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international journal of critical infrastructure protection 36 (2022) 100507

Available online 30 December 2021 1874-5482/© 2022 Elsevier B.V. All rights reserved.

Risk assessment and mitigation for electric power sectors: A developing country’s perspective

Obaid ur Rehman a, Yousaf Ali a, *, Muhammad Sabir b

a School of Management Sciences, Ghulam Ishaq Khan Institute of Engineering Sciences and Technology, Topi, Swabi, KPK, Pakistan b NUST Business School, National University of Science and Technology, Islamabad, Pakistan

A R T I C L E I N F O

Keywords: Risk assessment Risk mitigation Fuzzy set theory FUCOM VIKOR QFD

A B S T R A C T

The electric power sector is the driving force behind a country’s economy and disruptions in its services have dire consequences. The purpose of this study is to identify the risk mitigation measures that should be incorporated by the electric power sector of Pakistan. These risk mitigation strategies were identified while considering the risks that are Most Probable, Severe, Costly, Difficult to Detect, and Control. This study employed Fuzzy Multi-Criteria Decision-Making (MCDM) techniques for the assessment of risks. The criteria for risk evaluation were gauged through Fuzzy Full Consistency Method (FUCOM) and Fuzzy VIKOR approach was used for ranking these risks. Moreover, the risk mitigation strategies were evaluated using the Fuzzy Quality Function Deployment (QFD) method, considering the previously prioritized risks. The analysis ranked Corruption, Circular Debts, Outdated Infrastructure, Energy Losses, and Lack of Research and Development as the most critical risks. For risk miti- gation measures, Incorporation of the Internet of Things (IoT), Building an Investor-Friendly Environment, Improved Coordination between Organisations, and Maximizing the Energy Efficiency Potential gained prefer- ence, among other measures. The study proposed a novel framework for risk assessment and evaluation of risk mitigation strategies. Moreover, it fills a research gap in its application by focusing on the electric power sector of Pakistan, a developing country.

1. Introduction

In the current era, the economic progress of a country can be measured directly in terms of the development of its electric power sector [43]. The modern industrial requirements and domestic lifestyles, both are highly dependent on electrical energy. Moreover, the devel- opment of the electric power sector can be measured in terms of the diversity of the generation mix, the effectiveness of the protection mechanisms, stability in the supply, and the maintenance of the quality of energy. These factors are either directly or indirectly, related to the increased energy security of a country.

Due to the crucial role of electrical energy in the national economies, it is necessary to ensure a steady supply of electrical energy. For this purpose, it is incumbent upon the policymakers to design and install effective risk mitigation mechanisms as precautionary measures [6]. Risk can be defined as an uncertain event, whose future direction cannot be ascertained and thus it may lead to disruptions in the supply of en- ergy, or it may compromise the quality of supply of energy. Moreover, risks can occur for technical, natural, human, or environmental reasons.

In order to mitigate the effect of these risks, risk mitigation mechanisms are developed. The risk management processes range from the identi- fication and prioritization of risks to the determination of risk mitigation measures [71].

The developing countries of the world are more vulnerable to risks and disruptions as compared to developed countries. This is due to the disparity in socioeconomic conditions, resource potential, technological capabilities, and skilled workforce. The electric power sectors of these countries are also more prone to threats and disruptions. Besides, the disruptions in this sector are more costly because of their transmissive nature [59]. The interruption in the supply of electrical energy affects almost all sections of the economy, ranging from the industrial complex to healthcare facilities. Therefore, one of the primary steps in the revival of the developing economies should be increasing energy security in electrical energy infrastructure through risk preventive mechanisms [14].

Pakistan is a developing country, and its economy is currently engulfed by a multitude of issues. These issues range from inadequate economic policies to a variety of technical limitations in the

* Corresponding author. E-mail addresses: gem1971@giki.edu.pk (O. Rehman), yousafkhan@giki.edu.pk (Y. Ali).

Contents lists available at ScienceDirect

International Journal of Critical Infrastructure Protection

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https://doi.org/10.1016/j.ijcip.2021.100507 Received 6 May 2021; Received in revised form 20 December 2021; Accepted 23 December 2021

International Journal of Critical Infrastructure Protection 36 (2022) 100507

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infrastructure. The electric power sector of the country is one such case. The sector is overburdened, outdated, lagging in terms of technological development, and prone to a variety of technical and non-technical risks. It has been rightly assumed by the policymakers and researchers that the primary step in the revival of the national economy should be in the form of dedicated and adequate focus on the electric power sector. Moreover, the risk mitigation aspect of Pakistan’s electric power sector has been severely neglected. This neglect has resulted in frequent breakdowns on the national and provincial levels, causing huge losses to the national treasury. This is exacerbated by the fact that the country is susceptible to various climatic, economic, technical, and administrative risks. Thus, there is an urgent need to develop a risk identification and mitigation framework for Pakistan’s electric power sector.

This research study was aimed at the development of a risk assess- ment framework for the electric power sectors of developing countries. It also aimed to recommend the necessary and effective risk mitigation strategies for these countries. Moreover, Pakistan is a developing country, and its electric power sector is prone to numerous threats, therefore this study focused on Pakistan’s electric power sector. Furthermore, this study is divided into two major parts. The first part focuses on the risk assessment process through the Fuzzy Multi-Criteria Decision-Making (MCDM) method and the second part evaluates the risk mitigation measures through the Fuzzy Quality Function Deployment (QFD) technique.

The Fuzzy MCDM method is further, a combination of two tech- niques. The Fuzzy Full Consistency Method (FUCOM) was employed for the evaluation of criteria for risk assessment. The criteria in this study are limited to the Probability of Occurrence, Severity, Economic Cost, Difficulty of Detection, and Controllability. The Fuzzy FUCOM analysis assigned relative importance weight to each criterion. Furthermore, the Fuzzy VIKOR technique was employed for the evaluation and ranking of risks, based on the mentioned criteria. The alternatives are the risks that can occur in the electric power sectors of developing countries. These risks were identified from a literature review and a total of 45 risks were shortlisted and considered for assessment (table 1). The fuzzy set theory was used as these techniques are reliant on the qualitative judgments of experts which involve uncertainty. The fuzzy set theory is a tool for mitigation of the uncertainty or imprecision, that is inherent in expert opinion.

The second part of this study evaluated the risk mitigation measures in the light of the top twenty risks, which were ranked through the Fuzzy

MCDM analysis. The primary purpose of the QFD method is to incor- porate customer requirements (WHATs) into the product design process (HOWs). Lately, the scope of QFD has been diversified and it is employed in scenarios where the requirements and strategies of a process have been clearly determined. In this research study, the risks were consid- ered as WHATs, and the risk mitigation strategies were considered as HOWs (table 3). In addition, the fuzzy set theory was incorporated in the analysis as QFD is also dependent on expert opinion. The risk mitigation strategies were identified from the literature review and fifteen such strategies were finalized for the analysis. The Fuzzy QFD analysis resulted in the ranking of these strategies.

This study contributes to the existing literature by developing a novel method for risk assessment and mitigation, comprising of hybrid MCDM techniques. The application of the three MCDM techniques in this particular order and purpose, for risk assessment and mitigation, was not found in the literature. The proposed method can also be adopted for enhancing the resilience of other sectors. Furthermore, this study highlights the risk assessment and mitigation aspect of Pakistan’s elec- tric power sector. The analyses would aid in the development of a bolstering mechanism for the country’s electric power sector, as well as other developing countries. Thus, the contribution of this study is twofold: it develops a mechanism for assessment and mitigation of risks, and it proposes recommendations for enhancing the resilience of Paki- stan’s electric power sector.

The rest of this research paper is organized as follows; In the Liter- ature Review section, the previous research studies conducted in the context of Pakistan’s electric power sector, risk assessment of the electric power sector, and studies relevant to the methodology are discussed. It is followed by the Data Collection and Methodology section, where the data collection process and the techniques employed in this study are described. Furthermore, in the Results and Discussion section, the re- sults derived from the analyses are presented and discussed. In the end, the Conclusion of the study is presented.

2. Literature review

The electric power sector of Pakistan has been facing a myriad of problems ranging from energy insecurity to adverse environmental ef- fects associated with the generation of energy. Therefore numerous research studies have been conducted targeting reform in Pakistan’s electric power sector. A research study performed a Strength,

Table 1 Risks identified from the literature review.

Technical Risks Environmental Risks Economic Risks (I) Economic Risks (II) Administrative/ Policy Risks

Social Risks

Inappropriate Generation Mix

Droughts/ Excessive Rainfalls Inadequate Investment in Power Sector

Depreciation of Equipment Inadequate Government Policy Formulation

Social Instability

Outdated Infrastructure Unprecedented Temperature Fluctuations

Monopolization in Power Sector by IPPs

Dubious Tariff Structure Lack of Coherence in Subsequent Policies

Public Resistance to Implementation of Projects

Lack of Research and Development

Lightening on Transmission Mechanisms

Disruption in Energy Imports

Difficulties for Investors to invest in Electricity Market

Inappropriate Contracts Population Growth

Inaccurate Demand Forecasts

Land sliding Site Acquisition Risk Poor Macroeconomic Framework

Delays in Project Approvals and Permits

Rapid Urbanization Rate

Maintenance Issues Environmentally Harmful Emissions

Circular Debts Customer Defaults

Unskilled Work Force Natural Resources Depletion Corruption Interest Rate Fluctuation Energy Losses due to

Inefficient Mechanisms/ Theft

Waste Disposal Currency Exchange Rate Fluctuation

Inefficient Design Process International Commitments to Reduce Emissions

Inflation

Lack of Coordination between Organizations

Increase in Energy Prices due to Decarbonization Policies

Probable Changes in Taxation Framework

Weak Protection Mechanisms

Electricity Market Inefficiency

Poor Feasibility Study of Electricity Utility

Force Majeure

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Weaknesses, Opportunities, and Threats (SWOT) analysis of the renewable energy potential in the country [21]. The study considered various social, economic, and environmental factors and recommended a policy framework focused on energy security, economic gains, and ecological viability. Another study explored the feasibility of agricul- tural residue as an alternate source for the production of electrical en- ergy [70]. The results indicated that agricultural residue could serve as a cheaper option and has the potential to generate almost 1700 MW of energy. Moreover, it also identified feasible location sites for the gen- eration of energy from agricultural residue. Similarly, Tareen, et al., [65] analyzed the utility of biomass resources for energy generation and the potential for bioenergy utilization in Pakistan. The authors concluded that biomass energy is a clean, sustainable, and efficient renewable energy source and should be adopted in Pakistan’s energy framework.

Various research studies have also analyzed the current status of the national energy mix and presented alternate options. In a research study, Zameer & Wang, [79] used a linear programming model for the opti- mization of the production of electrical energy in Pakistan. The study’s objective was to minimize the cost of generation and thus, as a result, lowering of the circular debt. It also tried to reduce the costs associated with subsidies given by the government. Another research study employed the life cycle sustainability assessment for the identification of an optimized and sustainable energy mix for the country. The results prioritized hydropower as an energy source in terms of sustainability and oil was ranked as the least preferred alternative for sustainable energy production [3]. In addition, Ali, et al., [4] proposed a framework for the assessment of the technical feasibility of hybrid energy mixes in rural areas. The proposed framework was then applied in a rural area of Dera Ismaeel Khan, a city of Pakistan. The researchers recommended a combination of solar energy with a time-constrained supply from the national grid as an economically optimal option.

Researchers have also focused on other issues and prospects in Pakistan’s electric power sector, besides optimal generation mix and environmental issues. A research study used the Input-Output models for the analysis of Pakistan’s transition towards electrical energy produced from coal during the first two decades of the 21st century. The results assessed the potential of Pakistan’s electrical energy generation from coal and recommended its utilization owing to tremendous advantages [33]. Similarly, Mirza, et al., [40], estimated the impact of the devel- opment of the China Pakistan Economic Corridor (CPEC) on the gener- ation capacity and consumption of electrical energy in the country. The study predicted a significant rise in its consumption in the coming years. In addition, a research study explored the awareness level of domestic consumers regarding efficiency and conservation of electrical energy and recommended formulation of a strategy for its increase [8]. More- over, another research study proposed a framework for the development of a sustainable and competitive power market in the country [54]. Thus, the research studies have addressed Pakistan’s electric power sector from multiple perspectives. However, the literature review in- dicates a lack of research studies that were focused on risk assessment and mitigation of the country’s electric power sector.

Risk assessment refers to the process where imminent and important risks are identified, and necessary strategies are proposed for their mitigation. Multiple research studies have conducted risk assessments in the context of electrical energy, usually focusing on developed coun- tries. [75] used a hybrid fuzzy TOPSIS-AHP to develop an approach for gaguing hazards to the performance with regards to the safety of com- panies. A study assessed the exposure of Polish energy infrastructure to weather risks, analyzed the associated financial costs, and recom- mended strategies for diversification of risk management mechanisms [71]. Another study established fault tree analysis for determing the occurrence of safety hazard [76]. Similarly, Wu, et al., [72] analyzed technical risks concerning China’s investment in renewable energy projects as part of the Belt and Road Initiative (BRI). The researchers employed Analytical Network Process (ANP) technique and prioritized

various political, economic, and environmental risks. Another study performed a risk assessment of the incorporation of distributed energy storage mechanisms in the energy networks [58]. Moreover, Rossebø, et al., [56] proposed a resilience framework against the cyber threats faced by the European Union’s (EU) smart energy grids. In addition, Gasser, et al., [14] conducted a literature review of studies associated with the enhancement of resilience in energy networks. Thus, in the developed world, considerable research has been conducted with respect to risk assessment and mitigation in electric power sectors. This research study fills the research gap as it focuses on the electric power sector of Pakistan, a developing country. Therefore, the results of this study can be analyzed in the context of other developing countries as well.

Multi-Criteria Decision-Making (MCDM) tools are employed in sce- narios that involve several criteria and alternatives. MCDM techniques rank alternatives with respect to predetermined criteria. Moreover, the opinion of experts /decision-makers is considered during the evaluation of criteria and alternatives. There are multiple versions of MCDM techniques including the Analytical Hierarchy Process (AHP), Analytical Network Process (ANP), Technique for Order of Preference by Similarity to Ideal Solution (TOPSIS), Multi-Criteria Optimization and Compro- mise Solution (VIKOR), Full Consistency Method (FUCOM) and few others. Mostly, these techniques are used for the evaluation of both, criteria and alternatives. However, some techniques are better oriented towards the assessment of criteria, while others are more focused on alternatives. Therefore, in recent research endeavours, hybrid MCDM techniques are gaining popularity. In hybrid MCDM techniques, the evaluation of criteria and alternatives is carried through separate techniques.

This research study has employed a hybrid Fuzzy FUCOM-VIKOR approach for the assessment of risks. Full Consistency Method (FUCOM) is an MCDM technique, primarily used for the determination of the criterion weights. It was pioneered by [50] and it determines the optimal value of criterion weights with the aid of two constraints. The first constraint establishes that the ratio of criterion weights is equal to the ratio of their comparative priorities. The second constraint fulfills the condition of mathematical transitivity. Moreover, the final weight coefficients are assigned while minimizing their deviation from full consistency. In addition, fuzzy set theory was introduced by Zadeh, [78] and it is incorporated in MCDM problems for reduction of inherent uncertainty/vagueness in the expert opinion. A research study proposed a hybrid investigative model, comprising of agent-based modeling and MCDM analysis to reach an optimal solution for China’s electricity de- mand and supply imbalance [72]. Another study used Fuzzy DEMATEL, an MCDM analytical approach, for ranking the barriers that are obstructing the development of hydrogen refueling stations in China [73]. These refueling stations are essential for hydrogen fuel cell vehi- cles. Similarly, Chuanbo, et al., [11] employed an MCDM based research framework for reaching an optimal configuration for wind, solar and hydrogen-based energy system. FUCOM technique has been employed in several research studies. A research study employed the FUCOM technique for the prioritization of criteria that are used for the selection of sustainable suppliers [12]. Another research study assessed the sig- nificance of criteria for human resources evaluation, using the FUCOM method [62]. Similarly, Badi, [7] used the FUCOM approach for the assessment of the Libyan airlines. Fuzzy FUCOM was introduced by [49]. A research paper utilized the fuzzy FUCOM approach for the safety evaluation of road sections [41]. Similarly, Pamucar, et al., [48] employed fuzzy FUCOM for the evaluation of transport demand man- agement measures.

As stated previously, VIKOR is an MCDM technique, introduced by Opricovic, [45]. It is used for the evaluation and ranking of alternatives. The technique prioritizes the alternatives in order of the fulfillment of the compromise between the best and worst possible solutions. More- over, Fuzzy VIKOR is an improvement in the original technique and was also developed by Opricovic, [46]. Fuzzy VIKOR has found widespread

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research applications. In a research study, it was employed for the evaluation of suppliers in the context of resilience [51]. In addition, [67] using the Fuzzy VIKOR approach, evaluating the energy systems in Saudi Arabia from an investor’s perspective. Another research study utilized the Fuzzy VIKOR technique for the prioritization of risks for railways systems [13]. Similarly, Gul, et al., [16], using the Fuzzy VIKOR technique, performed risk assessment in the mining industry. Further- more, a research study used the Fuzzy VIKOR method for risk assessment in the construction of natural gas pipelines [39].

Quality Function Deployment (QFD) is another technique that was designed for the prioritization of technical strategies (HOWs) in light of customer requirements (WHATs) [2]. However, it has found applica- tions in research studies, targeting recommendations of strategies in light of predetermined factors. These factors and strategies are consid- ered as WHATs and HOWs respectively. Fuzzy QFD, an improved method, has been used with Fuzzy MCDM in various research studies. A research study proposed a performance evaluation framework based on the Fuzzy QFD-MCDM model [68]. Similarly, another study used the Fuzzy QFD-MCDM approach in a personnel selection process [47]. Furthermore, Pramanik, et al., [52] employed the Fuzzy QFD-MCDM approach for the selection of a resilient supplier. Moreover, a study used this technique for the assessment of green buildings while considering the preferences of different stakeholders [19]. In addition, Lam & Bai, [31] using the Fuzzy MCDM-QFD technique, prioritized resilience-building measures for various maritime risks.

This study has focused on risk assessment and evaluation of risk mitigation strategies for Pakistan’s electric power sector. For the assessment of risks, a hybrid Fuzzy FUCOM-VIKOR approach is employed while the Fuzzy QFD method has been used for the evaluation of mitigation strategies. The novelty of this study lies in its methodology and application. It is the first study that employs the three techniques i. e., Fuzzy FUCOM, Fuzzy VIKOR, and Fuzzy QFD for risk assessment and mitigation purposes. In addition, studies regarding risk assessment and evaluation of resilience strategies have rarely been conducted in the context of Pakistan’s electric power sector. Moreover, the method and results of this research study would be useful in the overall context of the developing world. Thus, this study has filled the research gap both, in its methodology and application.

3. Data collection and methodology

3.1. Fuzzy set theory

The concept of fuzzy set theory was first introduced by Zadeh, [78]. Since then, it has been widely employed for dealing with uncertain and imprecise circumstances. Qualitative decision making, in particular, has extensively utilized the fuzzy set theory, as it relies on qualitative judgments of decision-makers. In these cases, the expert opinion is collected in the form of linguistic variables, and thus there lies an inherent vagueness in their judgments [55]. Therefore, this vagueness is reduced by the incorporation of the fuzzy set theory, as it is based on the relative weights of attributes instead of the absolute weights.

Consider a fuzzy set z in an X universe under consideration. The fuzzy set z is defined with a membership function µz, which associates each element x in X, a real number in the interval [0,1]. This study has used triangular fuzzy numbers. A triangular fuzzy number has three distance variables i.e. the lowest possible value, the most likely value, and the highest possible value (z1, z2, and z3) in a fuzzy composition, as shown in equation 1.

μz(x) =

⎧ ⎪⎪⎪⎪⎪⎨

⎪⎪⎪⎪⎪⎩

0; x ≤ z1 x − z1 z2 − z1

; z1 < x ≤ z2

z3 − x z3 − z2

; z2 < x ≤ z3

⎫ ⎪⎪⎪⎪⎪⎬

⎪⎪⎪⎪⎪⎭

(1)

Where z1, z2, and z3 are real numbers and z1 < z2 <z3. The value of x at z2 gives the most probable value of the fuzzy set under consideration while z1 and z2 represent its lower and upper bounds respectively.

Consider two triangular fuzzy numbers; x=(x1, x2,x3) and y = (y1, y2 y3) the distance between these two fuzzy numbers can be calculated as shown in equation 2.

d(x, y) = ̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅̅ 1 3 [ (x1 − y1)

2 + (x2 − y2)

2 + (x3 − y3)

2] √

(2)

The mathematical operations between two fuzzy numbers x and y can be described with equations mentioned below.

(x1, x2, x3) ⊕ (y1, y2, y3) = (x1 + y1, x2 + y2, x3 + y3) (3)

(x1, x2, x3) ⊗ (y1, y2, y3) = (x1y1, x2 y2, x3y3) (4)

(x1, x2, x3)/(y1, y2, y3) = (x1 / y3, x2 / y2, x3 / y1)forxi > 0andyi > 0 (5)

3.2. Fuzzy MCDM – prioritization of risks

As stated previously, this research study relies on a fuzzy Multi- Criteria Decision-Making technique (MCDM) for the prioritization of risks. An MCDM technique comprises of two major elements: Criteria and Alternatives. Initially, the criteria are assigned with the importance weights relative to each other, and then the alternatives are ranked based on these criteria. Furthermore, these rankings are performed based on the qualitative judgments of the relevant experts. In this study, a hybrid MCDM technique has been employed for the assessment of risks in the context of Pakistan’s electric power sector. The fuzzy FUCOM technique has been used for assigning importance weights to the criteria while the Fuzzy VIKOR technique has been used for the assessment of alternatives. The analysis was performed with the aid of five criteria and a total of forty-five risks were ranked. These criteria and alternatives were identified through an extensive literature review. The identified criteria and alternatives were then shortlisted based on their relevancy to the study, frequency of utility in research studies, and recommen- dation of experts. Furthermore, for the reduction of redundancy, similar factors were clubbed together. The finalized criteria include Probability of Occurrence [71], Severity [71], Economic Cost [37,71], Ease of Detection [36], and Controllability of a risk [1]. The finalized alterna- tives are presented in table 1. The references for these alternatives are provided in Table A in the appendix.

As discussed earlier, the MCDM techniques rely on qualitative judgments of experts for assigning criteria and alternatives weights. Therefore, a questionnaire was designed and circulated amongst a pool of experts. The experts were initially asked to provide demographic in- formation. In the first part of the questionnaire, they were asked to rate the relative importance of each of the five criteria, on a five-point Likert scale. Subsequently, they were asked to rate the importance of each risk with respect to each criterion, on a five-point Likert scale. Thus, experts rated the probability, severity, detectability, and controllability of each risk. The linguistic scale used for the rating is described in table 2. The sample size of the experts’ pool was 31 and it included electrical engi- neers, economists, and managers associated with the generation, transmission, distribution, and consumption of electrical energy. It also included experts from academia working on relevant research projects

Table 2 Fuzzy scale for linguistic variables [30].

Linguistic Variable Fuzzy Membership Function

Very Low (1,1,3) Low (1,3,5) Moderate (3,5,7) High (5,7,9) Very High (7,9,9)

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(table 3).

3.3. Fuzzy full consistency method (FUCOM)

The Full Consistency (FUCOM) Method is an MCDM technique, and it was introduced by Pamučar, et al., [50]. It is used for the determination of relative importance weights of criteria. For n number of criteria, the technique employs “n-1′′ pairwise comparison matrices, and using the two conditions, explained previously, it assigns final weights to the criteria. The first aim of this study is the evaluation of the criteria, which are later employed for the prioritization of risks. Moreover, due to intrinsic vagueness and uncertainty in the qualitative judgments, this study used the Fuzzy FUCOM method, its steps are explained below.

3.3.1. Step 1: assignment of criteria importance ratings by experts In the first step, experts are asked to assign significance ratings as per

their expertise, to each criterion. Experts assign ratings on a linguistic scale, which are then translated to fuzzy numbers. After, the assignment of weights, the fuzzy numbers are de-fuzzified using geometric mean, and the criteria are sorted in descending order of importance rating. The de-fuzzification is only for ranking, the rest of the analysis is conducted with fuzzy numbers. The representation is given in equation 6.

Rj(1) > Rj(2) > ... > Rj(q) (6)

Where R represents the criteria for evaluation and q represents the rank of the criteria when sorted in descending order.

3.3.2. Step 2: determination of comparative priorities In the next step, the comparative priorities of the criteria are calcu-

lated. It refers to the advantage of a criterion over the next ranked cri- terion. Eq. (7) and 8 shows the calculation of the comparative priority.

αl/l+1 = IRq /

IRq+1 (7)

φ = αl/2, α2/3, …, αq/q+1 (8)

where (αj = αjL, αjM, αjU) Where IR is the importance rating assigned by experts, and α refers to

the comparative priority.

3.3.3. Step 3: construction of a non-linear programming model A non-linear programming model is constructed, which is based on

two conditions. The ratio of the final weight coefficients of the criteria is equal to the

comparative priority of the respective criteria, depicted in equation 9.

wq /

wq+1 = αq/q+1 (9)

Where wq refer to the final value of the weight coefficients. The final weight coefficients of criteria fulfill the condition of

mathematical transitivity, depicted in equation 10.

wq /

wq+2 = αq/q+1 ∗ αq+2/q+3 (10)

The second condition, the fulfillment of mathematical transitivity ensures the principle of satisfaction of minimum deviation from full consistency.

3.3.4. Step 4: formulation and solution of a non-linear programming model In the final step, a non-linear programming model is formulated

based on the two previously mentioned conditions, and the final value of weight coefficients is determined. The format of a standard model is presented in equation 11.

minx (11)

s.t. ⃒ ⃒wq − wq+1 ∗ αq/q+1

⃒ ⃒ ≤ x, ∀j

⃒ ⃒wq − wq+2 ∗ αq/q+1 ∗ αq+2/q+3

⃒ ⃒

≤ x, ∀j ∑n

j=1 wq = 1, ∀jwqL ≤ wqM ≤ wqU, wq ≥ 0

∀jq = 1, 2, 3, …, n

Where (wj = wjL, wjM, wjU) and (αq/q+1 = αq/q+1 L, αq/q+1 M, αq/q+1 U) This model is solved using Lingo software and the values for the

weight coefficients are obtained.

3.4. Fuzzy VIKOR

VIKOR is a Serbian term for Multi-Criteria Optimization and Compromise Solution. It is an MCDM technique, and it ranks the alter- natives in the order of best practical compromise solution between best and worst possible solutions. In this research study, Fuzzy VIKOR was applied for the prioritization of risks in the context of Pakistan’s electric power sector. The criteria for the assessment of these ranks were eval- uated through Fuzzy FUCOM, as explained in the previous section and those criteria weights were incorporated in the fuzzy VIKOR analysis.

3.4.1. Step 1: construction of a fuzzy decision matrix In the first step of the fuzzy VIKOR approach, experts are asked to

gage the importance of each alternative with respect to each criterion. This assessment is conducted on a linguistic scale. As in this study, triangular fuzzy numbers are used, therefore the expert opinion was translated into fuzzy values with the aid of table 2. These responses are then used to construct a fuzzy decision matrix, as shown in equation 12.

FDM =

⎡

⎣ x11 x12 ⋯ x1v

⋮ ⋱ ⋮ xu1 xu2 ⋯ xuv

⎤

⎦ (12)

For i = 1, 2, 3, …, u and j = 1,2,3,…,vwhere FDM refers to the Fuzzy Decision Matrix, xij is the rating assigned to Alternative Ai with respect to Criterion Cj by experts.

3.4.2. Step 2: determination of fuzzy best value and fuzzy worst values Eqs. (13) and 14 are applied for the determination of the fuzzy best

Table 3 Experts’ details.

S.No Education Professional Background Years of Experience

1 Masters Electrical Engineering (Generation) 5–10 Years 2 Bachelors Electrical Engineering (Generation) 0–5 Years 3 Masters Electrical Engineering (Generation) 0–5 Years 4 Masters Electrical Engineering (Transmission) 5–10 Years 5 Masters Electrical Engineering (Transmission) 10–15 Years 6 Bachelors Electrical Engineering (Transmission) 10–15 Years 7 Masters Electrical Engineering (Distribution) 5–10 Years 8 Masters Electrical Engineering (Distribution) 0–5 Years 9 Masters Electrical Engineering (Distribution) 5–10 Years 10 Masters Electrical Engineering (Distribution) 0–5 Years 11 Bachelors Electrical Engineering (Industry) 5–10 Years 12 Bachelors Electrical Engineering (Industry) 5–10 Years 13 Masters Electrical Engineering (Industry) 5–10 Years 14 Bachelors Electrical Engineering (Industry) 5–10 Years 15 PhD Electrical Engineering (Academia) 5–10 Years 16 PhD Electrical Engineering (Academia) 5–10 Years 17 PhD Electrical Engineering (Academia) 10–15 Years 18 PhD Electrical Engineering (Academia) 10–15 Years 19 PhD Electrical Engineering (Academia) 5–10 Years 20 PhD Electrical Engineering (Academia) 10–15 Years 21 PhD Electrical Engineering (Academia) 0–5 Years 22 PhD Electrical Engineering (Academia) 0–5 Years 23 PhD Energy Economists (Academia) 5–10 Years 24 PhD Energy Economists (Academia) 5–10 Years 25 PhD Energy Economists (Academia) 0–5 Years 26 PhD Energy Economists (Academia) 10–15 Years 27 Masters Managers (Industry) 10–15 Years 28 Masters Managers (Industry) 10–15 Years 29 Masters Managers (Industry) 10–15 Years 30 Masters Managers (Industry) 5–10 Years 31 Bachelors Managers (Industry) 0–5 Years

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and fuzzy worst values for each criterion respectively.

f∗j = maxixij (13)

f−j = minixij (14)

where (f∗j = f ∗ jL, f

∗ jM,f

∗ jU), (f

− j = f

− jL, f

− jM,f

− jU)and f

∗ j and f

− j refer to the fuzzy

best and worst values respectively.

3.4.3. Step 3: determination of the Si and Ri values Eqs. (15) and 16 are used for the calculation of Si and Ri values.

Si = ∑v

j=1 wj ∗

( f ∗j − Xij

) / ( f ∗j − f

− j

) (15)

Ri = max j

[ wj ∗ f ∗j − Xij

/ ( f ∗j − f

− j

)] (16)

where (Si = SiL, SiM,SiU), (Ri = RiL, RiM, RiU)and wj refer to the weight of coefficients calculated from the fuzzy FUCOM analysis.

3.4.4. Step 4: determination of the Qi values Eq. (17) is employed for the calculation of the values of Q for all

alternatives.

Qi = v(Si − S ∗ )/(S− − S∗) + (1 − v)(Ri − R∗)/(R− − R∗) (17)

Where (Qi = QL, QM, QU) and

S∗ = mini Sij, S− = maxi Sij

R∗ = min(i)Rij, R− = max(i)Ri j

S* refers to the maximum group utility and R∗ refers to the minimum individual regret of the opponent, and both these elements aid in the determination of the Qi index. Moreover, v refers to the weight of the strategy of maximum group utility. The decision tends towards maximum group utility, if v > 0.5 and it tends towards minimum indi- vidual regret if v = 0.5.

3.4.5. Step 5: de-fuzzification of Q values and ranking The triangular fuzzy values for Q are de-fuzzified and the alternatives

are ranked in the ascending order of these values. In the study, the geometric means formula is used for de-fuzzification purposes.

Q(De − Fuzzified) = (QL ∗ QM ∗ QU) 1/3 (18)

3.5. Fuzzy QFD - Prioritization of resilience strategies

Quality function deployment (QFD) or house of quality (HOQ) is a tool widely used to translate customer requirements into final product designs. In a house of quality, there are two main factors: WHATs and HOWs. WHATs are the customer requirements and HOWs are the stra- tegies to ensure fulfillment of these requirements.

The HOWs are prioritized based on their relationship with WHATs and the correlation between HOWs. Thus, HOWs are the means that can be adopted to achieve the ends, the WHATs. In this study, QFD was used to develop a mitigation framework for the risks prioritized through the Fuzzy MCDM analysis. WHATs are the risks that have been identified with the help of fuzzy MCDM analysis. HOWs are the mitigation stra- tegies, that can be employed in Pakistan’s electric power sector.

These strategies were identified from the literature. However, these strategies were then shortlisted based on their relevancy to the study, frequency of utility in previous research studies, and expert opinion. Thus, a total of 15 such strategies were finalized, as presented in table 4. (Table B in the appendix mentions these strategies with the literature sources) (Table C).

Similar to MCDM, QFD is also reliant on the expertise of decision-

makers, who are asked to rate the degree of relationship and correla- tion, as stated previously. Therefore, another questionnaire was designed and circulated amongst a pool of 7 experts. The questionnaire had two major parts. Initially, the experts were asked to provide de- mographic information. The experts were then asked to rate the effec- tiveness of each risk mitigation strategy against every given risk, according to table 6. In the second part, the experts were asked to rate the nature of the relationship between given risk mitigation strategies, according to table 7. The pool included engineers, managers, and re- searchers working in the electric power sector of Pakistan (table 5). The Fuzzy QFD analysis comprises the following steps.

3.5.1. Step 1: identification of “WHATs” and “HOWs” In the first step of Fuzzy QFD, the “WHATS” and “HOWs” are iden-

tified. In this study, WHATs are “risks that can occur in Pakistan’s electric power sector, ranked through MCDM analysis” and HOWs are the “mitigation strategies for those risks”.

3.5.2. Step 2: weights of WHATs The weights of WHATs refer to their relative importance. In this

study, these weights are derived from the Fuzzy VIKOR analysis.

3.5.3. Step 3: fuzzy relation and fuzzy correlation matrices In the house of quality, the fuzzy relationship and fuzzy correlation

matrices are filled according to the expert opinion. The fuzzy relation- ship matrix depicts the impact of each HOW with respect to each WHAT, while the fuzzy correlation matrix represents the degree of correlation amongst HOWs. As stated previously, the expert opinion, in the form of linguistic variables, was collected for the assignment of these weights. These linguistic variables were translated into fuzzy values according to table 6 and 7.

Table 4 Mitigation strategies identified from literature review.

S. No

Mitigation Strategies

1 Development of a Suitable Environment for Investment in the Energy Sector 2 Unbundling/ Decentralization of Authority in Electric power sectors 3 Geographic Diversification for Minimization of Weather Risks 4 Diversification of Generation Mix 5 Policy Formulation for Improvement of Communication and Data Sharing

between Organizations 6 Upgradation of Protection Systems and Transmission Lines 7 Increase in Redundancy in Operations in Electric power sector 8 Incorporation of IoT/Automation/ Digital Technology in Electric power

sector 9 Focus on Accurate Demand Forecasts 10 Development and Implementation of a Demand-Side Management Program

to Reduce Peak Electricity Demand 11 Development of an Enforcement Mechanism that allows the Recovery of

Government Funds from Failed Power System Projects that do not Adhere to Applicable Codes and Standards

12 Improvement of Tariff Structure and Revenue Mechanism 13 Maximizing the Energy Efficiency Potential to Maintain Demand-Supply

Balance 14 Maximizing the Research and Development in the Energy Sector 15 Incorporation of Islandable Energy Systems for Critical Load - Microgrids

Table 5 Experts’ details.

S.No Education Professional Background Years of Experience

1 Masters Electrical Engineering (Generation) 5–10 Years 2 Masters Electrical Engineering (Transmission) 10–15 Years 3 Masters Electrical Engineering (Distribution) 5–10 Years 4 PhD Electrical Engineering (Academia) 5–10 Years 5 PhD Electrical Engineering (Academia) 5–10 Years 6 PhD Electrical Engineering (Academia) 10–15 Years 7 PhD Energy Economists (Academia) 5–10 Years

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3.5.4. Step 4: calculation of relative importance In the Fuzzy QFD method, the relative importance of each WHAT is

calculated by the multiplication of its importance weight with respective values in the fuzzy relationship matrix, as depicted in equation 19.

RIj = ∑n

i=1 Wi ∗ Rij (19)

where j = 1,2,..,m and (Rj = RjU, RjM, RjU) In this research study, the relative importance weights were derived

from previous analysis of Fuzzy VIKOR. As fuzzy VIKOR ranks the al- ternatives in ascending order of Q values, therefore these values were subtracted from a common value that is 1 and incorporated as relative weights in the house of quality. In addition, Rij represents the respective weights in the relationship matrix.

3.5.5. Step 5: calculation of priority weights The priority weights are calculated with equation 20.

RI∗j = RIj + ∑

k=j Tkj ∗ RIk (20)

where j = 1,2,.., m and (RI∗j = RI ∗ jL, RI

∗ jM, RI

∗ jU)

3.5.6. Step 7: normalization of priority weights The priority weights, calculated in the previous step are normalized,

by dividing each priority weight by the maximum value amongst the priority weights, as shown in equation 21;

RI∗∗j = RI∗j

RI∗maxj (21)

where (RI∗∗j = RI ∗∗ jL , RI

∗∗ JM, RI

∗∗ jU)

RIj∗∗ represent the normalized priority weights and RIj∗max refers to the highest value amongst the priority weights.

3.5.7. Step 8: de-fuzzification and ranking of alternatives Eq. (22) is used for de-fuzzification of the normalized priority

weights and the HOWs are ranked in the descending order of these de- fuzzified values.

DF − RI∗∗j = a + 4b + c

6 (22)

4. Results and discussions

The results and discussion section is divided into two parts. The first part presents the results derived from the Fuzzy MCDM analysis which was aimed at prioritization of risks in the context of Pakistan’s electric power sector. The second part presents the results of the Fuzzy QFD

analysis which was aimed at the ranking of mitigation strategies for the previously identified risks in the context of Pakistan’s electric power sector. Subsequently, the major implications of both these analyses are discussed.

4.1. Fuzzy MCDM – prioritisation of risks

Initially, the Fuzzy FUCOM method was applied for the evaluation of the criteria weights. As stated previously, this research study employed five criteria for the assessment of risks. The criteria included Probability of Occurrence, Severity, Economic Cost, Detection, and Controllability of a risk. All these criteria are assessed as the benefit criteria. Thus, the most important risk would be the one with a higher probability of occurrence, severity, and economic cost. Moreover, it would also be most difficult to detect and control. The experts were asked to rate the importance of these criteria in the context of a developing country and then through the Fuzzy FUCOM technique, the final weight coefficients were calculated. The results of this part are summarized in Fig. 1.

The Economic Cost was selected as the most important criteria in the risk assessment process. It was followed by Detection, Controllability, Severity, and Probability of Occurrence. These weights were then incorporated in the Fuzzy VIKOR analysis and were used for the assessment of risks.

In the next step of the analysis, the experts were asked to rate the importance of alternatives with respect to each criterion. In this case, the alternatives are the risks in the context of Pakistan’s electric power sector. These responses were utilized in the construction of the fuzzy decision matrix. The best and worst possible solutions were determined from the fuzzy decision matrix and the values for S, R, and Q were computed. The risks were then ranked in the ascending order of the Q values, and the results are presented in table 8.

From these results, the top 20 risks were selected for further assessment. These are the risks that are most likely to occur, severe, costly, and difficult to detect and control. Therefore, these risks were considered in the Fuzzy QFD analysis, where the framework for risk mitigation for Pakistan’s electric power sector is proposed.

4.2. Fuzzy QFD – risk mitigation framework

In the second part of the analysis, Fuzzy QFD is employed for the proposition of a risk mitigation framework for Pakistan’s electric power sector. For this purpose, experts were asked to rate the effectiveness of the shortlisted mitigation strategies with respect to previously priori- tized risks. Moreover, experts were also asked to rate the degree of correlation of these strategies with respect to each other. For the house of quality, the relative importance weights were derived from the Fuzzy VIKOR analysis. The fuzzy relationship matrix and fuzzy correlation matrix were constructed and the relative priority weights for the

Table 6 Fuzzy scale for linguistic variables (QFD correlation) [9].

Degree of Relation Fuzzy Number

Strong 0.7; 1;1 Medium 0.3; 0.5; 0.7 Weak 0; 0; 0.3

Table 7 Fuzzy scale for linguistic variables (QFD relationship) [9].

Degree of Correlation Fuzzy Number

Strong Positive 0.7; 1; 1 Positive 0.5; 0.7; 1 Negative 0; 0.3; 0.5 Strong Negative 0; 0; 0.3

Fig. 1. Results of Fuzzy FUCOM analysis.

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strategies were calculated. The HOWs (strategies) were ranked in the descending order of the normalized priority weights. The results of this part are summarized in table 9.

The Fuzzy QFD analysis resulted in the ranking of Incorporation of IoT, Development of a Suitable Environment for Investment in the En- ergy Sector, Policy Formulation for Improvement of Communication and Data Sharing between Organizations, Maximizing the Energy Efficiency Potential to Maintain Demand-Supply Balance, Maximizing the Research and Development in the Energy Sector as the five most important mitigation strategies for Pakistan’s electric power sector.

The risk assessment analysis ranked Corruption as the most impor- tant risk for Pakistan’s electric power sector. This is evident from the fact that Transparency International ranked Pakistan at 120th place in comparison with other countries [69]. The rampant corruption in the

electric power sector not only leads to huge financial losses, but it is also a hurdle in the way of revival and up-gradation of the energy infra- structure. The widespread corruption has also been diminishing the confidence of investors. Similarly, Circular Debts are the second most important risk for Pakistan’s energy sector. Currently, the circular debts in the power sector are rising at a rate of 1.5 billion rupees per day and approximately amount to a net value of 2.15 trillion [27]. The discrepancy in demand-supply balance, high cost of generation, tech- nical and political issues, and the inconsistencies in the governmental policy account for the high level of circular debt [66]. These circular debts are not only a huge burden on the national exchequer, but they have also discouraged the interest of foreign and local investors in the revival of the electricity sector.

Moreover, the MCDM analysis also prioritized Outdated Infrastruc- ture as another important risk. The electrical energy infrastructure of Pakistan has been exhausted and the rapidly rising demand and less focus on up-gradation have rendered it ineffective for maintaining a stable supply of energy [28]. The malfunctioning of this infrastructure has resulted in the fatalities of citizens due to electric shocks and made it very difficult to trace and correct the frequently occurring faults [5]. The issue of outdated infrastructure is also associated with the issue of En- ergy Losses, which has been ranked in fourth place. The energy losses in Pakistan primarily occur due to two reasons, weak infrastructure, and thefts. Ineffective load management, inadequate choice of transmission lines, inadequate compensation of reactive power, and other similar factors contribute to energy losses due to infrastructure issues [25]. Similarly, theft by the consumers is also exacerbating the energy loss issue as they tamper with the billing meters, overburden distribution lines, and manipulate the billing mechanisms in other technical manners [18]. In the fiscal year of 2019–2020, the overall transmission and dis- tribution losses amounted to 16.5% of the total electrical energy output, causing a loss of Rs 280 billion to the national exchequer [27].

Lack of Research and Development is another risk factor that is plaguing the national energy sector currently and is a major threat for the coming decades. Most of the technical, managerial, and economic problems associated with the electric power sector are native to the country and thus require dedicated and quality research efforts for arrival at plausible solutions [53]. The next ranked risk is the Monop- olization in Power Sector by the Independent Power Producers.

Table 8 Results of Fuzzy VIKOR analysis.

Rank Risk Q Value

Rank Risk Q Value

1 Corruption 0.018 24 Increase in Energy Prices due to Decarbonization Policies

0.439

2 Circular Debts 0.088 25 Lack of Coherence in Subsequent Policies

0.490

3 Outdated Infrastructure

0.091 26 Unskilled Work Force 0.491

4 Energy Losses due to Inefficient Mechanisms/Theft

0.143 27 Inappropriate Contracts

0.514

5 Lack of Research and Development

0.189 28 Lack of Coordination between Organizations

0.515

6 Monopolization in Power Sector by IPPs

0.195 29 Inappropriate Generation Mix

0.550

7 Inflation 0.233 30 Waste Disposal 0.559 8 Electricity Market

Inefficiency 0.236 31 Depreciation of

Equipment 0.586

9 Inefficient Design Process

0.262 32 Disruption in Energy Imports

0.615

10 Inaccurate Demand Forecasts

0.272 33 Interest Rate Fluctuation

0.617

11 Delays in Project Approvals and Permits

0.325 34 Customer Defaults 0.617

12 Rapid Urbanization Rate

0.332 35 Site Acquisition Risk 0.629

13 Currency Exchange Rate Fluctuation

0.333 36 Natural Resources Depletion

0.641

14 Dubious Tariff Structure

0.339 37 Droughts/ Excessive Rainfalls

0.741

15 Poor Macroeconomic Framework

0.344 38 Force Majeure 0.749

16 Inadequate Investment in Power Sector

0.355 39 International Commitments to Reduce Emissions

0.751

17 Population Growth 0.368 40 Public Resistance to Implementation of Projects

0.764

18 Difficulties for Investors to invest in Electricity Market

0.373 41 Social Instability 0.783

19 Weak Protection Mechanisms

0.381 42 Environmentally Harmful Emissions

0.820

20 Poor Feasibility Study of Electricity Utility

0.401 43 Lightening on Transmission Mechanisms

0.924

21 Inadequate Government Policy Formulation

0.410 44 Unprecedented Temperature Fluctuations

0.926

22 Maintenance Issues 0.412 45 Land sliding 0.984 23 Probable Changes in

Taxation Framework 0.432

Table 9 Results of Fuzzy QFD analysis.

Rank Risk Mitigation Strategy RI∗∗

Value

1 Incorporation of IoT/Automation/ Digital Technology in Electric power sector

0.601

2 Development of a Suitable Environment for Investment in the Energy Sector

0.584

3 Policy Formulation for Improvement of Communication and Data Sharing between Organizations

0.581

4 Maximizing the Energy Efficiency Potential to Maintain Demand-Supply Balance

0.537

5 Maximizing the Research and Development in the Energy Sector

0.536

6 Diversification of Generation Mix 0.523 7 Unbundling/ Decentralization of Authority in Electric power

sectors 0.506

8 Focus on Accurate Demand Forecasts 0.503 9 Development and Implementation of a Demand-Side

Management Program to Reduce Peak Electricity Demand 0.488

10 Increase in Redundancy in Operations in Electric power sector 0.463 11 Geographic Diversification for Minimization of Weather Risks 0.462 12 Upgradation of Protection Systems and Transmission Lines 0.450 13 Improvement of Tariff Structure and Revenue Mechanism 0.414 14 Incorporation of Islandable Energy Systems for Critical Load -

Microgrids 0.404

15 Development of an Enforcement Mechanism that allows the Recovery of Government Funds from Failed Power System Projects that do not Adhere to Applicable Codes and Standards

0.304

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Currently, one of the biggest problems in the national power sector is the monopoly of the national institutions in the energy sector, however, there is a risk that despite the proposed privatization measures, the issue will not be solved [26]. Mostly commercial sector in Pakistan is strongly monopolized and these monopolies dictate their terms in policy frame- works. This rationale when viewed in addition to the inadequate financial deals made by the Independent Power Producers (IPPs), can be viewed as a significant risk for the energy sector in the coming times [35]. In conclusion, the highlighted risks pose a significant threat to the electric power sector and therefore, an effective policy framework must be designed for risk mitigation.

The second part of this study analyzed the mitigation strategies for the top twenty risks that were prioritized by the risk assessment analysis. The incorporation of IoT was ranked as the most important strategy for the mitigation of risks. The incorporation of IoT and automation would significantly enhance the overall energy sector as it would improve the protection mechanisms, maintain a steady demand-supply balance, identify areas of low financial recovery, aid in accurate forecasts, reduce the bureaucratic hurdles, and improve coordination between organiza- tions [20,22,42]. Moreover, it would highlight maintenance issues, aid in proper scheduling of these activities, and assist in tackling weather risks. Thus, besides playing a precautionary role with a respect to probable risks, the incorporation of IoT and digital technologies would play a supporting role with other risk mitigation strategies.

Moreover, for the minimization of financial risks and improvement of economic security, the electric power sector must be made investor friendly. The presence of investor dynamic would lead to greater financial accountability and up-gradation of the infrastructure [10]. Moreover, it will also lead to the exploration of the neglected avenues in terms of generation and transmission [24]. In addition, the energy sector, when being financed by multiple sources, would become more resilient to economic uncertainties. It would also aid in the reduction of the per-unit price of energy for consumers.

Table A Risks.

S. NO.

Type Risk Source

1 Technical Risks Inappropriate Generation Mix [29] 2 Outdated Infrastructure [72] 3 Lack of Research and Development [72] 4 Inaccurate Demand Forecasts [1] 5 Maintenance Issues [1] 6 Unskilled Work Force [64] 7 Energy Losses due to Inefficient

Mechanisms/Theft [64]

8 Inefficient Design Process [64] 9 Lack of Coordination between

Organizations [34]

10 Weak Protection Mechanisms [36] 11 Poor Feasibility Study of Electricity

Utility [44]

12 Environmental Risks Droughts/ Excessive Rainfalls [64] 13 Unprecedented Temperature

Fluctuations [71]

14 Lightening on Transmission Mechanisms

[64]

15 Land sliding [64] 16 Environmentally Harmful Emissions [1] 17 Natural Resources Depletion [77] 18 Waste Disposal [29] 19 International Commitments to Reduce

Emissions [37]

20 Increase in Energy Prices due to Decarbonization Policies

[29]

21 Economic Risks Inadequate Investment in Power Sector [43] 22 Monopolization in Power Sector by

IPPs [43]

23 Disruption in Energy Imports [43] 24 Site Acquisition Risk [44] 25 Circular Debts [72] 26 Corruption [72] 27 Currency Exchange Rate Fluctuation [72] 28 Inflation [1] 29 Probable Changes in Taxation

Framework [1]

30 Electricity Market Inefficiency [72] 31 Force Majeure [72] 32 Depreciation of Equipment [57] 33 Dubious Tariff Structure [37] 34 Difficulties for Investors to invest in

Electricity Market [77]

35 Poor Macroeconomic Framework [64] 36 Customer Defaults [38] 37 Interest Rate Fluctuation [38] 38 Policy/

Administrative Risks Inadequate Government Policy Formulation

[72]

39 Lack of Coherence in Subsequent Policies

[74]

40 Inappropriate Contracts [15] 41 Delays in Project Approvals and

Permits [1]

42 Socio-Political Risks Social Instability [44] 43 Public Resistance to Implementation of

Projects [72, 74]

44 Population Growth [77] 45 Rapid Urbanization Rate [77]

Table B Risk mitigation strategies.

S. NO.

Risk Mitigation Strategy Source

1 Development of a Suitable Environment for Investment in the Energy Sector

[43,61]

2 Unbundling/ Decentralization of Authority in Electric power sectors

[43]

3 Geographic Diversification for Minimization of Weather Risks [71] 4 Diversification of Generation Mix [29,63,

64] 5 Policy Formulation for Improvement of Communication and

Data Sharing between Organizations [17,64]

6 Upgradation of Protection Systems and Transmission Lines [64] 7 Increase in Redundancy in Operations in Electric power sector [64] 8 Incorporation of IoT/Automation/ Digital Technology in

Electric power sector [32,64]

9 Focus on Accurate Demand Forecasts [64] 10 Development and Implementation of a Demand-Side

Management Program to Reduce Peak Electricity Demand [64]

11 Development of an Enforcement Mechanism that allows the Recovery of Government Funds from Failed Power System Projects that do not Adhere to Applicable Codes and Standards

[64]

12 Improvement of Tariff Structure and Revenue Mechanism [64] 13 Maximizing the Energy Efficiency Potential to Maintain

Demand-Supply Balance [61]

14 Maximizing the Research and Development in the Energy Sector

[61]

15 Incorporation of Islandable Energy Systems for Critical Load - Microgrids

[63]

Table C Nomenclature.

Abbreviation Explanation

MCDM Multi-Criteria Decision-Making FUCOM Full Consistency Method VIKOR Multi-Criteria Optimization and Compromise Solution QFD Quality Function Deployment AHP Analytical Hierarchy Process ANP Analytical Network Process TOPSIS Technique for Order of Preference MW Mega Watts (Unit of Electric Power) CPEC China Pakistan Economic Corridor WAPDA Water and Power Development Authority NTDC National Transmission and Distribution Company DISCOs Distribution Companies NEPRA National Electric Power Regulatory Authority

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Furthermore, Pakistan’s electric power sector is currently operated by several independent organizations. For example, Water and Power Development Authority (WAPDA) is responsible for power generation, the National Transmission and Dispatch Company (NTDC) is tasked with Transmission and Distribution, and the Distribution Companies (DIS- COs) distribute the electrical power to consumers. Similarly, the Na- tional Electric Power Regulatory Authority (NEPRA) decides the tariff structure. Therefore, these institutions must develop an effective coor- dination process as their activities are interrelated. Minimization of bureaucratic hurdles, real-time data sharing, and a cohesive policy framework would prepare the energy sector for probable threats [23].

Moreover, the results from the Fuzzy QFD analysis also focused on maximizing the energy efficiency potential. This can be achieved through improved transmission mechanisms, robust protection mecha- nisms, and accurate demand forecasts. One of the important strategies for maximizing efficiency is to ensure an appropriate load factor during the planning and development phases [60]. A reasonable load factor minimizes the gap between the average load and maximum demand, and thus the installed capacity is utilized effectively.

In addition, efforts should be made for increasing research and development activities in the energy sector. Similarly, Diversification of Generation Mix is another important strategy that would lessen the weather risks, lower the costs of generation, reduce environmentally harmful emissions, and increase energy efficiency [3]. Thus, in conclu- sion, the strategies recommended by the Fuzzy QFD analysis have adequate potential to build a protective framework against risks and add to the resilience of Pakistan’s electric power sector.

5. Conclusion and policy implications

This research study proposed a framework for the assessment of risk mitigation strategies for the electric power sector of developing coun- tries. As a case in point, this study focused on Pakistan’s electric power sector. Initially, the criteria for the risk assessment were evaluated through the Fuzzy FUCOM technique. Subsequently, the criteria were used to evaluate and prioritize risks through the Fuzzy VIKOR analysis. These prioritized risks were then employed for the identification of risk mitigation measures through the Fuzzy QFD analysis. This study also presented a novel combination of techniques for the said purpose and filled the research gap in its application.

The criteria for risk assessment included the probability of occur- rence, severity, economic cost, detectability, and controllability. The Fuzzy MCDM analysis prioritized corruption, circular debts, energy losses, lack of research, and development among other risks. The Fuzzy QFD analysis prioritised risk mitigation strategies in accordance with the ranked risks. The adoption of following strategies in the national policy framework was recommended.

• Incorporation of IoT/Automation/ Digital Technology should be prioritized in the national electric power sector as it would lead to improvement of the protection mechanisms, help maintain a steady demand-supply balance, identify areas of low financial recovery, aid in accurate forecasts, reduce the bureaucratic hurdles, and improve coordination between organizations.

• A suitable environment for investment in the electric power sector should be developed as it would lead to the up gradation of the existing infrastructure, financial accountability, and resilience to- wards economic uncertainties.

• Policy formulation for the improvement of communication and data sharing between organizations is another important risk mitigation strategy. In Pakistan, different organizations are responsible for various aspects of electrical energy and their coordination must be improved to ensure efficient working of the overall electric power sector.

• The energy efficiency potential to maintain demand-supply balance should be maximized through improved transmission mechanisms,

robust protection mechanisms, accurate demand forecasts, and appropriate load factors.

• The generation mix should be further diversified in terms of geographic location, primary investors, and the source of production of electrical energy. It would lessen the weather risks, lower the costs of generation, reduce environmentally harmful emissions, and in- crease energy efficiency.

The study has few limitations. This study only relied on the quali- tative judgments of experts for analysis. This research area can be enriched by conducting studies with quantitative data and past experi- ences in future research studies. The risks can be measured by their practical cost and similarly risk mitigation measures can be evaluated based on their practical utility in the developed world. Moreover, future studies can adopt an enhanced and diverisifed pool of experts, covering extended geographical regions and technical domain.

Declaration of Competing Interest

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

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