under each phase and sub activities may be defined which in turn portray the risk associated for those broad and sub activities. Same has been employed in the present work where an attempt is made to explore the relationship between broad and sub activity risks under each phases of project related solar power plant. Development of questionnaires for risk rating using Saaty Scale, probability of risk occurrence & impact of risk for assessment of risk severity, risk index & risk ranking are carried out. For this, three projects located in three different parts of India have been considered. To achieve the above mentioned objectives, two research frameworks have been employed using Modified Analytic Hierarchy Process (MAHP) and TOPSIS.

As solar parks are still either rare or under development & installation, can be considered to be in the cocoon phase, not much literature is available on the risk assessment or risk management or such solar parks yet. As discussed earlier that for other infrastructure projects, risk management is studied both in detailed in theory and in application. However, these learning are not specifically applied to the risk management of solar parks except a few isolated studies here and there which are discussed later. One such relevant work is by [20] who studied the Analytic Network Process (ANP) and applied the same to the selection of photovoltaic (PV) solar power projects. These projects follow a long management and execution process from plant site selection to plant start- up. As a consequence, there are many risks of time delays and even of project stoppage. These risk and vulnerabilities are only hurdles in terms of economic aspect or efficiency consideration. This study identified 50 project execution delay and/or stoppage risks in order to invest based on risk minimization. The main conclusion of this study is that unlike the other models used in the literature, the single network model can manage all the information of the real- world problem and thus it is the decision analysis model. The strengths and weaknesses of ANP as a multi-criteria decision analysis tool are also described in their work. In the further works of [21], the criteria for accepting or rejecting any proposals for such an investment based on risk priorities. [22] extended the research in all forms of renewable energy, wind, water and solar power in their work. Cost effectiveness studies of solar power can be found in literature [23, 24, 25]. A preliminary case study of solar parks has earlier been carried out in our earlier works showing the role of vigilance in design and construction [26]. Adding to the previous work and understanding, an attempt is put forward in applying the known approaches of risk management on solar power parks and commenting on the better approach to deal with multi criteria decision making and substantial crucial parameters. The primary objective of this work is to compare the two approach and determine their merits and demerits over one other by identifying and evaluating the risks and uncertainties associated with a complex project like solar power plant installation in India.

## 2.0 Methodology

The methodology is primary data research, where the data pertaining to risks associated with the different activities of the solar power plant has been collected from solar power plants at three locations in India namely Rajasthan, Gujarat and Karnataka respectively. The identified risks pertained to the activities with respect to health, safety, environment, quality, site selection, investigation, planning, approvals, design, resources and maintenance of solar parks. The identified risks were categorized and grouped into 5 phases as following

1. Feasibility Study
2. Survey, investigation, master plan & concept report
3. Detailed Design & Specifications - Civil, Structural, Electrical, Scada & Transmission Line
4. Vendor Selection, Procurement, Construction & Commissioning
5. Operation & Maintenance

Further details of each phase with broad categories are depicted in Table-1, 2,3,4,5.

### 2.1 Analytical Hierarchy Process (AHP)

Analytical Hierarchy Process (AHP) is one of Multi Criteria decision making method that was originally extensively By [27] as a method to deliver a tioscales from paired comparisons. The input can be obtained from actual measurement such as price, weight etc. or from subjective opinion such as satisfaction feelings and preference in a quantitative magnitude scale. The limitations of this approach are a little in consistency as inputs from human judgment is relatively constrained. The ratio scales are derived from the principal Eigen vectors and the consistency indexes derived from the principal Eigen value.

Table 1: Broad Risks Identified under Phase-1

 No Activities with Risks a Letter of Intent (LOI) b Acceptance and Kick of Meeting & Finalization of the Scope and Deliverables. c Risks in Site location d Reconnaissance Survey of Site e Collection of Data f Inception Report Preparation (IR) & Submission g Review and Approval IR h Preparation & Submission of Draft Feasibility Report (DFR) i Presentation and Discussion j Approval of DFR k Submission of Final DFR

Table 2: Broad Risks Identified under Phase-2

 No Activities with Risks a Resource Mobilization & Establishing camp & site office. b Delay of Site Land Handover c Topographical Survey d Land Acquisition Risks e Environmental Risks f Resettlement and Rehabilitation Risks g Geo-tech Investigations h Data Analysis i Master Plan & Concept Report j Approval of Master Plan & Concept Report

Table 3: Broad Risks Identified under Phase-3

 No Activities with Risks a Revision in Master Plan b Risk in  DPR Preparation c Design of Civil Works d Design of Structural Works e Design of Electrical, SCADA& Transmission Line Works f Preparation & Submission of Draft Detailed Project Report (DDPR) including Tender Documents g Approval of DDPR& Tender documents h Submission of Final DPR & Tender documents

Table 4: Broad Risks Identified under Phase-4

 No Activities with Risks a Invitation of Tender & Award of Work b Letter of Intent (LOI) to Contractor c Acceptance and Kick of Meeting & Finalization of the Scope d Financial Closure Risks e Permit and Approval Risks f Civil Works Construction & Quality g Mechanical & Electrical Works & Quality h Safety

Table 5: Broad Risks Identified under Phase-5

 No Activities with Risks a Operation b Maintenance

The data analysis of the quantitative output of the qualitative attribute survey for each attribute of quality, risk assessment can be obtained using MAHP data analysis tool which is a multi-criteria decision making tool used to obtain ranks and outputs. Initially, a questionnaire is formulated to obtain the responses per tainting to “Probability of occurrence of risk” and “Impact” which is filled up by industry experts of a sample space of 200.These values range from 0to1 where 0 indicates nil probability of occurrence of a risk and impact while 1 indicates very high probability of occurrence of a risk and impact. For computational simplicity, the risk rating values obtained from questionnaire survey have been converted into “Saaty Scale” (1to5) where1 denotes least importance and 5 represent highest importance. Additionally, a level of risk nonsingular matrix is created for each item of the chosen subgroup (elements of row1 are divided by weights of respective column to that of row and soon). Probability weight based non-singular matrix is created for each item of the chosen subgroup. This process is repeated for all the three solar park sunder study in this case with the Saaty scales for each of the phases in terms of broad activities and sub-activities separately. Another constraint to this process is that it cannot differentiate between the better solar parks or can make any quantitative assertion among the case studies. The level of risk weights and probability weights are normalized. Normalized value= ${I I i=1 j=1,……n a a } 1 n MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaGG7bGaaCysaiaahMeadaqhaaWcbaGaaCyAaiabg2da9iaahgda aeaacaWHQbGaeyypa0JaaCymaiaacYcacqGHMacVcqGHMacVcaWHUb aaaOGaamyyamaaBaaaleaacaWHHbaabeaakiaac2hadaWcaaqaaiaa igdaaeaacaWHUbaaaaaa@47E1@$ , where n is the number of it emsunder the chosen subgroup.

The severity of the identification risks can be computed by the equation of EVM methodology: Risk Severity=Risk likelihood or probability of occurrence X Impact which lies from 0-1 demarcated by categories of flow (0-0.1), medium (0.11-0.2), high (0.21-0.3), very high (0.31-0.5), critical (0.51-0.7) and very critical (0.71-1).

In order to estimate the risk index and risk ranking, normalized weights have been estimated based on the total weights calculated for each phase. These normalized weights have been multiplied with risk severity to estimate the risk index which side noted as Risk Index=Risk Severity X Normalized Weights (Wn).

### 2.2 TOPSIS

The identified risks we reanalyzed with a Multi Criteria Decision Making (MCDM) technique termed as Fuzzy TOPSIS. TOP SIS is a multi-criteria decision analysis method, which was originally developed by [28] with further developments by [29]. TOPS Isis based on the concept that the chosen alternative should have the shortest geo metric distance from the Positive Ideal Solution (PIS) and the longest geo metric distance from the Negative Ideal Solution (NIS). It is a method of compensatory aggregation that compares a set of alternatives by identify in weights for each criterion. As the parameters or criteria are often of in congruous dimensions in multi-criteria problems it may create problems in evaluation. So, to avoid this problem a need o fuzzy system is necessary. Using fuzzy numbers in TOPSIS for criteria analysis the evaluation becomes simpler. Hence, Fuzzy TOPSIS is simple, realistic form of modeling and compensatory method which includes or excludes alternative solutions based on hard cut-off. It is important to note that for all the development phases of each solar parks the Saaty data used in the AHP is the input to the TOPSIS method where it is converted to the corresponding fuzzy numbers (whereSaatynumber1correspondstofuzzy (1,1,3), 2 corresponds to (1,3,5), 3 corresponds to (3,5,7), 4c corresponds to (5,7,9) and 5 corresponds to (7,7,9) respectively). In this approach the saaty numbers are converted into fuzzy number sets for the same parameters for different solar park. Then the mixed data set is created to combine the inputs from all sources of Rajasthan, Gujarat and Karnataka, where

The matrix $x ¯ ij MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qaceWH4bGbaebadaWgaaWcbaGaaCyAaiaahQgaaeqaaaaa@3940@$ is further converted to $r ij =( a j − c ij , a j − b ij , a j − a ij ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWHYbWaaSbaaSqaaiaahMgacaWHQbaabeaakiabg2da9iaacIca daWcaaqaaiaahggadaqhaaWcbaGaaCOAaaqaaiabgkHiTaaaaOqaai aahogadaWgaaWcbaGaaCyAaiaahQgaaeqaaaaakiaacYcadaWcaaqa aiaahggadaqhaaWcbaGaaCOAaaqaaiabgkHiTaaaaOqaaiaadkgada WgaaWcbaGaaCyAaiaahQgaaeqaaaaakiaacYcadaWcaaqaaiaahgga daqhaaWcbaGaaCOAaaqaaiabgkHiTaaaaOqaaiaadggadaWgaaWcba GaaCyAaiaahQgaaeqaaaaakiaacMcaaaa@4F28@$

where $a j − =min( a ij ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWHHbWaa0baaSqaaiaahQgaaeaacqGHsislaaGccqGH9aqpciGG TbGaaiyAaiaac6gacaGGOaGaamyyamaaBaaaleaacaWGPbGaamOAaa qabaGccaGGPaaaaa@4141@$ as risk is only a cost criteria and not a beneficial one thus analysis is preferred using the lower values. Moreover the weight age values (wj) are obtained from the experts just like the saaty value and further converted into fuzzy numbers. Then, the matrix is obtained. Henceforth,

are evaluated obtained. These new variables help in obtaining the distance parameters which are denoted as

Finally, the closeness coefficient is calculated using $C C i = d − d * + d − MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWHdbGaaC4qamaaBaaaleaacaWHPbaabeaakiabg2da9maalaaa baGaaCizamaaCaaaleqabaGaeyOeI0caaaGcbaGaaCizamaaCaaale qabaGaaiOkaaaakiabgUcaRiaahsgadaahaaWcbeqaaiabgkHiTaaa aaaaaa@40B8@$ . The higher the CCi value higher the ranking.

## 3.0 Results & Discussions

### 3.1 AHP results

The outcome of the analysis towards assessment of risk index for each project as obtained from the analysis as explained in the methodology section are finally tabulated in Table- 11, 12, 13, 14. The gist of conclusions can be summarized as

### a) Project in Rajasthan:

Highest risk is observed in Phase-2 i.e. SURVEY, INVESTIGATION, MASTER PLAN & CONCEPT REPORT Stage of the project on both Broad Activities as well as Sub Activities.

### b) Project in Gujarat:

Highest risk is observed inPhase-4 i.e. VENDOR SELECTION, PROCUREMENT, CONSTRUCTION AND COMMISSIONING Stage of the projection both Broad Activities as well as Sub Activities.

### c) Project in Karnataka:

Highest risk is observed in Phase-2i.e. SURVEY, INVESTIGATION, MASTERPLAN & CONCEPT REPORT Stage of the project based on Sub Activities and Phase 1 i.e. FEASIBILITY STUDY based on Broad Activities.

### 3.2 TOPSIS results

The analysis and interpretation of data obtained using the TOPSIS method is tabulated inTable-15 where the higher value of C Cede notes higher risk factors and its thereby rankings in terms of risk, higher number denoting higher risk.

It can be seen that if the analysis is performed based on broader activities then phase-3 is the highest in terms of risk. Similarly, inference can also be drawn from looking at the analysis is done with sub-activities where the data shows that phase-5 has the maximum risk. This is in accordance that phase-5 deals with subactivities with bigger natural hazards and uncertainties. How- ever, if the lowest risk is observed that conclusion remains the same whether broad activities or sub-activities are looked into Based on broad activities phase-1 is at the lowest most risk as it involves post project maintenance which grebes with the general understanding. With respect to sub-activities as well, Phase-1 shows the least most risk. In general, phase-3 shows relatively higher amount of risk from both the broad activities and sub-activities analysis. This contradicts with the AHP results were in two of the solar parks phase-2 was found to have greatest risk rankings. However in TOPSIS analysis, phase-2 stays third in terms of both broad and subactivities showing medium level of risk involved. The advantage of this analysis is that it takes into account all the solar park locations at once and provides a more generic and reliable understanding of the risk on the whole. Where as in AHP the solar parks can only be studied separately. In other words there is no way to inculcate the data from Rajasthan Gujarat and Karnataka all in one go for the AHP analysis. The TOPSIS approach combines the fuzzy numbers from all solar parks in the first step in formulating the X ij and thereby generalizes the parametric fuzzy numbers o of all the identified variables of all solar parks into a single chart. A sample calculation of Phase-5 is shown in Table-16.

Table 6: Sub Activity Risks Identified underPhase-1

 No Activities with Risks 1 Delay in Issue of LOI 2 Wrong Details of Contract 3 Delay in responding to Wrong details by Client 4 Delay in Acceptance of LOI 5 Delay in conducting Kick of Meeting 6 Gaps in scope of work 7 Improper objectives Scope & Deliverables finalisation 8 Proximity to International border 9 Proximity to wild life sanctuary 10 Presence of forest land 11 Proximity to eco sensitive zone 12 Proximity to Historical monuments, Place of worship etc. 13 Presence of sensitive lands within the project boundary 14 Highly undulating and rocky terrain. 15 Presence of Built-up Close to Project 16 Access to Site 17 Ground Water Table 18 Impact on Environment 19 Social Impact 20 Availability of Land 21 Permission from Government 22 Presence of low laying area. 23 Identification of Different Site for Reconnaissance 24 Wrongly Identification of Site Boundary &Orientation 25 Missing of Key Data during  Reconnaissance survey 26 Improper Data Collection 27 Inadequate Data Collection 28 Misinterpretation the Scope of Work 29 Defining of Unrealistic Approach  &Methodology 30 Insufficient Time Allocation for Investigation & Design 31 Delay in Submission of IR 32 Review by non-technical professional 33 Delay in review & forwarding the observations 34 Delay in approval of IR 35 Improper Approach & Methodology for Feasibility Report 36 Insufficient Survey & Investigation 37 Mistakes in Conducting Survey & investigations 38 Hydraulic and hydrological Investigations 39 Recommendation of Foundation Type 40 Poor Interpretation of Data 41 Wrong Planning of Master Plan 42 Presence of Utilities 43 Raw Material Sources 44 Preliminary Design 45 Drawings & Documentation 46 Mistake in Quantity Calculations 47 Adopting Wrong Schedule of Rates for Estimation 48 Delay in Preparation of Draft Feasibility Report 49 Delay in Submission of Draft Feasibility Report 50 Presenting Wrong Details about Project 51 Discussions of un-related points during presentation 52 Authenticity of Clients Observations & Incorporation in Report 53 Review by non-technical professional 54 Delay in review & forwarding the observations 55 Delay in approval of DFR 56 Delay in Receiving Comments/ Observation of Draft DFR 57 Delay in Attending the Comments/ Observation of Draft DFR 58 Delay in Submission of Final Feasibility Report

Table 7: Sub activity Risks Identified under Phase-2

 No Activities with Risks 1 Access to project office is improper 2 Delay in Marking of site 3 Delay in construction of project office 4 Lack of Conducive environment in the office 5 Lack of basic amenities and infrastructure 6 Delay due to all permits and procedures are in place before any work commence 7 Delay in setup of project site office, Lay down area and site establishment 8 Delay in Site Land handover 9 Delay in Taking over of the site 10 Delay in Survey &Investigation 11 Delay in Detailed Project Report (DPR) 12 Delay in Construction 13 Joint boundary demarcation 14 Delay due to Wrong Identification of Site 15 Delay due to Site Handing over for work 16 Mistake in Establishing Horizontal &vertical Control points 17 Deployment of unqualified surveyors 18 Deployment of poorly calibrated equipments 19 Not connecting to national grid such(GTS) and Mean Sea Level 20 Wrong project boundary Identification 21 Omission of  major topographical details 22 Elevation of land is not properly done through survey or equipments 23 Political interference 24 Faulty Revenue Survey 25 Delay in finalizing temporary rehabilitation schemes 26 Public interference for changing the Site 27 Interference of environmental activists 28 Delay due to inter department a issues 29 Delay in construction of diversion roads for existing traffic 30 Cost of Compensation 31 Problems with the physical possession of land 32 Deforestation 33 Reduction in Intensity of Rainfall 34 Ecological Imbalance 35 Increase in Surrounding Area temperature 36 Resettlement site not accepted by affected parties 37 Resettlement site very costly 38 Litigation by affected parties or Litigation in the Site Identified for R&R 39 Resistance and agitation by political parties 40 Delay in Final is action of Site and Locations of Investigations 41 Delay in Deployment of Required Machineries 42 Deployment of unqualified Personnel for Investigation 43 Improper/Inadequate/Insufficient Investigation 44 Collection of sample sand testing 45 Poor interpretation of data 46 In adequate foundation design recommendations 47 Missing of Soil Resistivity Data 48 Poor assessment of catchment area and historical floods 49 Deficient Hydrological Report 50 Computation of Finished Grade Level for the plant 51 Wrong QA& QC Report 52 Finalization of Route for Transmission Lines 53 Processing of the data and preparation of base maps in different layers. 54 Establishment of the documentation 55 Preparation of Engineering documents in line with project requirement 56 Delay in Finalization of Master Plan &Concepts 57 Lack of Involvement of Skilled Professional 58 Misunderstanding of Data Analysis 59 Preparation & Submission of Master Plan & Concept Report for Approval 60 Presenting Wrong Details about Project 61 Discussions of un-related points during presentation 62 Authenticity of Clients Observations &on Master Plan & Concept Report 63 Review by non-technical professional 64 Delay in review &forwarding the observations 65 Delay in approval of Master Plan & Concept Report

Table 8: Sub activity Risks identified underPhase-3

 No Activities with Risks 1 Minor Level Modification in Master Plan 2 Medium Level Modification in Master Plan 3 Large Level Modification in Master Plan 4 Delay in Finalization due to extent of Revision 5 Delay in approval of Revised Master Plan 6 Wrongly identification of Works 7 Lack of Coordination among different teams 8 Inadequate data &information 9 Un economical Design 10 Defective Design 11 In complete Detailing 12 Missing of Design & Specifications for Works 13 Improper Design of Site Leveling &Grading Plan 14 Identification of Type of Fencing/ Compound 15 Discarding importance of Roads, Drainage &Cross Drainage Structures 16 Faulty Design of Structure Foundation for the Module Mounting 17 Location &Size of Control room 18 Location &Size of Security/Guardroom 19 Source & Raw water storage including distribution 20 Design of Module Mounting structure 21 Design of Control Room 22 Design of Maintenance Staff Accommodation 23 Design of Security Cabins 24 Design of Main entrance Gate 25 Array layout including optimization 26 Cable Trenches 27 Switch Yard 28 Ear thing Layout 29 Overall SLD 30 HV System SLD 31 Overall PV array layout 32 Area power Ear thing &Grounding layout 33 SCADASLD 34 Substation 35 Auxiliary power 36 Transmission line 37 Site Lighting 38 Building Lighting 39 Lightening arrestor 40 Improper Approach &Methodology 41 Improper Use of Survey &Investigation Data 42 Delay in submission of drawings by detailed design consultant Civil Works 43 Delay in submission of drawings by detailed design consultant Structure Works 44 Delay in submission of drawings by detailed design consultant Electrical Works 45 Adopting Wrong Schedule of Rates for Estimation 46 Lack of accuracy in internal detailed estimate 47 Deficiency in Drawings 48 Short comings in internal detailed estimate/provisions 49 Delay in Preparation of DDPR &Tender documents 50 Delay in Submission of DDPR & Tender documents 51 Review by non-technical professional 52 Delay in review &forwarding the observations 53 Delay in approval of DDPR & Tender documents 54 Delay in Receiving Comments/Observation of Draft DPR& Tender documents 55 Delay in Attending the Comments/Observation of Draft DPR &Tender documents 56 Delay in Submission of Final Detailed Project Report &Tender documents

Table 9: Sub activity Risks Identified underPhase-4

 No Activities with Risks 1 Delay in  preparation and approval of tender document 2 Two packet system (Technical and financial evaluation)is not implemented 3 Delay in  issuing NIT(Notice Inviting Tender) 4 Delay in  Pre-Bid Meeting 5 Delay in  Response to the Queries of Bidders 6 Postponement of Tender Submission Date 7 Variations by the client 8 Improper evaluation of Tender Documents of Bidders 9 Delay in  Award of Contract to Successful Bidder 10 Delay in  Issue of LOI to Contractor 11 Wrong Details of Contract 12 Delay in  responding to Wrong details by Client 13 Delay in  Acceptance of LOI by Contractor 14 Delay in  conducting Kick of Meeting 15 Improper objectives Scope & Deliverables finalization 16 Delay in  mobilization of resources by contractor 17 Project not bankable 18 Lenders not comfortable with project viability 19 Adverse investment climate 20 Delay in  contractual clearances 21 Delay in  projects specific orders and approvals 22 Delay in  clearance from environment a land forest departments 23 Delay in the approval of relocation of major utilities (telecom cables, electrical cables, storm water drains, sewer lines). 24 Un suitable construction programmed planning (e.g. Sequence of activities is not properly planned) affecting workings chem. and quality of work. 25 Delay in Labor induction by doctor and safety officer. 26 Delay inperforms100%pre –checks and pre-inspection before the GEC do the official checks and inspections. 27 Delay in submission of GFC drawings by contractor. 28 Delay in granting approval of drawings. 29 Drawing bullet in system is not implemented onsite for drawing progress/Implementation tracking. 30 Longer Lead for Constriction Materials. 31 Delay in  Supply of Materials from Vendors 32 Increase in  Cost of Materials(Steel, Cement) 33 Risks of minor/major accidents during Work 34 In effective control and management 35 Delay in  Start of Construction Activity 36 Defect in  Level Carrying for Site  Work 37 Defects in  Foundations for Module Mounting structure 38 Defective works in  Other Civil Works 39 Improper Drainage Facility 40 In adequate program scheduling 41 Variation of construction programs 42 Lack of coordination between project participants 43 Incomplete approval and other documents 44 Poor construction plan 45 Insufficient experience and skill in  construction works 46 Unstable supply of critical construction materials 47 List of Approved materials/brands and vendors is not prepared 48 Defects in  Module Mounting structure 49 Improper Erection/Mounting of modules 50 Defect in  Area Power Ear thing & Grounding Layout 51 Defect in  Cables &Trenches 52 Defects in  Overall SLD &HV System SLD 53 Defective Switch Yard 54 Delay in  Implementation of SCADA 55 Substation 56 Auxiliary power 57 Transmission line 58 Yard Lighting 59 Building Lighting 60 Lightening arrestor 61 Main entrance Gate 62 Security/Guardroom 63 Raw water storage including distribution and connection 64 String extension cabling 65 Module Connector 66 Combiner Box 67 ICB to inverter cabling 68 PCU 69 Data logger along with PC 70 Weather Stn-Pyrano, A nemo & Temp sensor 71 Ear thing System/Lighting System 72 Documentation, Department approvals, Statutory clearance 73 Testing &Pre-commissioning 74 Staffing, SOP &Training 75 Safety of Workers during Construction 76 Safety of Machineries 77 Safety of Plant after Construction

Table 10: Sub activity Risks Identified underPhase-5

 No Activities with Risks 1 Reduction in Power Generation due to Variation in Solar Energy 2 Defect in the Solar Panels 3 High Rainfall 4 High Wind  Causing Dust cover on Panels 5 Fire Hazards 6 Robbery of Equipment 7 Unskilled Operational  Staff 8 Delay in Supply of Materials  for Maintenance 9 Poor Maintenance by Operating  Staff 10 Non Availability of Spare parts 11 Scarcity of Water 12 Delay in attending  the break-down in operation

Table 11: Risk Severity of Broad Activities Risk Factors Quality Parameters (EVM Methodology)

 No Risk Description of Task Project in Rajasthan Project in Gujarat Project in Karnataka Severity Risk Severity (Qualitative) Severity Risk Severity (Qualitative) Severity Risk Severity (Qualitative) 1 PHASE-1 :FEASIBILITY STUDY 0.39 Very High 0.25 High 0.39 Very High 2 PHASE-2     :    SURVEY, INVESTIGATION, MASTER PLAN&CONCEPT REPORT 0.56 Critical 0.30 High 0.33 Very High 3 PHASE-3   :   DETAILED DESIGN AND SPECIFICATIONS -CIVIL, STRUCTURAL, ELECTRICAL, SCADAAND TRANSMISSIONLINE 0.30 High 0.39 Very High 0.30 High 4 PHASE-4     :    VENDOR SELECTION, PROCUREMENT,CONSTRUCTION AND COMMISSIONING 0.33 Very High 0.42 Very High 0.23 High 5 PHASE-5  : OPERATION &MAINTENANCE   OF SOLAR PLANTS 0.10 Low 0.09 Low 0.07 Low

Table 12: Risk Severity of Sub Activities Risk Factors Quality Parameters (EVM Methodology)

 No Risk Description  of Task Project in Rajasthan Project in Gujarat Project in Karnataka Severity Risk Severity (Qualitative) Severity Risk Severity (Qualitative) Severity Risk Severity (Qualitative) 1 PHASE-1 :FEASIBILITY STUDY 0.39 Very High 0.25 High 0.39 Very High 2 PHASE-2     :    SURVEY, INVESTIGATION, MASTER PLAN&CONCEPT REPORT 0.56 Critical 0.30 High 0.33 Very High 3 PHASE-3   :   DETAILED DESIGN AND SPECIFICATIONS -CIVIL, STRUCTURAL, ELECTRICAL, SCADAAND TRANSMISSIONLINE 0.30 High 0.39 Very High 0.30 High 4 PHASE-4     :    VENDOR SELECTION, PROCUREMENT,CONSTRUCTION AND COMMISSIONING 0.33 Very High 0.42 Very High 0.23 High 5 PHASE-5  : OPERATION &MAINTENANCE   OF SOLAR PLANTS 0.10 Low 0.09 Low 0.07 Low

Table 13: Final Risk Index for factors Associated with Broad Activities Risk Quality Parameters

 No Risk Description  of Task Project in Rajasthan Project in Gujarat Project in Karnataka Final Rank Index Risk Ranking Final Rank Index Risk Ranking Final Rank Index Risk Ranking 1 PHASE-1 :FEASIBILITY STUDY 0.108 2 0.070 4 0.105 1 2 PHASE-2     :    SURVEY, INVESTIGATION, MASTER PLAN&CONCEPT REPORT 0.1531 1 0.0826 2 0.0935 2 3 PHASE-3   :   DETAILED DESIGN AND SPECIFICATIONS -CIVIL, STRUCTURAL, ELECTRICAL, SCADAAND TRANSMISSIONLINE 0.060 4 0.077 3 0.059 3 4 PHASE-4     :    VENDOR SELECTION, PROCUREMENT,CONSTRUCTION AND COMMISSIONING 0.0663 3 0.0834 1 0.0464 4 5 PHASE-5  : OPERATION &MAINTENANCE   OF SOLAR PLANTS 0.005 5 0.004 5 0.003 5

Table 14: Final Risk Index for factors Associated with Sub-Activities Risk Quality Parameters

 No Risk Description  of Task Project in Rajasthan Project in Gujarat Project in Karnataka Final Rank Index Risk Ranking Final Rank Index Risk Ranking Final Rank Index Risk Ranking 1 PHASE-1 :FEASIBILITY STUDY 0.084 3 0.054 4 0.086 2 2 PHASE-2     :    SURVEY, INVESTIGATION, MASTER PLAN&CONCEPT REPORT 0.138 1 0.074 3 0.088 1 3 PHASE-3   :   DETAILED DESIGN AND SPECIFICATIONS -CIVIL, STRUCTURAL, ELECTRICAL, SCADAAND TRANSMISSIONLINE 0.063 4 0.080 2 0.060 4 4 PHASE-4     :    VENDOR SELECTION, PROCUREMENT,CONSTRUCTION AND COMMISSIONING 0.093 2 0.121 1 0.061 3 5 PHASE-5  : OPERATION &MAINTENANCE   OF SOLAR PLANTS 0.004 5 0.004 5 0.003 5

Table 15: FinalRiskIndexforRiskQualityParametersforvariousphasesusingTOPSIS method (decimal values rounded off)

 No Phase no CCi based on broad   activities Ranking index CCi based on Sub activities Ranking index 1 PHASE-1 : FEASIBILITY STUDY 0.099 5 0.16 5 2 PHASE-2 : SURVEY, INVESTIGATION,  MASTER PLAN  &  CONCEPT  REPORT 0.18 3 0.20 3 3 PHASE-3   :  DETAILED   DESIGN AND  SPECIFICATIONS - CIVIL, STRUCTURAL, ELECTRICAL, SCADA   AND  TRANSMISSION LINE 0.29 1 0.23 2 4 PHASE-4 : VENDOR SELECTION, PROCUREMENT, CONSTRUCTION AND  COMMISSIONING 0.23 2 0.18 4 5 PHASE-5 : OPERATION & MAINTENANCE OF SOLAR  PLANTS 0.14 4 0.42 1

Table 16: Sample TOPSIS calculation forphase-5 (decimal values rounded off)

 No Activities   with risk Rajasthan Gujarat Karnataka Xij Rij Wj Vij d+ (FPIS) d− (FNIS) Cci a Operation 5,7,9 5,7,9 3,5,7 3 6.33 9 0.11 0.16 0.33 1 3 5 0.11 0.47 1.67 3.34 0.95 0.22 1 Reduction    in Power Generation due to Variation    in Solar Energy 7,7,9 3,5,7 3,5,7 3 5.67 9 0.11 0.18 0.33 1 3 5 0.11 0.53 1.67 3.33 0.96 0.22 2 Defect in the Solar Panels 7,7,9 7,7,9 7,7,9 7 7.00 9 0.11 0.14 0.14 1 3 5 0.11 0.43 0.71 4.29 0.00 0.00 3 High Rainfall 3,5,7 3,5,7 3,5,7 3 5.00 7 0.14 0.20 0.33 1 3 5 0.14 0.60 1.67 3.33 0.97 0.23 4 High Wind Causing  Dust  cover on Panels 3,5,7 3,5,7 3,5,7 3 5.00 7 0.14 0.20 0.33 1 3 5 0.14 0.60 1.67 3.33 0.97 0.23 5 Fire Hazards 3,5,7 7,7,9 1,3,5 1 5.00 9 0.11 0.20 1.00 1 3 5 0.11 0.60 5.00 0.00 4.29 1.00 6 Robbery of Equipment 5,7,9 7,7,9 1,3,5 1 5.67 9 0.11 0.18 1.00 1 3 5 0.11 0.53 5.00 0.07 4.29 0.98 7 Unskilled Operational Staff 5,7,9 5,7,9 5,7,9 5 7.00 9 0.11 0.14 0.20 1 3 5 0.11 0.43 1.00 4.00 0.29 0.07 8 Delay in Supply of Materials for Maintenance 3,5,7 5,7,9 1,3,5 1 5.00 9 0.11 0.20 1.00 1 3 5 0.11 0.60 5.00 0.00 4.29 1.00 b Maintenance 5,7,9 5,7,9 5,7,9 5 7.00 9 0.11 0.14 0.2 1 3 5 0.11 0.43 1.00 4.00 0.29 0.07 9 Poor Maintenance by Operating Staff 7,7,9 7,7,9 3,5,7 3 6.33 9 0.11 0.16 0.33 1 3 5 0.11 0.47 1.67 3.34 0.95 0.22 10 Non Availability of Spare parts 5,7,9 3,5,7 5,7,9 3 6.33 9 0.11 0.16 0.33 1 3 5 0.11 0.47 1.67 3.34 0.95 0.22 11 Scarcity of Water 5,7,9 3,5,7 7,7,9 3 6.33 9 0.11 0.16 0.33 1 3 5 0.11 0.47 1.67 3.34 0.95 0.22 12 Delay in attending the breakdown in operation 5,7,9 5,7,9 1,3,5 1 5.67 9 0.11 0.18 1.00 1 3 5 0.11 0.53 5.00 0.07 4.29 0.98 A+ 0.11 0.60 5.00 A− 0.11 0.43 0.71

## 4.0 Conclusions

After careful scrutiny of the results from AHP and TOPSIS analysis the following conclusions are drawn. TOPSIS has substantial advantages over the conventional AHP process in terms that it can evaluate the data using fuzzy numbers from various decision makers or sources. In this case, TOPSIS approach combines the input of the solar parks from Rajasthan, Gujarat and Karnataka and combines them with the same activities and sub-activities and provides an ensemble interpretation of the parameters. The results of maximum and minimum risk phase are more coherent in TOPSIS than in AHP. AHP concludes Phase-2 or Phase-4 as the highest risk depending upon the solar park. This in coherency is not well appreciated where the quality of results lie on external factors. The highest risk phase should be independent of the geo graphical location as the parameters in the study are same for any solar park. However, TOPSIS clearly shows both the phase2 and 4 to be of medium risk. It is very evident from the comparison and the analysis that TOPSIS is a more refined approach in such study of various solar power parks using the same identified parameters.

Furthermore, AHP is limited to the study of each solar park separately and there exist no way to interpret one with respect to another. Asset can be seen the results of AHP of each solar park are shown separately and the most critical case varies case to case which may not be the true representation of the actual problem under consideration. Moreover, for standalone projects or niche markets of study AHP is better suited in the abs hence of other related projects that can provide the same range of identifiable parameter across the board.

Phase-3 shows the maximum risk in terms of both the broad and sub activities for TOPSIS approach. However the lowest risks belong to Phase-1 for both the broad and sub-activities. This can be justified as the execution phase and the operation & maintenance phases have the maximum amount of uncertainties. The comparison between the two approaches how that they different their conclusions by a substantial margin.