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IGI Global

Publishing Progressive Academic Research Since 1988

Vicente González-Prida (University of Seville, Spain) and Anthony Raman (NTEC Tertiary Group, New Zealand)

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701 East Chocolate Avenue, Hershey, PA 17033, USA

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www.igi-global.com

IGI Global

Publishing Progressive Academic Research Since 1988

Section 1

Section 2

Wind, Solar and other Renewable Energies

Bordering topics about asset management and green energies

Chapter 1 DECISION SUPPORT SYSTEM FOR WIND FARM INSTALLATION USING BIPOLAR ANALYSIS Yasmina Bouzarour-Amokrane, Université De Toulouse, France Ayeley Tchangani, Université De Toulouse, IUT de Tarbes, France François Pérès, Université De Toulouse, France

Chapter 8 A SYSTEM SAFETY ANALYSIS OF RENEWABLE ENERGY SOURCES Warren Naylor, Northrop Grumman Electronic Systems, USA

Chapter 2 DECREASING WEAR OF LARGE VERTICAL AXIS WIND TURBINES BY EMPLOYING A MULTI-LEVEL TURBINE CONCEPT Jan H. Wiśniewski, Warsaw University of Technology Division of Aerodynamics, Poland Chapter 3 ASSESSING THE PROFITABILITY OF CHANGING A TURBINE FOR A HYDROELECTRIC POWER PLANT BASED ON LONG-PERIOD WATER GAUGE READINGS Jan H. Wiśniewski, Warsaw University of Technology, Poland Bartosz M. Olszański, Warsaw University of Technology, Poland Chapter 4 AN OVERVIEW TO THERMAL SOLAR SYSTEMS FOR LOW TEMPERATURE: OUTLINING THE EUROPEAN NORM 12976 Vicente González-Prida, University of Seville, Spain Anthony Raman, NTEC Tertiary Group, New Zealand Chapter 5 A RELIABILITY TEST INSTALLATION FOR WATER HEATING SOLAR SYSTEMS: REQUIREMENTS AND DESIGN ACCORDING TO THE EUROPEAN NORM 12976 Vicente González-Prida, University of Seville, Spain Anthony Raman, NTEC Tertiary Group, New Zealand Chapter 6 ELECTRICITY PRODUCTION FROM SMALL SCALE PHOTOVOLTAICS IN URBAN AREAS Constantinos Psomopoulos, Technological Educational Institute of Piraeus, Greece G. Ch. Ioannidis, Technological Educational Institute of Piraeus, Greece S.D. Kaminaris, Technological Educational Institute of Piraeus, Greece Chapter 7 THE TOPICALITY AND THE PECULIARITIES OF THE RENEWABLE ENERGY SOURCES INTEGRATION INTO THE UKRAINIAN POWER GRIDS AND THE HEATING SYSTEM Vira Shendryk, Sumy State University, Ukraine Olha Shulyma, Sumy State University, Ukraine Yulia Parfenenko, Sumy State University, Ukraine

Chapter 9 PREDICTIVE MAINTENANCE FOR QUALITY CONTROL IN HIGH PRECISION PROCESSES María Carmen Carnero, University of Castilla-La Mancha, Spain Carlos López-Escobar, Aluminum Company of America (ALCOA), Spain Rafael González-Palma, University of Cádiz, Spain Pedro Mayorga, Electrical Technology Institute (ITE), Spain David Almorza, University of Cádiz, Spain Chapter 10 RETROSPECTION OF SOME OF THE IMPACT OF GLOBALIZATION PROCESS IN THE DEVELOPING COUNTRIES’ NATURAL ENVIRONMENT Shahul Hameed, Te Wananga o Aotearoa, New Zealand Chapter 11 CLEAN TECHNOLOGY INDUSTRY - RELEVANCE OF PATENTS AND RELATED SERVICE PROVIDERS Liina Tonisson, Fraunhofer MOEZ, Leipzig, Germany Lutz Maicher, Fraunhofer MOEZ, Leipzig, Germany Chapter 12 MATHEMATICAL AND STOCHASTIC MODELS FOR RELIABILITY IN REPAIRABLE INDUSTRIAL PHYSICAL ASSETS Pablo Viveros, University Federico Santa María, Chile Adolfo Crespo, University of Seville, Spain René Tapia, University Federico Santa María, Chile Fredy Kristjanpoller, University Federico Santa María, Chile Vicente González-Prida, University of Seville, Spain Chapter 13 CHALLENGES IN BUILDING A GREEN SUPPLY CHAIN: A CASE OF INTEL CORPORATION Yudi Fernando, Universiti Sains Malaysia, Malaysia Kurtar Kaur, Universiti Sains Malaysia, Malaysia Ika Sari Wahyuni-TD, International Islamic University Malaysia (IIUM), Malaysia Chapter 14 LOW CARBON FOOTPRINT: SUPPLY CHAIN AGENDA IN MALAYSIAN MANUFACTURING FIRMS Muhammad Shabir Shaharudin, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Sains Malaysia, Malaysia Chapter 15 REVIEW OF SUPPLY CHAIN INTEGRATION ON GREEN SUPPLY CHAIN MANAGEMENT (GSCM) Alia Nadhirah Ahmad Kamal, Universiti Sains Malaysia, Malaysia Yudi Fernando, Universiti Sains Malaysia, Malaysia

Vicente González-Prida received a PhD in Industrial Engineering from the University of Seville, and an Executive MBA from the Chamber of Commerce. He currently works as Program Manager in the company General Dynamics – European Land Systems. He shares his professional performance with the development of research projects in the Department of Industrial Organization and Management at the University of Seville. He has written a multitude of articles for conferences and publications. His main interest is related to industrial asset management, specifically the reliability (design specification, data analysis), maintenance (outsourcing, e-maintenance), and sales management (cost analysis, logistics, organization). Recently, he has published the book After-Sales Service of Engineering Industrial Assets from Springer-Verlag. Anthony Raman is an Academic Staff Member at NTEC Tertiary Group, New Zealand. He has extensive experience within multicultural, bicultural, international and indigenous environments focusing in education & training and international business development. In the past, he held managerial and senior positions in two large public educational institutions in New Zealand and practiced international business consultancy. His qualifications are a postgraduate qualification in Marketing, Associateship of the Chartered Institute of Marketing, and a postgraduate level Graduate Certificate in Research Methods. Apart from being a Charted Marketer with the Chartered Institute of Marketing (UK), he is an elected Council Member and a Registered Professional Marketer with the Canadian Institute of Marketing.

701 East Chocolate Avenue, Hershey, PA 17033, USA

www.igi-global.com

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'HFLVLRQ6XSSRUW6\VWHP IRU:LQG)DUP,QVWDOODWLRQ 8VLQJ%LSRODU$QDO\VLV Yasmina Bouzarour-Amokrane Université de Toulouse, France Ayeley P. Tchangani Université de Toulouse, France François Pérès Université de Toulouse, France

$%675$&7 The necessity to control and reduce the negative impact of human activities on environment and life quality along with technology progress in renewable energy in general and wind energy in particular render it possible today to consider wind energy projects on a large scale. Developing wind energy on a large scale however raises other problems such as choosing an adequate site to settle a wind farm where many other issues such technical feasibility and performance levels, visual pollution, economic and social concerns, etc. must be addressed. Such decisions usually involve many parameters and necessitate the collaboration of many stakeholders. In this context, this chapter proposes an approach based on the concept of bipolar analysis through Benefit Opportunity Cost and Risk (BOCR) analysis, which permits one to address correctly a Group Decision-Making Problem (GDMP) to build a decision support system in order to assist the wind farm installation process.

,1752'8&7,21 Renewable energy is to play a larger role in providing electricity due to its existence over wide geographical areas, in contrast to other energy sources, which are concentrated in a limited number of

countries. Rapid deployment of renewable energy and energy efficiency is resulting in significant energy security, climate change mitigation, and economic benefits (Executive Summary Energy Technology Perspectives 2012. Pathways to a Clean Energy System, 2012).

DOI: 10.4018/978-1-4666-8222-1.ch001

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At national level, at least 30 nations around the world already have renewable energy contributing to more than 20% of energy supply. National renewable energy markets are projected to continue to grow strongly in the coming decade and beyond (Renewables 2013: Global Status Report, 2013). Among renewable energy, the wind power is an energy that does not require fuel and which does not generate greenhouse gas and toxic wastes. It helps to maintain the air quality without polluting the soil and without overexploiting them (only 2% of the soil is required). Potentially harmful, wind energy can however cause noise and visual disturbances that come into consideration when choosing wind farm installation. The location of a wind farm must consider several criteria including its impact on wildlife and wind speed for instance. The Wind power is growing at the rate of 30% annually and a worldwide installed capacity had reached 254 GW, at the end of 2012 (World Wind Energy Association [WWEA]. Half-year report. Technical report, 2012). So far, 72 countries own wind power for commercial use, 22 countries have an installed capacity able to deliver more than 1 GW (Wu, Li, Ba, & Wang, 2013). To achieve such performance it is required to simultaneously consider technological, environmental and political challenges related to the process of matching existing electricity generation capacities with wind energy (Pinson, 2013). Due to the complexity of the socio-economic environment and the rapid technological evolution, a post-evaluation study on wind farm planning is essential to optimize management ability and minimize losses. For example, ignoring the importance of management has exposed China to severe overcapacity and overproducing power equipment problems since 2009. Even now, China produces 20GW of unrequired energy (Wu et al., 2013). Post-evaluation study on wind farm planning consists in: evaluating the planning work, the guiding ideology and optimization of general design plan, judging of design, the feasibility of advanced technology and the accuracy of budget estimates

as for example the selection of turbines for commercial-scale wind farms considering varying wind conditions (Chowdhury, Zhang, Messac, & Castillo, 2013; Montoya, Manzano-Agugliaro, López-Márquez, Hernández-Escobedo, & Gil, 2014). A set of selection indicators are proposed in literature in order to evaluate wind farm planning such as: wind resources, wind farm sites, equipment, policies, management, uncertainty factors, power demand and production, economics, impact on visual environment, connection with power grid and road, load-bearing capacity of soil, restrictions regarding the existence of prohibited places, varying wind velocities and directions, etc. (Wu et al., 2013 ;Rahbari, Vafaeipour, Fazelpour, Feidt, & Rosen, 2014). Considering wind farm sites selection problems, because of the growing concerns of people regarding the (negative) impact of such projects and the great pressure exerted by the authorities in terms of environmental regulations, the implementation of a wind farm installation is done through a rigorous evaluation process taking into account different characteristics at different levels. Structured evaluation process leads to gain environmental and social agreements that will lead to the approval of the competent authorities and thus the implementation of the best project (Lee, Chen, & Kang, 2009). From this point of view, multi-criteria analysis appears to be a suitable tool to merge and analyze all potential projects by establishing a relationship between alternatives and factors that influence the decision (Abu-Taha, 2011; Wu, Geng, Xu, & Zhang, 2014). With the aim of selecting a site for a wind farm installation, this chapter proposes to structure the problem in a multi-criteria framework and use a bipolar analysis to evaluate the negative and positive impact of each potential site on the objectives of the committee or stakeholders group. In order to represent the social link and the potential influences that decision makers may respectively undergo, bipolar approach proposes to model the interactions between decision makers


through concordance and discordance measures. These measures are used to represent respectively the positive and negative influence that each decision maker can undergo from its neighbourhood. The recommendation is then made using a process of consensus seeking. The next section introduces wind farm installation and underlines the importance of postevaluation study. Section 3 presents an overview of the proposed bipolar evaluation model. The developed consensus process to resolve wind farm installation problem is exposed in section 4. Eventually, the paper ends in Section 5 with a case study.

conditions, analysis on traffic conditions, study on local terrain and geological conditions, land acquisition and impact on environment. Wu et al. listed these indicator as follow (Wu et al., 2013): •

:,1')$50,167$//$7,21 For wind farm resource selection (Pestana-Barros & Sequeira-Antunes, 2011; Han, Mol, Lu, & Zhang, 2009; Lee et al., 2009), some studies have demonstrated that the site selection is critical for optimization of wind farm project (Wu et al., 2013). Thomsen et al. (2001) have compared potential for site-specific design of MW (Megawatts) size wind turbines installed at different sites. The result showed that the variation in aerodynamically driven loads and energy production could be more than 50% between different sites. Fuglsang and Thomsen (2001) have presented a method for sitespecific design of wind turbines and compared a 1.5 MW stall regulated wind turbine in normal onshore flat terrain with an offshore wind farm and showed a potential increase in energy production of 28%, installation cost reduced by 10.6–4.6% to offshore wind farm. Connection with electric networks, influence of wind turbines’ height above ground, Wind Effect Gusting and micro sitting of WEGs are factors having a great influence on annual energy production (Joselin-Herbert, Iniyan, Sreevalsan, & Rajapandian, 2007). The successful site selection goes through the assessment of some indicators such as: wind resource assessment, analysis on grid-connection

•

•

•

Wind Resource Assessment: The following indicators can be used to estimate the wind resource potential: annual average wind speed, wind power’s effective usage hours, capacity factor, etc. The larger these indicators are, the more abundant the wind power is. The high quality wind is necessary for more stable wind direction, smaller changes in wind speed, less weather disasters and smaller intensity of turbulence. The study of geographical distribution of wind speeds, characteristic parameters of the wind, topography and local wind flow and measurement of the wind speed are also crucial in wind resource assessment for successful application of wind turbines. Grid-Connection Analysis: The selected site should be as close to the grid as possible to reduce investment and circuit losses in grid engineering and also meet the voltage decrease requirement. What’s more, enough capacity and good quality of power grid is also required, so as to avoid the damaging effects on the grid caused by wind farm’s random output or stop running. Analysis on Traffic Conditions: Traffic and transportation conditions of the selected wind farm should be taken into consideration. It will have to be defined in particular whether the conditions are appropriate for equipment’s transportation, whether the carrying burden of road is suitable for wind turbines and other transport vehicles, that is, the road used for transporting wind turbine should reach three or four levels at least. Terrain and Geological Conditions Analysis: The more complex is the terrain condition, the more serious will be the turbulence phenomenon, of course this


•

is adverse for the output of wind turbine. The geological conditions of selected site should also be taken into consideration. It will have to be decided whether they are suitable for deep mining, housing construction and installing wind turbines or not. The ideal foundation is rock, compact soil or clay, lower water table, small earthquake intensity; the ideal wind farm is a place with regular wind, small turbulences and such as there is no slope of hillside steeper than 30°. Analysis on Land Acquisition and Impact on Environment: Requires also to pay attention to wind farm construction procedure, costs associated with land acquisition, impact on local residents and status of their proper placement.

These issues represent different characteristics that need transversal skills and require several actors as associations, political groups and industry (Lee et al., 2009). Indeed, installing a wind farm implies project leaders, elected representatives, public authorities, etc. The choice of potential implantation sites (alternatives selection) generally returns to elected representatives acting on behalf of the civil society; the choice may be subjected to the approval of public authorities. To take into account of the multiplicity of characteristics involved in a group decision context, the following section provides a bipolar evaluation model to address the selection of wind farm location problem in a multi-criteria environment.

%,32/$5(9$/8$7,2102'(/

ing the potential sites to select A = {a1 , a2 ,… an } , a set of objectives for each decision maker

{

}

dk , O k = o1k , o2k ,…, oqkk . The evaluation of potential sites is realized using a set of criteria associated to each objective and noted

( ) {

C oik = c1, c2, …, cm

k

}.

In the literature, the concept of bipolarity is focused by some works mainly in the field of Industrial Engineering (Imoussaten, Montmain, Trousset, & Labreuche, 2011; Felix, 2008, 1994) and the goal oriented engineering for management led by objectives in Software Engineering systems (Gonzales-Baixauli, Prado, & Mylopoulos, 2004; Giorgini, Mylopoulos, Nicchiarelli, & Sebastiani, 2002 ; Fleurey & Solberg, 2009). These works propose models of bipolar and qualitative influence to assess the effect of actions on the performance of complex systems with not necessarily multicriteria formalism. In the multicriteria framework, Grabisch et al. (2008) define the concept of bipolarity through the scales of criteria or attributes characterizing alternatives. These scales can be bipolar and univariate or unipolar and bivariate. In the former case, the scale is divided into two zones by a neutral point. A positive appreciation is associated to the area located above the neutral point, and a negative appreciation to the zone positioned below this point. On unipolar bivariate scales, an alternative can receive both positive and negative evaluations, reflecting contradictory assessments. According to the nomenclature of bipolarity introduced by Grabisch et al. (2008), the model proposed in this chapter can be considered bipolar and univariate. The elicitation of criteria is realized by considering their supporting nature (where variation is correlated positively with considered objective ok

Formerly a group decision making problem (GDMP) is characterized by a set of decision makers noted D = {d1, d2, …d p } involved in the selection process, a set of alternatives represent-

variation) or rejecting nature C r l (ai ) (where variation is correlated negatively with considered objective variation) with regards to objective achievement. The set of criteria is distributed as follows:


k

k

k

C ol ( ai ) = Csol ( ai ) ∪ Crol ( ai ) . The alternatives are then represented for each decision maker dk Åby selectability measures µsk (ai ) and rejectability measures µ sk ( ai ) ag

gregating respectively the results from the evaluation of the attributes of each supporting and rejecting category (Bouzarour-Amokrane, Tchangani, & Pérès, 2012; 2013a, Tchangani, BouzarourAmokrane, & Pérès, 2012)). In this phase, the Analytic Hierarchy Process (AHP) can be used to structure and assess the various attribute values of alternatives. The AHP procedure is a flexible tool to solve unstructured complex decision problems considering quantitative and qualitative aspects in hierarchical structure. The procedure principle is to decompose a decision problem into clusters going from general to operational level. The AHP procedure can be associated to Benefit, Opportunity, Cost, and Risk analysis (BOCR analysis) to distinguish positive certain (benefit) and uncertain (opportunity) factors from negative certain (cost) and uncertain (risk) factors. Opportunities are usually considered as expectations about positive spin-off, future profits and revenue of future positive developments; whereas benefits represent current revenue or those profits from positive developments one is relatively certain of. Risks in BOCR analysis are supposed to stand for the expected consequences of future negative developments, whereas costs represent (current) losses and efforts and consequences of negative developments one is relatively certain of (Wijnmalen, 2007). Considering AHP-BOCR approach, each alternative can be represented by considering distinctly certain criteria (benefit/cost) and uncertain criteria (opportunity/risk) in supporting and rejecting attribute categories (see Figure 1). The bipolar measures are initially called ‘a priori measures’ and do not reflect the potential impact of influence (positive or negative) of a vicinity.

In the social collaborative decision, decision makers are encouraged to collaborate in the evaluation phase. Since decision group members have usually different perceptions, attitudes, motivations and personalities, positive and / or negative influences can be exercised during the evaluation phase. When a decision maker is individualistic, he will prefer to consider only his point of view. Conversely, a ‘collaborative’ or ‘holistic’ decision maker will tend to integrate the advice of his vicinity in his assessment based on the importance to give to each vicinity member (Bouzarour-Amokrane, Tchangani, & Pérès, 2013b; Tchangani, 2013). To model the influence related to each decision maker’s opinion, the concordance and discordance degrees are defined and integrated in relatives’ measures. These measures allow decision makers to express their level of agreement or disagreement with respect to ‘influential’ players. Considering that V (dk ) is the influencing vicinity of decision maker d k in positive or negative way, the concordance and discordance degrees noted respecd are defined as relative degrees tively ωkkc ′ and ωkk ′ of concordance and discordance that dk attaches to the opinion of the decision maker d kc compared to other members of his vicinity, where c d ∑ ωkk ′ = and ∑ ωkk ′ = 1 . k ′∈V (dk )

k ′∈V (dk )

Based on this definition, we can consider that a decision maker will be as mush important in the community as the other makers give him a good confidence. Thus, the importance degree of d k noted ΘK can be defined with the following equation.

Θk

∑ max ( 0, ω − ω ) = ∑ {∑ max ( 0, ω − ω )} c k ′k

k′

pk

j =1

l

d k ′k

c lj

d lj

(1)

for non-zero importance degree, the Equation (2) can be used.


    1 ∑ k 1 + (exp −α ωc − ωd    k ′k k ′k Θk =    pk  1  ∑ j =1 ∑l 1 + (exp −α ωc − ωd  lj lj 

( (

k /V (k )

µs

))

∑

    

where α is a turning parameter. k V ( d k )

k V (dk )

µr

and

involving the vicinity opinions of decision

maker dk are then defined considering ‘a priori’ bipolar measure and influence of each member of the vicinity V ( d k ) as follows.

Figure 1. Bipolar hierarchical structure of criteria

k ′∈V (dk )

k ′∈V dk

i

(2) The ‘Relative measures’ noted µ s

i

(ω µ (a ) + ω µ (a ))   ∑ ∑ ( ) (ω µ (a ) + ω µ (a ))

))

( (

(a ) = c kk ′

k s0

c kk ′

d kk ′

i

k s0

k r0

i

d kk ′

i

k r0

(3)

i

µrk /V ( dk ) ( ai ) =

∑ ∑ (∑

(ω µ ( a ) + ω µ ( a ) ) ω µ ( a ) + ω µ ( a ))) ( )(

k ′∈V ( j )

i

k ′∈V d k

c kk ′

k r0

c kk ′

d kk ′

i

k r0

i

k s0

d kk '

i

k s0

(4)

i

In relative selectability measure, a decision maker dk considers also ‘a priori’ rejectability measure of vicinity member in relation of their


discordance degrees. Inversely, in relative rejectability measure, decision maker d considers ‘a k priori’ selectability measure of vicinity member in relation of their degree of discordance. The final bipolar measures considering the vicinity influence are then given by Equations (5) and (6).

(

)

k /V (dk )

µsk (ai ) = δ k µsk (ai ) + 1 − δ k µs 0

(a ) i

(5)

µrk ( ai ) = δ k µrk0 ( ai ) + (1 − δ k ) µrk /V ( dk ) ( ai ) (6) where µsk (ai ) / µrk (ai ) represent ‘a priori’ mea0

0

sures of alternative ai . 0 ≤ δ K ≤ 1 , is the individualism degree of decision maker d . When Åδ K tends to 0, the decik sion maker is considered as ‘holistic’ (altruist) and gives more importance to the global opinion represented by his vicinity. When G N tends to 1, the decision maker is ‘individualist’ and considers his opinion as better than the one of his vicinity.

Once the individual responses have been obtained, a process of identifying consensus is proposed using distance evaluation to achieve the satisfactory group solution considering the individual nature and the potential interactions that may influence their choices. The individualism notion is considered to represent the attitude of each individual and its impact on the final result (Bouzarour-Amokrane et al., 2013b). Based on the formalism of the satisficing game theory (Stirling, 2003), each decision maker represents its solutions through a satisficing equilibrium set defined as follows: εqS k = Sqk ∩ εk

(8)

with q k : caution index of the decision maker d k for adjusting his aspiration level; a low value of q k enlarges the satisficing set and inversely, a large value of q k reduces it. εK is an equilibrium set defined by Equation (9) to avoid Pareto dominance that can exist in satisficing set.

{

εk = a ∈ A k (ai ) = φ

}

(9)

with k ( ai ) , the set of alternatives for which there are no strictly better alternatives. The intersection result of satisficing equilibrium sets of decision maker leads to a common legitimate solution. If the intersection is p

non empty

S ,k q

∩ε

≠ ∅ , selection criteria are

k =1

proposed to determine the final solution as follows. 1.

&216(168602'(/

}

{

Sqk = ai ∈ A µsk (ai ) ≥ q k µrk (ai )

2.

Selection of alternative selected by the most important decision maker (considering the importance degree). Selection of alternative with the highest average selectability measure.

 k*   Θ k µ sk ( ai )    a = arg amax s ,k  ∑ i ∈∪ ε q  k   3.

Selection of alternative providing the lowest average rejectability.

  a k * = arg min  Θ µk a  ( ) k r i    ai ∈∪ εqs ,k  ∑  k

(7)

where S qk is the satisficing equilibrium set defined by Equation (8)

4.

Selecting the alternative having the maximum balance between selectability and rejectability for all decision makers


 k*  Θ k µ sk ( ai ) − µ sk ( ai )  a = amax s ,k  ∑ i ∈∪ ε q  k 

(

5.



)   . 

Selecting a Qualified Majority: In some cases, the use of qualified majority is recommended in decision making (e.g. voting in the Council of the European Union), where, only the responses from a subset of decision makers are considered. p

In the case of an empty intersection ∩εqS ,k = ∅ , k =1

a consensus process is needed to find a common legitimate solution. To reach an agreement, a bipolar soft consensus process based on distance evaluation is proposed below. These measures are commonly called ‘proximity measures’ when a comparison of individual assessments is made with respect to the collective opinion in a ‘soft’ consensus process.

6RIW&RQVHQVXV0RGHO Supporting the idea that a final agreement between the decision makers is not necessary to the resolution of group decision problems, this section proposes to reach a common solution through a soft consensus process (HerreraViedma, Herrera, & Chiclana, 2002; Herrera, Herrera-Viedma, & Verdegay, 1996) based on proximity and bipolar measures defined from final bipolar measures. The proposed bipolar approach based on distance analysis has to be distinguished from TOPSIS (Technique For Order Preference By Similarity To Ideal Solution) based also on distance evaluation to find the optimal solution which represents the shortest distance from the positive solution and the farthest distance from the negative solution (J. Park, I. Park, Kwun, & Tan, 2011). Indeed, the presented bipolar approach proposes more flexible method based on identification of satisficing and

non-dominated individual solutions first according to caution index of each individual and reach of a consensus from distance evaluation between individual solutions with regards to convergence notions. Moreover, the proposed approach is different from TOPSIS approach which is characterized by cardinal attributes where preferences are set in advance and the best action can be selected among the poorest if all actions are bad. The proximity measures are used to assess the gap between decision makers’ evaluation regarding an alternative ai while bipolar consensus measures evaluate the difference between decision maker dk and the rest of the group regarding bipolar measures of alternative ai . Using proximity and bipolar consensus measures, the feedback process can be divided into two phases. In the identification phase, the proximity and consensus measures are used to identify alternatives with wide divergence and, decision makers showing a significant gap assessment on bipolar measures against other members. A recommendation phase is then used to send instructions to decision makers with significant differences. Unlike soft consensus process proposed in the literature such as those presented by HerreraViedma et al. (2007) or Khorshid, (2010), the proposed model does not aim to converge decision maker assessments on all alternatives, but focuses on alternative with the same trend (convergent) initially. Targeted recommendations are given to decision makers with inconsistent opinions considering the rest of the group. This leads to a common solution. An iterative process is engaged with several consultation phases until an agreement is reached. In each iteration, the proximity measures and bipolar consensus are calculated to determine alternatives with strong disagreement (using proximity measures) and identify decision makers with a large divergence (using measurements of bipolar consensus).


The main problem in this situation is how to determine the convergence of individual preferences (Tapia-GarcíA, Del Moral, MartíNez, & HerreraViedma, 2012). To achieve this convergence, boundary conditions (tolerance threshold) are determined by the moderator and / or decision makers for each level. Alternatives and decision makers with convergent assessments are identified and targeted adjustments are recommended. This process is governed by a feedback mechanism to guide decision makers in changing their opinions. The parameters of the proposed soft consensus are developed below.

3UR[LPLW\0HDVXUH A proximity measure (alternative) is a measure used to calculate the average distance between decision makers evaluations over alternativeai . It is obtained by Equation (10).

disi =

∑∑ k

 kk ′ d k ′,k ′≠k  si  p     2   

2

1 2 2

kk ′ ri

(10)

,

the binomial coefficient taking into account the distance combinations avoiding redundancies (for example: d s12i d s21i ).

%LSRODU&RQVHQVXV0HDVXUH Bipolar consensus measures allow calculating the distance between a decision makerdk and the rest of the group over final bipolar measures of alternative ai . Bipolar measurements consensus are given by Equations (11) and (12).

k si

d

d



( ) + (d ) 

P  P!    2  =   2 ! (P − 2) !

k si

∑ =

∑ =

k ′,k ′≠k

(d ) kk ′ si

p −1

k ′ , k ′≠ k

(d ) kk ′ si

p −1

(11)

(12)

where dsk , d rki represent respectively, supporti

with disi is the proximity measure of alternative

ability and rejectability consensus measures. These measurements are then integrated into a feedback mechanism defined below.

)HHGEDFN0HFKDQLVP

ai ,

d skki ′ ( ai ) = Θ k µ sk ( ai ) − Θ k ′ µ sk ′ ( ai )

,

the gap between selectabilty measure of dk and

d kc over evaluation of alternativeai considering the importance degree of each decision maker. drkk (ai ) = Θk µrk (ai ) − Θk ′ µrk (ai ) , ′

′

i

A feedback mechanism allows decision makers to change their preferences in order to achieve a tolerated degree of proximity. Literature generally represents the feedback process by an identification phase followed by a recommendation one. According to this structure, the proposed feedback process associated with the bipolar approach is given as follows.

the gap between rejectability measure of d k and dk ′ over evaluation of alternative ai considering the importance degree of each decision maker.


,GHQWLILFDWLRQ3KDVH

For dsk > ωs : decision maker d k presents a

•

i

The identification phase is used to evaluate proximity degree of individual evaluations of each alternative, and compare them to tolerance threshold. Alternatives with high variation (the proximity measurements exceeding tolerance threshold) are discarded. Only converging alternatives are subsequently processed. To identify the contribution of each actor, the distance between the bipolar assessments is calculated using bipolar consensus measures to identify decision makers whose evaluations are far of the group. From these measurements, evaluations of alternatives can be modified using the following steps:

significant difference related to its selectability measure compared to selectability measures of the rest of the group. To know the divergence direction and identify if the considered alternative has an important selectability (positive divergence) or a very low selectability (negative divergence), Equation (13) is used.

k si

div

∑ =

k ′,k ′≠k

(d ) kk ′ si

p −1

(13)

if divski ! , alternative ai presents a good selectability measure,k no change is required. divs otherwise ( ), the selectability measure is smaller than average, an increase of considered measure is recommended. Similar recommendations are applied for rejectability measures: i

1.

2.

Identification of alternatives whose proximity measure disi meet the condition (1) di ≤ ω where ω is the tolerance threshold for alternatives (average distances on the alternatives set can be considered as threshold tolerance). This allows excluding alternatives that may create conflicts and focus on alternatives already having a certain convergence. Identifying decision makers with divergent opinions through the non-fulfilment of the following conditions: (2) dsk ≤ ωs , (3)

For drk > ωr : decision maker d k presents a

•

i

significant difference related to its rejectability measure compared to the rejectability measures of the rest of the group. The divergence direction indicates whether the alternative has a low rejectability (positive divergence) or a significant rejectability (negative divergence). The divergence direction of rejectability measure is given by Equation (14)

d ≤ ωr , where ωS and ZU are respectively i

k ri

selectability and rejectability tolerance threshold.

5HFRPPHQGDWLRQ3KDVH div •

In this recommendation phase, a discussion session allows to give targeted recommendations for divergent decision makers (i.e. those that do not fulfill conditions (2) and (3)) considering selected alternatives (that meet the condition (1)). The recommendations are based on the following rules:

k ri

∑ =

k ′,k ′≠k

(d ) kk ′ ri

p −1

(14)

if divrk > , alternative ai presents a low rejecti

ability measure, no change is required. otherwise (divrk > ), the rejectability meai

sure is bigger than average, a reduction of this measure is recommended.


Once the changes are done, the satisficing equilibrium set of each decision maker is rebuilt. p

The iterative process is stopped when

∩ε k =

Sk q

≠ ∅.

If the solution satisfies the group, the process is stopped. Otherwise, a new iteration can be proposed. The next section illustrates on a case study the developments of this chapter.

&$6(678'< Deciding the key location to build a wind farm is an essential element in order to obtain optimum energy production at a cheapest cost. A strategic location is crucial to reduce costs associated with turbine foundations, access roads and construction areas. The location selection will also enable the authorities to predict the environmental impact on the wind farm surroundings. The real world problem we have been dealing with in this section was treated initially by Lee et al. (2009) to select the most appropriate site to settle a wind farm with sustainable objectives to satisfy. The problem was reformulated to fit the developed framework with a highlight on sustainability as the main goal to reach. Three objectives have been considered: performance objective, Operational objective (business drivers), and socio-economic objectives. Performance concerns the capabilities of the conversion system for delivering the results, such as availability and efficiency, in variant processing environments. Business drivers are defined as the expectations of participants about the wind farm, such as potential, challenge, and opportunities. Socio-economic needs consider the aptitude of the envisaged solution to be competitive with respect to the other challengers (Lee et al., 2009). Several criteria can be considered to limit the variability and disadvantages associated with the installation of a wind farm. For example, wind availability criterion takes into account the strength of the wind which can vary from zero to storm level.

The discussed method is used to evaluate the priority of criteria in selecting a location for wind farm considering bipolar environment based on BOCR analysis. The repartition of criteria on each objective in the considered context is summarized in Table 1. Considering that a decisional committee is composed of three entities: the wind specialists, local elected officials and public authorities respectively notedD1, D2, D3 . The importance degree given by each decision maker, for Perfor mance objective, Operational objective and Socio-economic objectives are respectively ω1,2 = [ 0.1 0.8 0.1] , ω2,/ = 0.8 0.1 0.1 , ω 3,/ = 0.1 0.1 0.8 It is assumed that production department ( G ) will give more importance to the performance objective, their first goal being to establish a production site with a good yield. To manage the budget and preserve the communal heritage, elected officials (D ) will promote socio-economics aspects and public authorities ( G ) will pay more attention to the operability of the future site. The AHP method associated to BOCR analysis is used to evaluate alternatives over benefit, opportunity, cost and risk factors through the proposed repartition of criteria. For example, the initial results of criteria evaluation given byD are summarized in Table 2. The aggregating results of criteria evaluation considering supporting and rejecting categories are represented in Table 3 by the ‘a priori’ bipolar measures for each decision maker. The social link and the potential influences between decision makers are modelled through concordance and discordance measures noted respectively ωkkc ′ ωkkd ′ and represented by the following matrices.

ωkkc ′

 − 0. 1 0. 9   − 0. 7 0 . 3      = 0.7 − 0.3 ωkkd ′ = 0.2 − 0.8      0.2 0.8 −  0.6 0.4 − 


Table 1. Repartition of criteria and sub-criteria with regards to objectives of wind farm project Objective Performance objective

Factors Benefit

Criteria Wind availability

Sub-Criteria Geographical distribution of wind speed frequency Mean wind power density Annual mean wind speed

Site advantage

Influence of selected height of installation Effect of wind gusting Micro-siting of WEGs

Opportunity

Advanced technologies

Computerized supervisory Variable speed wind power generation Swept area of a turbine rotor

Cost

Wind turbine

Design and development Manufacturing Installation, maintenance

Socio-economic objective

Opportunity

Financial schemes

Switchable tariff Discount of tax rate and duty rate Other investment and production incentives

Policy support

Wind power concession program Clean development mechanisms program Other policy supports

Cost

Connexion

Electric connection Grid connection

Foundation

Main construction Peripheral construction

Operational Objective

Benefit

WEG functions

Real and technical availability Affordable, reliable, and maintenance free Power factor, capacity factor

Risk

Human Technique

Technical complexity and difficulties

Pertinence

Loyalty or lease agreement, geology suitability, etc.

These matrices show for example that decision maker G gives good confidence to decision maker D through a good degree of concordance and a low degree of discordance. Considering the concordance and discordance given above, the importance degree ΘK of each decision maker d k is deduced from Equation (2) and given by the

Conflicts Entrepreneurs, policy makers, residents

following vector: ΘK = 0.3334 0.3285 0.3381 . The relative measures taking into account the vicinity are obtained using Equations (3) and (4) while bipolar final measures result from Equations (5) and (6). Considering that the individualism degree N is medium ( δ = [ 0.5 0.5 0.5] ), the results are respectively presented in Table 4 and Table 5.


Table 2. Evaluation results of criteria given by decision maker G Objective Performance objective

Factors Benefit

Criteria Wind availability

Site advantage

Opportunity

Cost

Socio-economic objective

Opportunity

Advanced technologies

Wind turbine

Financial schemes

Policy support

Cost

Connexion

Foundation

Sub-Criteria

a1

a2

a3

a4

a5

Geographical distribution of wind speed frequency

0,1853

0,2265

0,1235

0,2147

0,2500

Mean wind power density

0,1690

0,2184

0,1632

0,2431

0,2063

Annual mean wind speed

0,1976

0,2298

0,1774

0,2137

0,1815

Influence of selected height of installation

0,2196

0,2016

0,1576

0,1990

0,2222

Effect of wind gusting

0,1698

0,1995

0,2237

0,2022

0,2049

Micro-siting of WEGs

0,1849

0,2165

0,1825

0,2141

0,2019

Computerized supervisory

0,2117

0,1964

0,1862

0,1990

0,2066

Variable speed wind power generation

0,2100

0,1900

0,2200

0,1875

0,1925

Swept area of a turbine rotor

0,2005

0,2112

0,1952

0,1952

0,1979

Static reactive power compensator

0,1990

0,1914

0,2040

0,1990

0,2065

Design and development

0,1854

0,1987

0,1987

0,2053

0,2119

Manufacturing

0,1848

0,2065

0,1957

0,1957

0,2174

Installation, maintenance

0,1842

0,1974

0,2039

0,2039

0,2105

Switchable tariff

0,2000

0,2000

0,1902

0,2024

0,2073

Discount of tax rate and duty rate

0,2101

0,1957

0,1836

0,2029

0,2077

Other investment and production incentives

0,2174

0,1932

0,1763

0,2005

0,2126

Wind power concession program

0,1718

0,2154

0,1872

0,2077

0,2179

Clean development mechanisms program

0,1963

0,2120

0,1780

0,2042

0,2094

Other policy supports

0,1839

0,2217

0,1763

0,2141

0,2040

Electric connection

0,1296

0,2222

0,1111

0,2407

0,2963

Grid connection

0,1569

0,2157

0,0980

0,1961

0,3333

Main construction

0,1351

0,1892

0,1892

0,2162

0,2703

Peripheral construction

0,1290

0,1935

0,1613

0,1935

0,3226

continued on following page


Table 2. Continued Objective

Factors

Operational Objective

Benefit

Risk

Criteria

a1

a2

a3

a4

a5

Real and technical availability

0,1740

0,2099

0,1961

0,2044

0,2155

Affordable, reliable, and maintenance free

0,2008

0,1988

0,2008

0,1988

0,2008

Power factor, capacity factor

0,1889

0,2111

0,1852

0,2185

0,1963

Human

Conflicts Entrepreneurs, policy makers, residents

0,2053

0,1947

0,2105

0,2000

0,1895

Technique

Technical complexity and difficulties

0,2078

0,1939

0,2078

0,1967

0,1939

Pertinence

Loyalty or lease agreement, geology suitability, etc.

0,2069

0,1936

0,2202

0,1989

0,1804

WEG functions

Sub-Criteria

ε1S ,1 = {a5 , a2 , a4 } , ε1S ,2 = {a 3 }

The graphical representation of the evaluation results (Table 4) in the plane ( µr , µs ) is given in

a n d

ε1S ,3 = {a1 , a4 } . In this case there is no common

k

Figure 2. The index of caution q is assumed to be 1 for each decision maker dk . The satisficing

 3 solution ∩ε1S ,k = ∅ . We propose then to use  k =1 the proposed soft consensus procedure.

( )

equilibrium sets εqS k of actors are as follows:

Table 3. ‘A priori’ bipolar measures for each decision maker Decision Makers

D

Bipolar Measures Selectability measure ( P V )

a1

a2

a3

a4

a5

0.1943

0.2078

0.1835

0.2089

0.2055

0.2043

0.1889

0.2088

0.2226

0.1753

0.1944

0.1940

0.2122

0.1842

0.2152

0.1809

0.2108

0.1776

0.2055

0.2252

0.2090

0.1647

0.1978

0.2221

0.2064

0.2025

0.1917

0.2048

0.1933

0.2076

1

0

1

Rejectability measure ( µR ) 0

G

2

Selectability measure ( µS ) 0

Rejectability measure ( P U ) 2

0

D

Selectability measure ( P V ) 3

0

3

Rejectability measure ( µR ) 0


Table 4. Relative bipolar measures Decision Makers

D

D

Relative Bipolar Measures

a1

a2

a3

a4

a5

µs

0.1976

0.1861

0.1926

0.2102

0.2135

µr1/V ( d1 )

0.1996

0.1893

0.2050

0.1951

0.2109

0.2008

0.1929

0.1968

0.2060

0.2035

0.2049

0.1815

0.2013

0.2166

0.1957

µs

0.1947

0.1972

0.2014

0.2026

0.2041

µr3/V ( d3 )

0.1900

0.2043

0.1894

0.2040

0.2122

1/V (d1 )

µ s2/V ( d2 ) 2/V (d2 )

µr

G

3/V (d3 )

Using a feedback mechanism, decision makers have the ability to change their assessments based on recommendations made by the analyst during the discussion sessions, with the aim to converge to a common solution.

decision makers with assessments that deviate from those of other decision makers. Using Equation (10), the proximity measure is given by

di = [ 0.0077 0.0079 0.0119 0.0113 0.0090]. •

Identification Phase: The first phase of the feedback mechanism uses proximity measures and bipolar consensus to identify respectively, divergent alternatives and

Assuming that the average distances on the set of alternatives is the tolerance threshold, the proximity distance must not exceed 0.0096

Figure 2. Graphic representation of final bipolar measures for each decision maker


Table 5. Final bipolar measures Decision Makers

Final Bipolar Measures

a1

a2

a3

a4

a5

0.1960

0.1969

0.1881

0.2095

0.2095

0.2020

0.1891

0.2069

0.2089

0.1931

0.1976

0.1935

0.2045

0.1951

0.2093

0.1929

0.1961

0.1894

0.2111

0.2105

0.2019

0.1809

0.1996

0.2123

0.2053

0.1963

0.1980

0.1971

0.1987

0.2099

D

Selectability measure ( µS )

Rejectability measure ( P U )

Selectability measure ( P V )

D

Rejectability measure ( µR )

G

Selectability measure ( µS )

Rejectability measure ( P U )

Table 6. Bipolar consensus measures

d rki

d sk i

D

d1

d2

d3

d1

d2

d3

a1

0,0017

0,0019

0,0031

0,0025

0,0035

0,002

a2

0,0033

0,0022

0,0034

0,0026

0,002

0,0032

a3

0,0046

0,0024

0,0026

0,0045

0,0056

0,0034

a4

0,0039

0,0067

0,0048

0,0014

0,0012

0,0023

a5

0,0007

0,0009

0,0005

0,0057

0,0033

0,0042

(d

i

≤ 0.0096) . Consequently, alternative a3 and

a4 (having widely divergence) should be discarded. Assuming that thresholds Zs Zr were obtained from averages of bipolar distances on the set of alternatives, Table 6 shows the gaps observed at the actor level for each alternative. • Recommendation Phase: The second phase allows the analyst to make targeted recommendations to divergent decision makers: Table 6 shows that: ƕ Decision maker D presents a deviation from the average concerning the

selectability measure of alternative D . The direction of the divergence is positive (divS1 = 0.003 ), the select2

ƕ

ability measure is important and cannot be modified. Decision maker G presents a divergence regarding the rejectability measures of alternatives a1 and a5. The divergence direction of the rejectability measures is given by divr2 = 0.0035 1

and divr25 = −0.0015 . The negative


Figure 3. Graphic representation of final bipolar measures (iteration1)

divergence of alternative a5 leads to the recommendation of reducing the rejectability measure. Alternative a1 which has a low rejectability is spared. Decision maker D presents a strong rejectability measure on alternative a5 compared to the rest of the group. The negative divergence direction

ƕ

(div

3 r5

)

= −0.0042 implies a recom-

mendation of reducing this measure. The reduction of rejectability measures of alternatives a5 by decision makers d2 and d3 to µr2 ( a5 ) = 0.2005 and µr3 (a 5 ) = 0.1980 respectively leads to the following graphical representation (see Figure 3). The satisficing equilibrium sets ε qS k of each decision makers dk are deduced

(

)

intersection of the sets is the alternative  3 a5 ∩ε1S ,k = a 5  .  k =1 In the example discussed here, the integration of positive and negative influences of decision makers in the model and the relatively small number of decision makers allowed reaching a consensus quickly after a single recommendation step. The individualism average rate considered for all decision makers allows also to nuance the individual assessments and reduce differences that a high degree of individualism could make appear as shown in figure 4 for individualism degrees G N equal to 0.9 for each decision maker. A sensitivity analysis can be performed by varying the caution index and/or individualism degree to test different possible scenarios and stability of recommended solutions.

as follows: ε1S ,1 = {a 5, a2, a 4 } , ε1S ,2 = {a3 , a5 } and ε1S ,3 = {a1, a 4 , a 5 } . The solution obtained by the


Figure 4. Graphic representation of final bipolar measures for δ K = 0.9 0.9 0.9

&21&/86,21 The renewable energies now is a topic of great interest for many countries. This growth is due to the depletion of perishable fossil fuels and continuous industrial development. This leads companies to greater efforts to achieve competitive energetic capacity while meeting the new environmental and ecological constraints required by the authorities. In addition, recent environmental disasters (Fukushima nuclear plant disaster, oil spill caused by the sinking of the tanker Erika, etc.) push countries to question their fossil energy sources. Wind energy has relatively safe and positive characteristics that explain the rapid growth of its exploitation in recent decades. However, in some cases, the wind farms installation is done without detailed post-evaluation study which can cause huge losses. In this chapter, the selection of suitable wind farm problem was considered in bipolar multicriteria context. The review of used indicators on post-evaluation study on wind farm planning was first introduced. Since wind farm selection problem requires the involvement of multiple decision makers, the second section presented the

proposed bipolar evaluation model whose positive and negative criteria are considered distinctly to represent potential wind farms, for each decision maker, by selectability (positive criteria) and rejectability (negative criteria) measures. Considering group decision interactions, relative bipolar measures were defined through concordance and discordance degrees which represent respectively a positive and negative influential vicinity impact on final decision of each decision maker. Based on the satisficing game theory, a final choice of each actor was represented with a satisficing equilibrium set. If the individual solutions do not converge, a soft consensus process was proposed to lead decision makers towards a common solution based on distance evaluation. A real case problem was considered as an application of the proposed methodology. To limit the variability and disadvantages associated with the installation of a wind farm more criteria have to be considered related to its environmental and social impacts such as visual and noise pollution, the pollution level caused by the wind farm construction, the wind farm capacity considering a real need, etc., must be assessed and integrated in the evaluation process. The proposed model can be adjusted for


other forms of sustainable energy selection process such as hydroelectricity, solar energy, geothermal energy, etc. This approach can also be used in the selection of used materials in the renewable energy exploitation such as the turbine selection, solar panels selection, etc.

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