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

2CEPT University, Ahmedabad, 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

x ¯ ij =(min( a ij k ), 1 k k=1 3 b ij k , max( c ij k )) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qadaqdaaqaaiaadIhaaaWaaSbaaSqaaiaahMgacaWHQbaabeaakiab g2da98aacaGGOaGaciyBaiaacMgacaGGUbGaaiika8qacaWHHbWaa0 baaSqaaiaahMgacaWHQbaabaGaaC4Aaaaak8aacaGGPaWdbiaacYca daWcaaqaaiaaigdaaeaacaWHRbaaamaaqadabaGaaCOyamaaDaaale aacaWHPbGaaCOAaaqaaiaahUgaaaaabaGaam4Aaiabg2da9iaaigda aeaacaaIZaaaniabggHiLdGccaGGSaGaaeiiaiaab2gacaqGHbGaae iEa8aacaGGOaWdbiaahogadaqhaaWcbaGaaCyAaiaahQgaaeaacaWH RbaaaOWdaiaacMcacaGGPaaaaa@5975@

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 V ij r ij * w j     MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGwbWaaSbaaSqaaiaadMgacaWGQbaabeaakiaab2dacaqGGaGa amOCamaaBaaaleaacaWGPbGaamOAaaqabaGccaGGQaGaam4DamaaBa aaleaacaWGQbaabeaakiaabccacaqGGaGaaeiiaaaa@4229@ is obtained. Henceforth,

A + = max ( V ij3 ) and  A = min ( V ij1  ) MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWHbbWaaWbaaSqabeaadaahaaadbeqaaiabgUcaRaaaaaGccqGH 9aqpciGGTbGaaiyyaiaacIhadaWgaaWcbaaabeaakiaacIcacaWHwb WaaSbaaSqaaiaahMgacaWHQbGaaC4maaqabaGccaGGPaGaaeiiaiaa hggacaWHUbGaaCizaiaabccacaWHbbWaaWbaaSqabeaacqGHsislaa GccqGH9aqpciGGTbGaaiyAaiaac6gadaWgaaWcbaaabeaakiaacIca caWHwbWaaSbaaSqaaiaahMgacaWHQbGaaCymaaqabaGccaGGGcGaai ykaaaa@5187@ are evaluated obtained. These new variables help in obtaining the distance parameters which are denoted as

d + = 1 3 ( V ij A * ) 2  and  d = 1 3 ( V ij A ) 2 MathType@MTEF@5@5@+= feaagKart1ev2aaatCvAUfeBSjuyZL2yd9gzLbvyNv2CaerbuLwBLn hiov2DGi1BTfMBaeXatLxBI9gBaerbd9wDYLwzYbItLDharqqtubsr 4rNCHbGeaGqiVu0Je9sqqrpepC0xbbL8F4rqqrFfpeea0xe9Lq=Jc9 vqaqpepm0xbba9pwe9Q8fs0=yqaqpepae9pg0FirpepeKkFr0xfr=x fr=xb9adbaqaaeGaciGaaiaabeqaamaabaabaaGcbaaeaaaaaaaaa8 qacaWGKbWaaWbaaSqabeaacqGHRaWkaaGccqGH9aqpdaGcaaqaamaa laaabaGaaGymaaqaaiaaiodaaaWaaabqaeaacaGGOaGaaCOvamaaBa aaleaacaWHPbGaaCOAaaqabaGccqGHsislcaWGbbWaaWbaaSqabeaa caGGQaaaaOGaaiykaaWcbeqab0GaeyyeIuoakmaaCaaaleqabaGaaG OmaaaaaeqaaOGaaeiiaiaabggacaqGUbGaaeizaiaabccacaWGKbWa aWbaaSqabeaacqGHsislaaGccqGH9aqpdaGcaaqaamaalaaabaGaaG ymaaqaaiaaiodaaaWaaabqaeaacaGGOaGaaCOvamaaBaaaleaacaWH PbGaaCOAaaqabaGccqGHsislcaWGbbWaaWbaaSqabeaacqGHsislaa GccaGGPaaaleqabeqdcqGHris5aOWaaWbaaSqabeaacaaIYaaaaaqa baaaaa@5790@

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.

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