assaying compromise solution from the perspective … · mr. saikat samanta independent management...

Journal of Advance Management Research, ISSN: 2393-9664

Vol.05 Issue-05, (December 2017), Impact Factor: 4.598

Double-Blind Peer Reviewed Refereed International Journal - Included in the International Serial Directories

Journal of Advance Management Research, ISSN: 2393-9664 (JAMR) http://www.jamrpublication.com email id- [email protected] Page 212

ASSAYING COMPROMISE SOLUTION FROM THE PERSPECTIVE OF A

RENEWABLE ENERGY GENERATION POTENTIAL IN INDIA

Dr. Ayan Chattopadhyay

Associate Professor, Army Institute of Management Kolkata

Mr. Saikat Samanta

Independent Management Researcher

ABSTRACT

The global phenomenon of industrialization, urbanization and economic growth has been fuelled by a host

of factors of which energy and more precisely, electricity or power is the key component. In India,

electricity is majorly generated from conventional sources like fossil fuels which not only have limited

supply but also propagate pollution hazards. Thus, efforts are towards usage of renewable energy options

to meet multiple agendas of growing energy needs and pollution reduction with an aim to create sustainable

environment in the energy sector. The central and state government’s relentless effort and focus on

diversified renewable sources like solar, wind, hydro and biomass projects has created an environment that

allows private participation in energy generation too. Such a scenario intrigues the need for knowledge of

renewable energy generation potential of different states in India and the researchers have selected solar

power as their area of study. Ranking of alternatives, here Indian states, against a set of criteria creates a

perfect multi-criteria decision making environment. The researchers have used six criterions, and

compromise solution evaluated using VIKOR, a method which not only considers maximum group utility

but also considers minimum individual regret. Weights of six criterions have been used to calculate the

utility and regret measures and the same is derived from entropic consideration, Shannon’s entropy weight.

KEY WORDS

Renewable energy, Solar Power Potential, VIKOR, Shannon’s Weight, Compromise solution




Journal of Advance Management Research, ISSN: 2393-9664 (JAMR) http://www.jamrpublication.com email id- [email protected] 13

INTRODUCTION

Energy consumption is many a times considered proportional to a country’s overall development. India is

the fourth largest energy consumer in the world after the United States, China, and Russia. Though Indian

economy has been growing at an annual rate of approximately 7 percent since 2000, some 500 million

Indians (40%) use traditional fuels – fuel wood, agricultural waste and biomass cakes – for cooking and

general heating needs (NITI Aayog, 2017). These traditional fuels are burnt in cook stoves, known as chulah

or chulha in some parts of India. Traditional fuel is inefficient source of energy, its burning releases high

levels of smoke, carbon monoxide and other air pollutants (ICMR 2001). Some reports, including one by

the World Health Organisation, claim 300,000 to 400,000 people in India die of indoor air pollution and

carbon monoxide poisoning every year because of biomass burning and use of chullahs. Up to 31 December

2016, only 133,177,143 rural households are provided with electricity which is 74% of total 179,231,219

rural households (WHO & UNEP 2017). Most of the power generated in India is from non-renewable

energy sources i.e., coal and mineral oil-based power generating stations, all of which emit greenhouse

gases (CO2, CFC, NO, SO2 etc) profoundly in the environment. The dependency only on conventional

resources to fulfill all energy requirements of our country has not only environmental impacts but also the

fossil fuel reserves are depleting fast; eventually they will dwindle. The question on attaining sustainability

through usage of conventional energy resources is big today (Venkatakrishnan & Rengaraj, 2014). The

Installed capacity by source in India as on 30 September 2017 shows that conventional or non-renewable

source of energy generation contributes maximum to India’s total energy generation.

Fig 1; Growth of electricity sector in India from 1947-2017; Source - CEA, 2017

In contrast to the conventional energy resources, the alternative renewable energy resources that one may

think of includes wind and solar energy which are constantly replenished and will never run out. Most

renewable energy comes either directly or indirectly from the sun. Sunlight, or solar energy, can be used

directly for heating and lighting homes and other buildings, for generating electricity, and for hot water

heating, solar cooling, and a variety of commercial and industrial uses. The sun's heat also drives the winds,

whose energy, is captured with wind turbines. The sun's heat causes water to evaporate. When this water

vapor turns into rain or snow and flows downhill into rivers or streams, its energy can be captured using

hydroelectric power. Not all renewable energy resources come from the sun. Geothermal energy taps the

Earth's internal heat for a variety of uses, including electric power production, and the heating and cooling

of buildings and the energy of the ocean's tides come from the gravitational pull of the moon and the sun

upon the Earth. In fact, ocean energy comes from a number of sources. In addition to tidal energy, there's

the energy of the ocean's waves, which are driven by both the tides and the winds. The sun also warms the

surface of the ocean more than the ocean depths, creating a temperature difference that can be used as an

energy source. All these forms of ocean energy can be used to produce electricity that forms the renewable

energy resources.

Source Installed Capacity (MW) % Contribution

Coal 193426.5 58.7

Large Hydro 44765.42 13.6

Small Hydro 4384.55 1.3

Wind Power 32508.17 9.9%

Solar Power 13114.85 4

Biomass 8295.78 2.5

Nuclear 6780 2.1

Gas 25185.38 7.6

Diesel 837.63 0.3





The solar power demand has grown exponentially worldwide during the last decade because it is

inexhaustible and clean. It has also become more efficient since the power conversion efficiency of

photovoltaic solar cells has increased. As India is a tropical country, where sunshine is available for longer

duration and in great intensity, it has enormous potential as future energy source. It is also profitable and

beneficial to facilitate rural and backward areas by permitting the decentralized distribution of electricity,

thereby empowering people at the grassroots level (Srivastava & Srivastava, 2013). The Department of

non-conventional Energy Sources, Govt. of India, the first ministry of renewable energy in the world,

formed in 1982, undertook a number of developmental programs and demonstration projects. Solar power

generating capacity grew by 73% in 2010 picking up the pace again after a brief slowdown in 2009. In

2006, the Rural Electrification Program was the first step by the Government of India in recognizing the

importance of solar power. It gave guidelines for the implementation of off-grid solar applications (Basu,

Karmakar, & Bhattacharya, 2015). Following these trends, today, India accounts for approximately 4% of

the total global electricity generation and contributes 4.43 per cent to the global renewable generation

capacity amounting to 2,011 GW in 2016 (CEA, International Renewable Energy Agency (IRENA)).

To take appropriate decisions or in understanding the true solar energy potential in Indian states, the

researchers felt a knowledge or resource gap in existing literature. An effort in analyzing the same is thus

an emergent need as this sector is open to private participation as well. Evaluation of solar energy or power

potential in Indian states can be best determined in consideration of the most important need based criterion

like number of household depends on solid fuel lighting, number of un-electrified villages (UE), demand

of power or power deficit, space availability which is proportional with the area of waste land and inversely

proportional with the population density and solar radiation power potential in different state as has been

found out from existing literature (EAI Catalyzing Cleantech and Sustainability). As multiple criterions are

taken into consideration against a set of alternatives; here Indian states, multi-criteria decision making

(MCDM) methods are most suitable for the purpose of analysis. Different methods are available under the

MCDM approach such as analytic hierarchy process (AHP), Technique for Order Preference by Similarity

to Ideal Solution (TOPSIS), Vise Kriterijumska Optimizacija I Kompromisno Resenje (VIKOR) etc

(Akkas, Erten, Cam, & Inanc, 2017). In this study, VIKOR method is used to identify the solar energy or

power potential in Indian states.

LITERATURE REVIEW

Existing literature on existing energy resources, solar energy, factors affecting solar power generation and

topics related to energy generation in India were reviewed with the multifold objectives; primarily to have

a thorough understanding on the subject; identify the research areas already explored and thus frame the

research gap. Individual researches along with various organizational researches carried both by public and

private sector were consulted. The importance of renewable energy and its advantages over fossil fuels have

been explored in the paper “Assessment of Renewable Energy in India” (Dhingra et al., 2014). The research

work concludes that owing to sheer size of the population and the expansion of the economy, the increase

in absolute terms of energy produced by conventional fossil fuel is going to be very high. It is therefore

very critical to reverse this trend in order to achieve effective results in the global efforts against climate

change. (Kapoor et al., 2014) in their research paper “Evolution of solar energy in India: A review” have

tried to outline the journey of solar energy in India since 1950 till date and highlighted the potential issues

as barriers and challenges for better planning and management in the field of solar energy. (Khare, Vikas

et al., 2013) presents in a coherent and integrated way the major constraints hampering the development of

renewable energy in India through their work “Status of solar wind renewable energy in India”. They have

shown that condition of renewable energy sources such as solar and wind system is satisfactory in India but

requires further attention on specific technological systems and better policy management. Emphasis on the

shift from conventional energy to renewable energy resources and the need to develop an inbuilt





consciousness about the necessity for such a change is captured in the work “Overview of

solar energy in India” (Venkatakrishnan & Rengaraj, 2014). An overview of technical, economic and

policy aspects of solar energy development and the status of solar energy in terms of resource potential,

existing capacity, along with historical trends and future growth prospects of solar energy have been

analyzed in the research paper “Solar Energy Fundamentals and Challenges in Indian restructured power

sector” (Upadhyay & Chowdhury, 2014). The paper also highlights that ways of improving the efficiency

of renewable power generation lay in technology up gradation. The paper “The Renewable Energy Sector

in India: an overview of research and activity” (Mezzetti, 2011) gives an overview of the present scenario

and the projected growth in the next decade and tried to highlight some of the priorities identified in India

for R&D and technology transfer. (Akkas et al., 2017) suggested in their research paper “Optimal Site

Selection for a Solar Power Plant in the Central Anatolian Region of Turkey” about location selection in

the regions of Turkey, which is key to PVPS’s establishment. They had analyzed, the criteria for selecting

the appropriate location by the multi-criteria decision making (MCDM) methods and concluded by finding

the city that is most suitable for installation of solar power plants. The study on “A Decision Support System

for Selection of Solar Power Plant Locations by Applying Fuzzy AHP and TOPSIS: An Empirical Study”

(Kengpol et al., 2013) also provides an approach about the site selection. The research proposes a decision

support system for avoiding flood on solar power plant site selection in Thailand and integrates the

qualitative and quantitative variables based on adoption of the Fuzzy Analytic Hierarchy Process (Fuzzy

AHP) and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) model. The

research work, “Optimal site selection for sitting a solar park using multi-criteria decision analysis and

geographical information systems” describes a general integrated framework to evaluate land suitability for

the optimal placement of photovoltaic solar power plants, which is based on a combination of a geographic

information system (GIS), remote sensing techniques, and multi-criteria decision-making methods

(Georgiou & Skarlatos, 2016). “Hotspots of solar potential in India” (Ramachandra et al., 2011), a

research work, focuses on the assessment of resource potential with variability in India derived from high

resolution satellite derived insulation data. Data analysis reveals that nearly 58% of the geographical are

potentially represent the solar hotspots in the country with more than 5kWh/m2/day of annual average

Global insulation. Studies on solar power generation potential in Indian states have not been found and that

forms the basis of further studies by the researcher. This gap identified by them has been translated to

specific objectives.

OBJECTIVES

The present research work frames two basic objectives

1. Ranking 29 states of India in terms of solar power generation potential (excluding Telangana as it’s

a newly formed state) and finding the optimal and/ or compromise solution

2. Evaluate the importance level or weights of criterions influencing solar power generation potential.

RESEARCH FRAMEWORK

RESEARCH DESIGN

This study has been systematically designed to address the research objectives. Descriptive study forms the

basis of the ensuing study. The descriptive form of research allows one to draw rich inference that leads to

important recommendations. Prioritizing Indian states with respect to solar energy generation potential is

accomplished using VIKOR, an MCDM approach while relative importance of criterions have been

determined using Shannon’s Entropy approach. The author used R 3.4.0 version programming language

and software environment for all computations made in the present study.

RESEARCH METHODOLOGY





The VIKOR (VIse Kriterijumska Optimizacija Kompromisno Resenje - the Serbian name),

can be translated as Multi-criteria Optimization and Compromise Solution method is one of the multi

criteria decision analysis method which was developed by Serafim Opricovic (1980). VIKOR ranks

alternatives and determines the solution named compromise that is the closest to the ideal. The idea of

compromise solution was introduced in MCDM by Po-Lung Yu in 1973, and by Milan Zeleny before

Opricovic. Applications of this method were presented in 1998 (Oprikovic) and up to now. VIKOR is used

in the ensuing study to find solutions the objectives framed. VIKOR, as an MCDM method focuses on

ranking and selecting from a set of alternatives, and determines the compromise solution obtained with the

initial weights for a problem with conflicting criteria. Assuming that each alternative is computed according

to each criteria function, the compromise ranking is performed through comparing the measure of closeness

to the ideal alternative. It is a compromise decision making method which not only considers maximum

group utility but also considers minimum individual regret. The multi-criteria measure for compromise

ranking is developed from the Lp – metric used as an aggregating function in a compromise programming

method.

𝑳𝒑𝒊 = { ∑ [ (𝒂𝒋

∗− 𝒂𝒊𝒋)

(𝒂𝒋∗− 𝒂𝒋

−) ]

𝒑𝒏𝒊=𝟏 }

𝟏𝒑⁄ ; 1 ≤ 𝑝 ≤ ∞ 𝑎𝑛𝑑 𝑖 = 1,2, … , 𝑚

The compromise ranking algorithm of VIKOR consists of the following steps:

I. Establishing the decision matrix for ranking.

where i Ꞓ 1, 2, … m and represents Alternatives; j Ꞓ 1, 2, … n and represents Criteria

II. Establishing Normalised performance matrix, the purpose of which is to unify the unit of

matrix entries as per the following transformation formulae:

𝒇𝒊𝒋 = 𝒙𝒊𝒋

√𝒙𝒊𝒋𝟐

where i Ꞓ 1, 2, … m and j Ꞓ 1, 2, … n

III. Determining the Best and Worst Values of all Criteria Functions; j = 1, 2, …, n

𝒇𝒋∗ = 𝐦𝐚𝐱

𝒊(𝒇𝒊𝒋 | 𝒋 ∈ 𝑰) OR 𝒇𝒋

∗ = 𝐦𝐢𝐧𝒊

(𝒇𝒊𝒋 | 𝒋 ∈ 𝑱);

𝒇𝒋− = 𝐦𝐢𝐧

𝒊(𝒇𝒊𝒋 | 𝒋 ∈ 𝑰) OR 𝒇𝒋

− = 𝐦𝐚𝐱𝒊

(𝒇𝒊𝒋 | 𝒋 ∈ 𝑱);

where I is associated with benefit criteria and J is associated with cost criteria

IV. Computation of Utility Measure and Regret Measure.

The Utility Measure (𝑺𝒊) is given by 𝑺𝒊 = ∑ 𝒘𝒋 ( 𝒇𝒋

∗− 𝒇𝒊𝒋 )

( 𝒇𝒋∗− 𝒇𝒋

− )𝒏𝒋=𝟏 and

The Regret Measure (𝑹𝒊) is given by 𝑹𝒊 = 𝐦𝐚𝐱𝒋

𝒘𝒋 ( 𝒇𝒋

∗− 𝒇𝒊𝒋 )

( 𝒇𝒋∗− 𝒇𝒋

− ) ;

where 𝑤𝑗 are the criteria weights expressing their relative importance

V. Computation of VIKOR Index 𝑸𝒊

𝑸𝒊 = 𝒗 (𝑺𝒊 − 𝑺∗)

(𝑺− − 𝑺∗)+ (𝟏 − 𝒗)

(𝑹𝒊 − 𝑹∗)

(𝑹− − 𝑹∗)

where 𝑆∗ = min𝑖

𝑆𝑖 𝑎𝑛𝑑 𝑆− = max𝑖

𝑆𝑖

C1 C2 C3 …… Cn

A1 x11 x12 x13 …… x1n

A2 x21 x22 x23 …… x2n

A3 x22 x32 x33 …… x3n

……

…..

……

……

……

……

Ai xi1 xi2 xi3 …… xin





𝑅∗ = min𝑖

𝑅𝑖 𝑎𝑛𝑑 𝑅− = max𝑖

𝑅𝑖

𝑎𝑛𝑑 𝑣 = 𝑤𝑒𝑖𝑔ℎ𝑡 𝑜𝑓 𝑡ℎ𝑒 𝑑𝑒𝑐𝑖𝑠𝑖𝑜𝑛 𝑚𝑎𝑘𝑖𝑛𝑔 𝑠𝑡𝑟𝑎𝑡𝑒𝑔𝑦 (𝑚𝑎𝑥. 𝑔𝑟𝑜𝑢𝑝 𝑢𝑡𝑖𝑙𝑖𝑡𝑦) VI. Ranking the alternatives. It is done by sorting the values of S, R & Q in decreasing order. The

results are the three ranking lists. The best alternative is ranked by Min. Q-value.

VII. Proposing a compromise solution the alternative A’ which is ranked by Min. Q value if the

following two conditions are satisfied:

(i) C1 : Acceptable Advantage: Q(A’’) - Q(A’) ≥ DQ where DQ = 1 / (m-1); m= no. of

alternatives and A’ & A’’: alternatives with 1st and 2nd ranking position in the ranking

list by Q-values respectively.

(ii) C2: The alternative must also be best ranked by S or/and R. This compromise

solution is stable within a decision making process which could be

‘voting by majority rule’ when 𝒗 > 0.5 is needed

‘by consensus’ when 𝒗 = 0.5 is needed

‘with veto’ when 𝒗 < 0.5 is needed

If both C1 & c2 are satisfied then the yield is most acceptable; a single optimal solution. If one of the

conditions C1 & C2 are not satisfied then a set of compromise solution is proposed which consists of:

Alternative A’ & A’’ if only C2 is not satisfied or

Alternatives A’, A’’, … 𝐴(𝑀)if C1 is not satisfied. 𝐴(𝑀) is determined by the relation

𝑄(𝐴(𝑀)) − 𝑄(𝐴′) < 𝐷𝑄 for maximum M.

CHOICE OF WEIGHTS

In typical MCDM environment, weights of attributes reflect the relative importance in decision making

process. Because the evaluation of criteria entails diverse opinions and meanings, it is not practical to

assume that each evaluation criterion will be of equal importance. There are two categories of weighting

methods: subjective methods and objective methods. The subjective methods determine weight solely

according to the preference or judgments of decision makers. The objective methods determine weights by

solving mathematical models automatically without any consideration of the decision maker’s preferences.

Many objective weighting measures had been proposed by researchers. Shannon’s entropy concept

(Shannon & Weaver, 1947) is well suited for weight evaluation. The Shannon entropy is a measure of

uncertainty in information formulated in terms of probability theory. Shannon developed this for a measure

of information in a communication stream. Later research has applied his measure to a wide range of

applications including spectral analysis (Burg, 1967), and economics (Golan, Judge, & Miller, 1996) and

even in social sciences. Shannon’s entropy is a highly established and popular method of weight

determination in a multi-criteria environment. The procedure of Shannon’s Weight determination involves

a series of sequential steps as described below.

Step i. Normalization of the data matrix as 𝒑𝒊𝒋 = 𝒙𝒊𝒋

∑ 𝒙𝒊𝒋𝒎𝒋=𝟏

, j = 1, 2, ….., m & i = 1,2,…., n

Raw data normalizing is done to eliminate the anomalies of disparate units of measurement so as allow

comparison on a similar platform.

Step ii. Entropy Ei is calculated as 𝑬𝒊 = − 𝒉𝟎 ∑ 𝒑𝒊𝒋𝒎𝒋=𝟏 . 𝐥𝐧 𝒑𝒊𝒋

i.e. 𝑬𝒊 = − 𝒉𝟎 ∑𝒙𝒊𝒋


𝒎𝒋=𝟏 𝐥𝐧

𝒙𝒊𝒋


, i = 1,2, …,n and

𝒉𝟎 is the entropy constant and is defined as 𝒉𝟎 = (𝐥𝐧 𝒎)−𝟏

Step iii. Defining 𝒅𝒊 as 𝒅𝒊 = 𝟏 − 𝑬𝒊 and

Step iv. Defining Shannon’s Entropy Weight 𝑾𝒊 as 𝑾𝒊 = 𝒅𝒊

∑ 𝒅𝒊𝒏𝟏=𝟏

DATA COLLECTION





Out of the two different types of data sources, the researchers chose secondary data for the

present study. The same has been collected from different government website like REC, Report of ministry

of Power, Census report etc owing to its reliability.

FINDINGS & ANALYSIS

To explore the nature of relationship that exists among the chosen criterions (variables), Corplot (correlation

plot, Fig 2) was extracted. One finds that correlation values are quite low except for two cases where it is

found to be 0.55 (not very high). For criterions with high value of correlation amongst them, it may be

argued that multi collinearity may exist; a situation in which two or more explanatory variables is highly

related linearly. In case one finds existence of exact or near exact linear relationships among variables,

multicollinear exists, which indicates estimate of impact of an independent variable on the dependent

variable tends is less precise as the collinear independent variables contain same information about the

dependent variable. VIF (variance inflation factor), a measure of existence of multicollinearity, were found

to be < 10 for the variables (Fig 3), thereby indicating absence of multicollinearity in the data set. Hence,

no criterions were dropped.

1. Correlation & Multi-Collinearity Test

Fig 2: Correlation Plots; Source - R output from Secondary Data

[HHSFL: Number of households that depend on solid fuel lighting, Pow_Def: Power Deficit, W_Land: W

aste Land, Rad_Pot: Solar Radiation Potential, UE_Vill: Unelectrified Villages, Pop_Den.: Population De

nsity]

Variance Inflation Factor

HHSFL Pow_Def W_Land Rad_Pot UE_Vill Pop_Den

1.227082 1.365607 1.621983 1.522234 1.102344 1.120709

Fig 3: VIF Table; Source - R output from Secondary Data

2. Decision and Normalized Decision Matrix

Fig 4 below shows original values of alternatives against each criterion that forms the decision matrix, also

called the performance matrix. Each of the criterions has different measuring units and further analysis is

not allowed unless they are transformed to a common unit or made unit free. The effect of disparate units

is eliminated through the process of statistical normalization. Fig 6 shows normalized decision

(performance) matrix. Here, all data shown are unit free. Relative importance of criterions has been

evaluated using Shannon’s entropy. Fig 7 below shows the weights and one can find out that unelectrified

villages have the highest importance followed by power deficiency and population density in order of

decreasing weights in the list of top 3 important criterions affecting solar power generation potential in

Indian states.





Fig 4: Decision Matrix; Source - Secondary Data

Fig 5: Normalized Performance Table; Source - R output from Secondary Data

No. of household

depend on solid

fuel lighting

Power deficit

(mu)

Waste

Land

(1000 ha)

Radiation

potential

(GWp)

UE Village

Population

Density

(No/km2)

HHSFL Pow_Def W_Land Rad_Pot UE_Vill Pop_Den

1 ANDHRA PRADESH AP 650873 5939 2056 38.44 0 303

2 ARUNACHAL PRADESH ARP 82147 74 1160 8.65 1191 17

3 ASSAM ASM 3954090 2082 851 13.76 373 397

4 BIHAR BIH 15720722 6656 432 11.20 214 1102

5 CHATTISH GARH CH 1338238 -1546 308 18.27 251 189

6 DELHI DEL 26724 -5774 16 2.05 0 11297

7 GOA GOA 9362 1 0 0.88 0 394

8 GUJRAT GUJ 1157263 -4380 2595 35.77 0 308

9 HARYANA HAR 438770 -1269 103 4.56 0 573

10 HIMACHAL PRADESH HP 45774 -295 656 33.84 0 123

11 JAMMU & K J &K 280097 2438 288 111.05 101 57

12 JHARKHAND JH 3307160 2796 1116 18.18 356 414

13 KARNATAKA KAR 1212552 -3240 788 24.7 10 319

14 KERALA KER 416684 -1095 25 6.11 0 859

15 MADHYA PRADESH MP 5857437 -8853 1351 61.66 46 236

16 MAHARASTRA MAHA 3789062 -11333 1718 64.32 0 365

17 MANIPUR MAN 150624 37 24 10.63 62 122

18 MEGHALAYA MEGH 206169 150 421 5.86 121 132

19 MIZORAM MIZ 32056 -56 0 9.09 11 52

20 NAGALAND NAG 72394 127 160 7.29 0 119

21 ORISSA ORI 5468174 -659 1095 25.78 365 269

22 PUNJAB PUN 178520 3784 24 2.81 0 550

23 RAJASTAN RAJ 4076342 170 2295 142.31 0 201

24 SIKKIM SIK 9354 -531 107 4.94 0 86

25 TAMIL NADU TN 1202045 -11649 492 17.67 0 555

26 TRIPURA TRI 250306 -1073 1296 2.08 0 350

27 UTTAR PRADESH UP 20643515 7044 507 22.83 2 828

28 UTTARAKHAND UT 235654 336 224 16.8 49 189

29 WEST BENGAL WB 8889813 7257 21 6.26 0 1029

INDIAN STATE

(Alternatives)

Abbreviations

Used

Table 5: Performance Table; Source - R output from Secondary Data

NORMALIZED PERFORMANCE TABLE [HHSFL] [POW-DEF] [W_LAND] [RAD_POT] [UE_VILL] [POP_DEN] [AP] 0.0219256616 4.055478e-01 0.3792403704 0.174922842 0.0007143202 0.026214240 [ARP] 0.0027672485 5.053129e-03 0.2139683024 0.039362190 0.8507553053 0.001470766 [ASM] 0.1331996244 1.421705e-01 0.1569715736 0.062615460 0.2664414180 0.034346711 [BIH] 0.5295767840 4.545085e-01 0.0796847471 0.050966072 0.1528645133 0.095340239 [CHA] 0.0450806125 6.828553e-05 0.0568122734 0.083138406 0.1792943590 0.016351457 [DEL] 0.0009002392 6.828553e-05 0.0029512869 0.009328611 0.0007143202 0.977367227 [GOA] 0.0003153734 6.828553e-05 0.0001844554 0.004004477 0.0007143202 0.034087164 [GUJ] 0.0389841903 6.828553e-05 0.4786618489 0.162772894 0.0007143202 0.026646818 [HAR] 0.0147806446 6.828553e-05 0.0189989096 0.020750472 0.0007143202 0.049573464 [HP] 0.0015419678 6.828553e-05 0.1210027641 0.153990347 0.0007143202 0.010641424 [J&K] 0.0094354997 1.664801e-01 0.0531231647 0.505337709 0.0721463357 0.004931392 [JH] 0.1114067889 1.909264e-01 0.2058522633 0.082728857 0.2542979754 0.035817476 [KAR] 0.0408466856 6.828553e-05 0.1453508813 0.112398392 0.0071432016 0.027598490 [KER] 0.0140366437 6.828553e-05 0.0046113858 0.027803813 0.0007143202 0.074316938 [MP] 0.1973168057 6.828553e-05 0.2491992901 0.280586431 0.0328587272 0.020417692 [MAHA] 0.1276404015 6.828553e-05 0.3168944341 0.292690873 0.0007143202 0.031578210 [MAN] 0.0050740019 2.526565e-03 0.0044269304 0.048372263 0.0442878496 0.010554909 [MEGH] 0.0069451210 1.024283e-02 0.0776557373 0.026666177 0.0864327388 0.011420065 [MIZO] 0.0010798558 6.828553e-05 0.0001844554 0.041364428 0.0078575217 0.004498813 [NAGA] 0.0024387036 8.672263e-03 0.0295128693 0.033173453 0.0007143202 0.010295362 [ORI] 0.1842038808 6.828553e-05 0.2019786992 0.117312977 0.2607268568 0.023272708 [PUN] 0.0060137217 2.583925e-01 0.0044269304 0.012787024 0.0007143202 0.047583604 [RAJ] 0.1373178717 1.160854e-02 0.4233252190 0.647587658 0.0007143202 0.017389644 [SIK] 0.0003151039 6.828553e-05 0.0197367313 0.022479678 0.0007143202 0.007440345 [TN] 0.0404927411 6.828553e-05 0.0907520731 0.080408080 0.0007143202 0.048016182 [TRI] 0.0084319439 6.828553e-05 0.2390542413 0.009465128 0.0007143202 0.030280475 [UP] 0.6954086640 4.810033e-01 0.0935189046 0.103888878 0.0014286403 0.071634953 [UT] 0.0079383687 2.294394e-02 0.0413180170 0.076449109 0.0350016876 0.016351457 [WB] 0.2994670715 4.955481e-01 0.0038735641 0.028486394 0.0007143202 0.089024597





3. Shannon’s Weight Determination

Fig 6: Shannon’s Weight Table; Source - R output from Secondary Data

The VIKOR outputs are shown in Fig 7. From the utility measure (S) and regret measure (R), VIKOR Index

(Q) is calculated. Lower the Q value better is the alternative. Assam emerges as the state with highest solar

power generation potential followed by Jharkhand and Bihar in order of decreasing potential. The Q value

or rank is also presented graphically in Fig 7 from where visual comparison may be made.

4. Utility Measure (S), Regret Measure (R) and VIKOR Index (Q)

Fig 7: VIKOR Output; Source - R output from Secondary Data

5. Optimal and Compromise Solution The states are ranked according to VIKOR Index, lowest value indicating the best alternative and vice-

versa. However, it is a matter of interest to evaluate and find out if the best alternative is an optimal single

solution or there exists a compromise solution for the best alternative. Test of single optimal solution is

first measured. DQ = 1/(m-1) = 0.0357. Acceptable Advantage: Q(A’’) - Q(A’) = 0.1690453 – 0.1665611

= 0.0024842 ≤ DQ. Thus, the 1st condition (C1) - existence of single optimal solution is not satisfied. The

2nd condition states that the alternative must also be best ranked by S or/and R. The output shows that A’ is

also the best alternative by R value (not by S); thereby meeting criteria 2 (C2). Thus one may conclude that

there is no single optimal solution but a compromise solution. The same is evaluated from the condition

that states that Compromise Solution is a set of Alternatives A’, A’’, 𝐴(𝑀) if C1 is not satisfied where 𝐴(𝑀)

is determined by the relation Q (𝐴(𝑀)) - Q(A') < DQ for maximum M. Fig 8 shows the measurement of

compromise solution. It is observed that it is only A’’ that meets the compromise solution criterion thereby

including A’ (Assam) and A’’ (Jharkhand) in the compromise solution set.

HHSFL POW_DEF W_LAND RAD_POT UE_VILL POP_DEN 0.1696 0.2122 0.0906 0.0971 0.2242 0.2054

VIKOR Output Alternatives S R Q Ranking AP 0.52288 0.22421 0.60265 8 ARP 0.52180 0.21052 0.50412 5 ASM 0.59938 0.15412 0.16656 1 BIH 0.42757 0.18408 0.23674 3 CHA 0.71700 0.21266 0.68246 13 DEL 0.99852 0.22421 1.00000 29 GOA 0.80145 0.22421 0.83536 27 GUJ 0.67583 0.22421 0.73042 15 HAR 0.79508 0.22421 0.83004 26 HP 0.75071 0.22421 0.79297 20 J & K 0.61718 0.20537 0.54702 6 JH 0.57500 0.15732 0.16904 2 KAR 0.74463 0.22251 0.77580 19 KER 0.80213 0.22421 0.83593 28 MP 0.65303 0.21573 0.65089 10 MAHA 0.66624 0.22421 0.72241 14 MAN 0.77529 0.21272 0.73152 16 MEGH 0.74998 0.20830 0.67886 12 MIZ 0.78752 0.22233 0.81028 23 NAG 0.78227 0.22421 0.81934 24 ORI 0.63030 0.21266 0.61003 9 PUN 0.68989 0.22421 0.74217 18 RAJ 0.58225 0.22421 0.65225 11 SIK 0.78935 0.22421 0.82525 25 TN 0.76587 0.22421 0.80564 22 TRI 0.75258 0.22421 0.79454 21 UP 0.39999 0.22402 0.49865 4 UT 0.75827 0.21517 0.73478 17 WB 0.52279 0.22421 0.60258 7





Fig 8: Compromise Solution Table; Source – Author’s calculation using R Output

CONCLUSIONS AND RECOMMENDATIONS

Solar power or electricity generation has the potential to grow to very large scale. The two prime challenges

for solar energy organizations and even for Ministry of New and Renewable Energy include choosing the

right area or location for solar power generation and secondly making the right decision for fulfilling future

renewable energy demand. It may be concluded from the study that Assam and Jharkhand forms the

compromise solution and no single optimal solution exists. Both these states have the highest solar power

generation potential in India followed by Bihar, Uttar Pradesh and Arunachal Pradesh in order of decreasing

potential. The next set of 5 states with decreasing solar power generation potential in India are Jammu &

Kashmir, West Bengal, Andhra Pradesh, Orissa and Madhya Pradesh. It is to be noted that some

geographically smaller states like West Bengal, Assam, Jharkhand have also emerged in the list of top five.

The study acts a ready reference to those organizations planning to invest in solar energy sector in India

and looking for a comprehensive report on location or site selection performance.

LIMITATIONS AND SCOPE FOR FURTHER STUDY

Since no research is devoid of limitations, the present study too suffers from the same. The study takes into

account six criterions. One may include more of them to further strengthen the outcome in future. The

external effects of political stability and governance were not included in this study. Inclusion of the same

would make the study even more robust. Some of the criteria data used in the study are not of the same time

period or frame. The latest published data from government sources have only been considered. In future,

data at a particular time frame for all criterions, if found available, may help in more precise analysis. One

may use some other ranking and weight determination method to get further perspective on the subject.

REFERENCES

(2017). Draft National Energy Policy. NITI Aayog, Govt. of India. NITI Aayog.

Akkas, O. P., Erten, M. Y., Cam, E., & Inanc, N. (2017). Optimal Site Selection for a Solar Power

Plant in the Central Anatolian Region of Turkey. (M. R. Islam, Ed.) International Journal of

Photoenergy, 2017, 1-14.

Aliguliyev, R. M., & Mahmudova, R. S. (2015). Multicriteria Personnel Selection by the Modified

Fuzzy VIKOR Method. The Scientific World Journal , 2015, 1-17.

Basu, S. N., Karmakar, A., & Bhattacharya, P. (2015). Growth of Solar Energy in India – Present

Status and Future Possibilities. International Journal of Electrical, Electronics and Computer

Systems (IJEECS) , 3 (5), 2347-2820.

Alternatives Q Rank Q(AM

) - Q(A1) > / < DQ = 1/(m-1) Alternatives Q Rank Q(A

M) - Q(A

1) > / < DQ = 1/(m-1)

AP 0.6027 8 0.4361 > 0.0357 MAHA 0.7224 14 0.5559 > 0.0357

ARP 0.5041 5 0.3376 > 0.0357 MAN 0.7315 16 0.5650 > 0.0357

ASM 0.1666 1 0.0000 < 0.0357 MEGH 0.6789 12 0.5123 > 0.0357

BIH 0.2367 3 0.0702 > 0.0357 MIZ 0.8103 23 0.6437 > 0.0357

CHA 0.6825 13 0.5159 > 0.0357 NAG 0.8193 24 0.6528 > 0.0357

DEL 1.0000 29 0.8334 > 0.0357 ORI 0.6100 9 0.4435 > 0.0357

GOA 0.8354 27 0.6688 > 0.0357 PUN 0.7422 18 0.5756 > 0.0357

GUJ 0.7304 15 0.5639 > 0.0357 RAJ 0.6523 11 0.4857 > 0.0357

HAR 0.8300 26 0.6635 > 0.0357 SIK 0.8253 25 0.6587 > 0.0357

HP 0.7930 20 0.6264 > 0.0357 TN 0.8056 22 0.6391 > 0.0357

J&K 0.5470 6 0.3805 > 0.0357 TRI 0.7945 21 0.6280 > 0.0357

JH 0.1690 2 0.0025 < 0.0357 UP 0.4987 4 0.3321 > 0.0357

KAR 0.7758 19 0.6092 > 0.0357 UT 0.7348 17 0.5682 > 0.0357

KER 0.8359 28 0.6694 > 0.0357 WB 0.6026 7 0.4360 > 0.0357

MP 0.6509 10 0.4843 > 0.0357

Compromise Solution





Bhattacharya, S., & Jana, C. (2009). Renewable energy in India: Historical developments

and prospects. Energy , 34, 981-991.

Buragohain, T. (2012). Impact of Solar Energy in Rural Development in India. International Journal

of Environmental Science and Development , 3 (4), 1-5.

Burg, A. (1967). Light Sensitivity as Related to Age and Sex. Perceptual Motor Skills, 24, 1275-

1288.

Dhingra, R., Jain, A., Pandey, A., & Mahajan, S. (2014). Assessment of Renewable Energy in India.

International Journal of Environmental Science and Development , 5 (5), 1-4.

Dwivedi, A., Bari, A., & Dwivedi, G. (2013). Scope and Application of Solar Thermal Energy in

India-A Review. International Journal of Engineering Research and Technology , 6 (3), 315-322.

E A I Catalyzing Cleantech and Sustainability. (n.d.). Retrieved 2017, from www.eai.in:

http://www.eai.in/ref/ae/sol/cs/sup/if/key_factors_to_consider_while_setting_up_a_solar_plant.ht

ml

Electricity sector in India. (2017). Retrieved 2017, from Wikipedia:

https://en.wikipedia.org/wiki/Electricity_sector_in_India

Georgiou, A., & Skarlatos, D. (2016). Optimal site selection for sitting a solar park using multi-

criteria decision analysis and geographical information systems. Geosci. Instrum. Method. Data

Syst. , 5, 321-332.

Golan, A., Judge, G., Miller, D., 1996. Maximum entropy econometrics: robust estimation with

limited data, John Wiley and Sons, New York.

http://www.eai.in/ref/ae/sol/cs/sup/if/key_factors_to_consider_while_setting_up_a_solar_plant.ht

ml

Hwang, C. L., & Yoon, K. S. (1981). Multiple attribute decision making: Methods and applications.

Berlin: Springer-Verlag.

Hwang, C.L.; Lin, M.J. Group Decision Making under Multiple Criteria: Methods and Applications

Springer: Berlin, Germany, 1987.

Indoor Air Pollution in India. (2001). ICMR Bulletin, 35 (5), 1-9.

Kapoor, K., Pandey, K. K., Jain, A., & Nandan, A. (2014). Evolution of solar energy in India: A

review. Renewable and Sustainable Energy Reviews , 40, 475-487.

Kengpol, A., Rontlaong, P., & Tuominen, M. (2013). A Decision Support System for Selection of

Solar Power Plant Locations by Applying Fuzzy AHP and TOPSIS: An Empirical Study. Journal

of Software Engineering and Applications , 6, 470-481.

Key factors to consider while setting up a solar plant. (2017). Retrieved 2017, from E A I Catalyzing

Cleantech & Sustainability:

Khare, V., Nema, S., & Baredar, P. (2013). Status of solar wind renewable energy in India.

Renewable and Sustainable Energy Reviews , 27, 1-10.

Kumar, V., Kumar, R., & Maithani, A. (2015). Energy Policy in India. IOSR Journal of Electrical

and Electronics Engineering (IOSR-JEEE) , 10 (1), 67-72.

List of states and union territories of India by population. (2011). Retrieved Nov 2017, from

https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_population

Lolla, S., Roy, S. B., & Chowdhury, S. (2015). Wind and Solar Energy Resources in India. Energy

Procedia, 76, 187-192.

Mardani, A., Zavadskas, E. K., Govindan, K., Senin, A. A., & Jusoh, A. (2016). VIKOR Technique:

A Systematic Review of the State of the Art Literature on Methodologies and Applications.

Sustainability, 8 (37), 1-38.

Mezzetti, S. (2011). The Renewable Energy Sector in India: an overview of research and activity.

Indo-Italian Business Conference 2011 (pp. pp. 1-34). Chennai: European Business and

Technology Centre.





Noorollahi, E., Fadai, D., Shiraz, M. A., & Ghodsipour, S. H. (2016). Land Suitability

Analysis for Solar Farms Exploitation Using GIS and Fuzzy Analytic Hierarchy. (S. Sundaram, &

T. Mallick, Eds.) Energies, 9, 643, 1-24.

Ojha, P. (., Gaur, G. K., Kumar, D. S., & Singh, D. L. (2014). Solar Energy and Economic

Development In India: A Review. International Journal of Emerging Technology and Advanced

Engineering, 4 (1).

Opricovic, S and Tzeng, G. H. (2007), Extended VIKOR method in comparison with outranking

methods, European Journal of Operational Research, Vol. 178, 514–529.

Opricovic, S. (1998). Multicriteria Optimization in Civil Engineering, Faculty of Civil Engineering,

Belgrade, ISBN 86-80049-82-4

Opricovic, S., and Tzeng, G. H. (2004). Compromise solution by MCDM methods: A comparative

analysis of VIKOR and TOPSIS. European Journal of Operational Research , 156, 445–455.

Osmani, A. R. (2014). Conventional Energy to Renewable Energy: Perspectives for India. The

NEHU Journal, XII (2), 41-60.

Pangsri, P. (2015). Application of the Multi Criteria Decision Making Methods for Project

Selection. Universal Journal of Management, 3(1), 15-20.

Pohekar, S., & Ramachandran, M. (2004). Application of multi-criteria decision making to

sustainable energy planning - A review. Renewable and Sustainable Energy Reviews, 8, 365-381.

Prasad, K. P., Rao, M.V., and Patro, C. (2016). Supplier Selection through AHP-VIKOR Integrated

Methodology. SSRG International Journal of Industrial Engineering, 3 (5), 1-6.

Ramachandra, T., Jain, R., & das, G. K. (2011). Hotspots of solar potential in India. Renewable

and Sustainable Energy Reviews, 15, 3178-3186.

Reddy, B. V., Sata, S. P., Reddy, S. K., & Babu, B. J. (2016). Solar Energy Analytics Using Internet

of Things. International Journal of Applied Engineering Research, 11 (7), 4803-4806.

Remap Renewable Energy Prospects for India. (2017). Retrieved 2017, from IRENA:

http://www.irena.org/DocumentDownloads/Publications/IRENA_REmap_India_paper_2017.pdf

Rural Electrification Corporation. (2017). Retrieved 2017, from http://www.recindia.nic.in:

http://www.recindia.nic.in

Saini, S. S., and Nagar, S. (2013). Comparative Study Of External Supply Chain Performance Of

FMCG Firms Using Multi Criteria Decision Technique [VIKOR Method]. International Journal of

Advances in Engineering Science and Technology, 2 (2), 196-206.

Sasikumar, N., & Jayasubramaniam, D. (2013). Solar Energy System in India. IOSR Journal of

Business and Management (IOSR-JBM), 7 (1), 61-68.

Savita Lolla, S. B. (2015). Wind and Solar Energy Resources in India. Energy Procedia, 76, 187-

192.

Shannon, C. E. (1948). A Mathematical Theory of Communication, Bell System Technical Journal,

27 (July and October), 379–423, 623–656.

Sharma, N. K., Tiwari, P. K., & Sood, Y. R. (2012). Solar energy in India: Strategies, policies,

perspectives and future potential. Renewable and Sustainable Energy Reviews, 16, 933-941.

Shimraya, B. A., Singhb, K. M., & Mehta, R. K. (2017). A survey of Multi-Criteria Decision

Making Technique used in Renewable Energy Planning. International Journal of Computer (IJC) ,

25 (1), 124-140.

Srivastava, S. P., & Srivastava, S. P. (2013). Solar Energy and its future role in Indian Economy.

International Journal of Environmental Science: Development and Monitoring, 4 (3).

UNEP Towards a Green Economy: Pathways to Sustainable Development and Poverty Eradication.

(2011). Retrieved 2017, from Sustainable Development Knowledge Platform:

https://sustainabledevelopment.un.org/index.php?page=view&type=400&nr=7&menu=35





Upadhyay, A., & Chowdhury, A. (2014). Solar Energy Fundamentals and Challenges in

Indian restructured power sector. International Journal of Scientific and Research Publications, 4

(10), 1-13.

Venkatakrishnan, & Rengaraj. (2014). Overview of Solar Energy in India. International Journal of

Development Research, 4 (3), 605-608.

https://en.wikipedia.org/wiki/List_of_states_and_union_territories_of_India_by_population

Retrieved Nov 2017.

https://en.wikipedia.org/wiki/Electricity_sector_in_India. Retrieved on Nov 2017

www.currentresults.com. (1961-1990). Retrieved on Nov 2017, from Current results - Weather and

science facts: https://www.currentresults.com/Weather/India/annual-sunshine.php

Yadav, H. K., Kumar, V., & Yadav, V. K. (2015). Potential of Solar Energy in India: A Review.

International Advanced Research Journal in Science, Engineering and Technology (IARJSET) , 2

(1), 1-5.

Yu, P. L. (1973). A Class of Solutions for Group Decision Problems, Management Science, 19 (8),

936-946.

Zeleny, M. (1973). Compromise Programming, Multiple Criteria Decision Making, University of

South Carolina Press, Columbia.

https://en.wikipedia.org/wiki/Electricity_sector_in_India

assaying compromise solution from the perspective … · mr. saikat samanta independent management...

Documents