Multiple criteria decision making for sustainable energy and transportation systems proceedings of the 19th international conf

In the twenty-first century the world has entered an age of exponentiallyincreasing demand for energy and transportation services in a globalised economy.The evidence for climate change

Founding Editors:

Lecture Notes in Economics

and Mathematical Systems

Decision Making, Auckland,

Theodor J Stewart • Jyrki Wallenius

New Zealand, 7th - 12th January 2008 Matthias Ehrgott • Boris Naujoks

Editors

This work is subject to copyright All rights are reserved, whether the whole or part of the material is concerned, specifically the rights of translation, reprinting, reuse of illustrations, recitation, broadcasting, reproduction on microfilm or in any other way, and storage in data banks Duplication of this publication

or parts thereof is permitted only under the provisions of the German Copyright Law of September 9,

1965, in its current version, and permission for use must always be obtained from Springer Violations are liable to prosecution under the German Copyright Law.

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Printed on acid-free paper

Springer is part of Springer Science+Business Media (www.springer.com)

 Springer-Verlag Berlin Heidelberg 2010

Springer Heidelberg Dordrecht London New York

ISSN 0075-8442

Ass Prof Dr Matthias Ehrgott

The University of Auckland

Department of Engineering Science

Level 3, 70, Symonds Street

Auckland 1010

New Zealand

m.ehrgott@auckland.ac.nz

Boris NaujoksLogin GmbHWilhelmstraße 45

58332 SchwelmGermanyboris.naujoks@login-online.de

Professor Theodor J Stewart

University of Cape Town

Department of Statistical Sciences

00100 HelsinkiFinlandjyrki.wallenius@hse.fi

ISBN 978-3-642-04044-3 e-ISBN 978-3-642-04045-0

DOI 10.1007/978-3-642-04045-0

Library of Congress Control Number: 2009933604

In the twenty-first century the world has entered an age of exponentiallyincreasing demand for energy and transportation services in a globalised economy.The evidence for climate change as a consequence of human activity and a growingrealization of limited resources has put the sustainability of energy and transporta-tion systems on the top of the political agenda in many countries around the world.Economic and technological growth as well as the development of infrastructuremust consider the sustainability of such activity for the future and governments areestablishing policies towards a sustainable, low emissions energy future.

The environmental impacts of human economic activity necessitate the eration of conflicting goals in decision-making processes to develop sustainablesystems Any sustainable development has to reconcile conflicting economic andenvironmental objectives and criteria The science of Multiple Criteria Decision-Making (MCDM) has a lot to offer in addressing this need Decision-making withmultiple (conflicting) criteria is the topic of research that is at the heart of theInternational Society of Multiple Criteria Decision-Making To provide a forumfor the discussion of current research the Society organised the 19th InternationalConference under the theme “MCDM for Sustainable Energy and TransportationSystems”

consid-This book is based on selected papers presented at the conference, held atThe University of Auckland, New Zealand, from 7th to 12th January, 2008 Theconference was attended by 137 people from 39 countries on six continents

125 papers were presented in 39 scientific sessions, including two plenaryaddresses by Prof Anna Nagurney, University of Massachusetts, on “Multicrite-ria Decision-Making for the Environment: Sustainability and Vulnerability Analysis

of Critical Infrastructure Systems from Transportation Networks to Electric PowerSupply Chains” and Prof Jim Petrie, University of Sydney and University of CapeTown, on “Multi Criteria Decision-Making within Energy Networks for ElectricityProduction in Emerging Markets”

The International Society on Multiple Criteria Decision-Making awards prizes tooutstanding researchers in the field The winners in 2008 were:

 MCDM Gold Medal: Prof Theodor J Stewart, University of Cape Town

 Edgeworth-Pareto Award: Prof Kalyanmoy Deb, Indian Institute of TechnologyKanpur

 Georg Cantor Award: Prof Valerie Belton, University of Strathclyde.

v

1 Multiple Criteria Decision-Making, Transportation, Energy Systems, and theEnvironment

2 Applications of Multiple Criteria Decision-Making in Other Areas

3 Theory and Methodology of Multiple Criteria Decision-Making

4 Multiple Objective Optimization

Part I contains ten papers applying MCDM methods to problems in energy andtransportation systems and environmental contexts The applications range from cityelectric transport to natural resource management, railway transport, and environ-mental synergies in supply chain integration An even wider variety of applications

is covered in the ten papers in Part II Many different MCDM methods are applied

in risk assessment, banking, manpower planning, wirelesses sensor networks, andothers Parts III and IV have a theoretical and methodological focus The five papers

in part III address the analytic hierarchy process, a bibliometric analysis of MCDMand multiattribute utility theory, conjoint measurement, model predictive control,

and classification Part IV includes seven papers on multiple objective optimization.These papers present a variety of algorithms for discrete and continuous multiob-jective optimization problems, including five of the eight papers presented in thespecial track on evolutionary multiple objective optimization of the conference.

Acknowledgements As editors, we wish to thank all the people who made the conference and

this book possible First of all, our thanks go to the local organizing committee of Matthias Ehrgott (chair), Ivan Kojadinovic, Richard Lusby, Michael OSullivan, Andrea Raith, Paul Rouse, Lizhen Shao, Cameron Walker, Judith Wang, Hamish Waterer, and Oliver Weide Secondly, we acknowl- edge the contributions of the Executive Committee of the International Society on Multiple Criteria Decision-Making.

The book, of course depends on the hard work of the authors who have submitted papers and the referees whose dedication in reviewing papers ensure the quality of this book We wish to thank the following individuals who acted as referees:

Lauren Basson, Nicola Beume, Bogusław Bieda, Antonio Boggia, Henri Bonnel, Claude Bouvy, Dimo Brockhoff, G¨ulc¸in B¨uy¨uk¨ozkan, Herminia I Calvete, Metin Celik, Eng Choo, Carlos

A Coello Coello, Kalyanmoy Deb, Xavier Delorme, Hepu Deng, Liz Dooley, Ian Noel Durbach, Matthias Ehrgott, Michael T.M Emmerich, Jos´e Luis Esteves dos Santos, L Paul Fatti, Carlos

M Fonseca, Eugenia Furems, Lucie Galand, Xavier Gandibleux, Martin Josef Geiger, Evangelos Grigoroudis, Evan J Hughes, Masahiro Inuiguchi, Alessio Ishizaka, Rafikul Islam, Yaochu Jin, Dylan F Jones, Julien Jorge, Alison Joubert, Birsen Karpak, Joshua D Knowles, Ivan Kojadinovic, Murat K¨oksalan, Juha Koski, Elizabeth Lai, Riikka Leskel¨a, Anatoly Levchenkov, Chieh-Yow ChiangLin, Richard Lusby, Oswald Marinoni, Benedetto Matarazzo, J¨orn Mehnen, Kristo Mela, Gilberto Montibeller, Jos´e Mar´ıa Moreno-Jim´enez, Sanaz Mostaghim, Anna Nagurney, Boris Naujoks, Shigeru Obayashi, Tatsuya Okabe, Lu´ıs Paquete, Long Pham, Carlo Poloni, Mike Preuß, Domenico Quagliarella, Andrea Raith, Piet Rietveld, G¨unter Rudolph, Thomas L Saaty, Ahti Salo, Ramiro Sanchez-Lopez, Robert Scheffermann, Thomas Schlechte, Anita Sch¨obel, Yong Shi, Theodor J Stewart, Christian Stummer, Jacques Teghem, Jeffrey Teich, J´ozsef Temesi, Heike Trautmann, Luis G Vargas, Bego˜na Vitoriano, Raimo Voutilainen, Tobias Wagner, Jyrki Wallenius, William C Wedley, Heinz Roland Weistroffer, John F Wellinton, Fred Wenstop, Lyn- don While, Marino Widmer, Diederik Wijnmalen, Jan-Bo Yang, Ming-Miin Yu, Yeboon Yun, Mahdi Zarghami, Wim Zeiler, Eckart Zitzler, Constantin Zopounidis.

June 2009

Part I Multiple Criteria Decision Making, Transportation,

Energy Systems, and the Environment

On the Potential of Multi-objective Optimization in the Design

of Sustainable Energy Systems 3Claude Bouvy, Christoph Kausch, Mike Preuss, and Frank Henrich

Evaluation of the Significant Renewable Energy Resources

in India Using Analytical Hierarchy Process 13

Joseph Daniel, Nandigana V R Vishal, Bensely Albert,

and Iniyan Selvarsan

Multiple Criteria Decision Support for Heating Systems

in Electric Transport 27

Ivars Beinarts and Anatoly Levchenkov

Multi Criteria Decision Support for Conceptual Integral

Design of Flex(eble)(en)ergy Infrastructure 35

Wim Zeiler, Perica Savanovic, Rinus van Houten, and Gert Boxem

A Multi Criteria Knapsack Solution to Optimise Natural

Resource Management Project Selection 47

Oswald Marinoni, Andrew Higgins, and Stefan Hajkowicz

Environmental and Cost Synergy in Supply Chain Network

Integration in Mergers and Acquisitions 57

Anna Nagurney and Trisha Woolley

The Analytic Hierarchy Process in the Transportation Sector 79

Rafikul Islam and Thomas L Saaty

RECIFE: A MCDSS for Railway Capacity Evaluation 93

Xavier Gandibleux, Pierre Riteau, and Xavier Delorme

ix

Balancing Efficiency and Robustness – A Bi-criteria

Optimization Approach to Railway Track Allocation 105

Thomas Schlechte and Ralf Bornd¨orfer

Tolling Analysis with Bi-objective Traffic Assignment 117

Judith Y.T Wang, Andrea Raith, and Matthias Ehrgott

Part II Applications of Multiple Criteria Decison Making

in Other Areas

National Risk Assessment in The Netherlands .133

Erik Pruyt and Diederik Wijnmalen

Evaluation of Green Suppliers Considering

Decision Criteria Dependencies 145

Orhan Feyzio˜glu and G¨ulc¸in B¨uy¨uk¨ozkan

A Multiobjective Bilevel Program for Production-Distribution

Planning in a Supply Chain 155

Herminia I Calvete and Carmen Gal´e

An Ordinal Regression Method for Multicriteria Analysis

of Customer Satisfaction 167

Isabel M Jo˜ao, Carlos A Bana e Costa, and Jos´e Rui Figueira

Discrete Time-Cost Tradeoff with a Novel Hybrid

Meta-Heuristic .177

Kamal Srivastava, Sanjay Srivastava, Bhupendra K Pathak,

and Kalyanmoy Deb

Goal Programming Models and DSS for Manpower Planning

of Airport Baggage Service 189

Sydney C.K Chu, Minyue Zhu, and Liang Zhu

A MCDM Tool to Evaluate Government Websites

in a Fuzzy Environment 201

G¨ulc¸in B¨uy¨uk¨ozkan

Investigating Coverage and Connectivity Trade-offs

in Wireless Sensor Networks: The Benefits of MOEAs 211

Matthias Woehrle, Dimo Brockhoff, Tim Hohm, and Stefan Bleuler

AHP as an Early Warning System:

An Application in Commercial Banks in Turkey 223

Ilyas Akhisar and Birsen Karpak

Contents xi

A Multi-Criteria Evaluation of Factors Affecting Internet

Banking in Turkey 235

Sezi Cevik Onar, Emel Aktas, and Y Ilker Topcu

Part III Theory and Methodology of Multiple Criteria

Decision Making

Priority Elicitation in the AHP by a Pareto Envelope-Based

Selection Algorithm 249

Ludmil Mikhailov and Joshua Knowles

Bibliometric Analysis of Multiple Criteria Decision

Making/Multiattribute Utility Theory 259

Johanna Bragge, Pekka Korhonen, Hannele Wallenius,

and Jyrki Wallenius

Ordinal Qualitative Scales 269

Salvatore Greco, Benedetto Matarazzo, and Roman Słowi´nski

Multi-objective Model Predictive Control .277

Hirotaka Nakayama, Yeboon Yun, and Masakazu Shirakawa

Multiple Criteria Nonlinear Programming Classification with

the Non-additive Measure 289

Nian Yan, Yong Shi, and Zhengxin Chen

Part IV Multiple Objective Optimization

A Reduced-Cost SMS-EMOA Using Kriging, Self-Adaptation,

and Parallelization 301

Jan-Willem Klinkenberg, Michael T M Emmerich, Andr´e H

Deutz, Ofer M Shir, and Thomas B¨ack

Faster Hypervolume-Based Search Using Monte Carlo

Sampling 313

Johannes Bader, Kalyanmoy Deb, and Eckart Zitzler

Using a Gradient Based Method to Seed an EMO Algorithm 327

Alfredo G Hernandez-Diaz, Carlos A Coello, Fatima Perez,

Rafael Caballero, and Julian Molina

Nadir Point Estimation Using Evolutionary Approaches:

Better Accuracy and Computational Speed Through

Focused Search 339

Kalyanmoy Deb and Kaisa Miettinen

A Branch and Bound Algorithm for Choquet Optimization

in Multicriteria Problems 355

Lucie Galand, Patrice Perny, and Olivier Spanjaard

Decision Space Diversity Can Be Essential for Solving

Multiobjective Real-World Problems 367

Mike Preuss, Christoph Kausch, Claude Bouvy, and Frank Henrich

Computing and Selecting "-Efficient Solutions

of f0,1g-Knapsack Problems 379

Emilia Tantar, Oliver Sch¨utze, Jos´e Rui Figueira, Carlos A Coello

Coello, and El-Ghazali Talbi

Ilyas Akhisar School of Banking and Insurance, Marmara University, Istanbul,

Turkey, akhisar@marmara.edu.tr

Emel Aktas Istanbul Technical University, Management Faculty, Macka 34367,

Istanbul, Turkey, aktasem@itu.edu.tr

Bensely Albert Department of Mechanical Engineering, College of Engineering,

Guindy, Anna University, Chennai 600025, India, benzlee5@yahoo.com

Johannes Bader Computer Engineering and Networks Lab, ETH Zurich, 8092

Zurich, Switzerland, bader@tik.ee.ethz.ch

Thomas B¨ack Leiden Institute for Advanced Computer Science (LIACS), Leiden

University, Niels Bohrweg 1, 2333-CA Leiden, The Netherlands, baeck@liacs.nl

Carlos A Bana e Costa Centre for Management Studies of Instituto Superior

T´ecnico, Technical University of Lisbon, Av Rovisco Pais, 1049-001 Lisbon,Portugal, carlosbana@ist.utl.pt

Ivars Beinarts Riga Technical University, Kronvalda blvd 1-202, Riga, Latvia,

ivars.beinarts@latnet.lv

Stefan Bleuler Computer Engineering and Networks Lab, ETH Zurich, 8092

Zurich, Switzerland, stefan.bleuler@tik.ee.ethz.ch

Ralf Bornd¨orfer Konrad-Zuse-Zentrum f¨ur Informationstechnik Berlin (ZIB),

Takustr 7, Berlin-Dahlem 14195, Germany, borndoerfer@zib.de

Claude Bouvy Forschungsgesellschaft Kraftfahrwesen mbH Aachen,

Steinbachstraß e7, 52074 Aachen, Germany, bouvy@fka.de

Gert Boxem Faculty of Architecture, Building and Planning, Technische

Universiteit Eindhoven, The Netherlands, g.boxem@bwk.tue.nl

Johanna Bragge Helsinki School of Economics, Department of Business

Technology, P.O Box 1210, Helsinki 00101, Finland, johanna.bragge@hse.fi

Dimo Brockhoff Computer Engineering and Networks Lab, ETH Zurich, 8092

Zurich, Switzerland, dimo.brockhoff@tik.ee.ethz.ch

xiii

G ¨ulc¸in B ¨uy ¨uk¨ozkan Department of Industrial Engineering, Galatasaray

University, C¸ ıra˘gan Caddesi No 36 Ortak¨oy, ˙Istanbul, Turkey,

gbuyukozkan@gsu.edu.tr

Rafael Caballero Department of Applied Economics (Mathematics), University

of Malaga, Malaga, Spain, r caballero@uma.es

Herminia I Calvete Dpto de M´etodos Estad´ısticos, IUMA, Universidad

de Zaragoza, Pedro Cerbuna 12, Zaragoza 50009, Spain, herminia@unizar.es

Sezi Cevik Onar Istanbul Technical University, Management Faculty, Macka,

Istanbul 34367, Turkey, cevikse@itu.edu.tr

Zhengxin Chen College of Information Science and Technology, University

of Nebraska, Omaha, NE 68182, USA, zchen@mail.unomaha.edu

Sydney C.K Chu Department of Mathematics, University of Hong Kong,

Pokfulam Road, Hong Kong, China, schu@hku.hk

Carlos A Coello Coello Centro de Investigacion y de Estudios Avanzados,

CINVESTAVIPN, Department of Computer Science, M´exico D.F., Mexico,ccoello@cs.cinvestav.mx

Joseph Daniel Department of Mechanical Engineering, College of Engineering,

Anna University, Guindy, Chennai 600025, India, joesneha@gmail.com

Kalyanmoy Deb Department of Mechanical Engineering, Indian Institute

of Technology, Kanpur 208016, India, deb@iitk.ac.in

Xavier Delorme Centre G´enie Industriel et Informatique, Ecole des Mines

de Saint-Etienne, 158 cours Fauriel, F-42023 Saint-Etienne cedex 2, France,Delorme@emse.fr

Andr´e H Deutz Leiden Institute for Advanced Computer Science (LIACS),

Leiden University, Niels Bohrweg 1, Leiden 2333-CA, The Netherlands,deutz@liacs.nl

Matthias Ehrgott Department of Engineering Science, The University of

Auckland, Private Bag 92019, Auckland 1142, New Zealand, m.ehrgott@auckland.ac.nz

Michael T M Emmerich Leiden Institute for Advanced Computer Science

(LIACS), Leiden University, Niels Bohrweg 1, Leiden 2333-CA, The Netherlands,emmerich@liacs.nl

Orhan Feyzio˘glu Department of Industrial Engineering, Galatasaray University,

C¸ ıra˘gan Caddesi No: 36 Ortak¨oy, ˙Istanbul, Turkey, ofeyzioglu@gsu.edu.tr

Jos´e Rui Figueira Centre for Management Studies of Instituto Superior T´ecnico,

Technical University of Lisbon, Tagus Park, Av Cavaco Silva, Porto Salvo, Lisbon2780-990, Portugal, figueira@ist.utl.pt

Lucie Galand LIP6-UPMC, 104 av du Pr´esident Kennedy, Paris 75016, France,

lucie.galand@lip6.fr

Contributors xv

Carmen Gal´e Dpto de M´etodos Estad´ısticos, IUMA, Universidad de Zaragoza,

Mar´ıa de Luna 3, Zaragoza 50018, Spain, cgale@unizar.es

Xavier Gandibleux Laboratoire d’Informatique de Nantes Atlantique UMR

CNRS, 6241, Universit´e de Nantes, 2, rue de la Houssini`ere BP 92208, F-44322Nantes cedex 03, France, Xavier.Gandibleux@univ-nantes.fr

Salvatore Greco Faculty of Economics, University of Catania, Corso Italia, 55,

Catania 95129, Italy, salgreco@unict.it

Stefan Hajkowicz CSIRO Sustainable Ecosystems, St Lucia Qld 4067, Australia,

stefan.hajkowicz@csiro.au

Frank Henrich Siemens AG, Energy Sector, Wolfgang-Reuter-Platz 4, Duisburg

47053, Germany, frank.henrich@siemens.com

Alfredo G Hernandez-Diaz Department of Economics, Quantitative Methods

and Economic History, Pablo de Olavide University, Seville, Spain, agarher@upo.es

Andrew Higgins CSIRO Sustainable Ecosystems, St Lucia Qld 4067, Australia,

andrew.higgins@csiro.au

Tim Hohm Computer Engineering and Networks Lab, ETH Zurich, Zurich 8092,

Switzerland, tim.hohm@tik.ee.ethz.ch

Rafikul Islam Department of Business Administration, International Islamic

University, Malaysia, P.O Box 10, Kuala Lumpur 50728, Malaysia,

rislam@iiu.edu.my

Isabel M Jo˜ao Department of Chemical Engineering, Instituto Superior

de Engenharia de Lisboa, Polytechnic Institute of Lisbon, Rua Conselheiro Em´ıdioNavarro, Lisbon 1957-007, Portugal, ijoao@deq.isel.ipl.pt

Birsen Karpak Management Department, Youngstown State University, USA,

bkarpak@ysu.edu

Christoph Kausch Chair of Technical Thermodynamics, RWTH Aachen

University, Schinkelstr 8, Aachen 52062, Germany, kausch@ltt.rwth-aachen.de

Jan-Willem Klinkenberg Leiden Institute for Advanced Computer Science

(LIACS), Leiden University, Niels Bohrweg 1, Leiden 2333-CA, The Netherlands,jklinken@liacs.nl

Joshua Knowles School of Computer Science, University of Manchester, Oxford

Road, Kilburn building, Manchester M13 9PL, UK, j.knowles@manchester.ac.uk

Pekka Korhonen Helsinki School of Economics, Department of Business

Technology, P.O Box 1210, Helsinki 00101, Finland, firstname.lastname@hse.fi

Anatoly Levchenkov Riga Technical University, Kronvalda blvd., Riga 1-202,

Latvia, anatolijs.levcenkovs@rtu.lv

Oswald Marinoni CSIRO Sustainable Ecosystems, St Lucia Qld 4067, Australia,

oswald.marinoni@csiro.au

Benedetto Matarazzo Faculty of Economics, University of Catania, Corso Italia,

55, Catania 95129, Italy, matarazz@unict.it

Kaisa Miettinen Department of Mathematical Information Technology, P.O Box

35 (Agora), University of Jyv¨askyl¨a, FI-40014, Finland, kaisa.miettinen@jyu.fi

Ludmil Mikhailov Manchester Business School, University of Manchester, Booth

Street East, Manchester M15 6PB, UK, ludmil.mikhailov@manchester.ac.uk

Julian Molina Department of Applied Economics (Mathematics), University

of Malaga, Malaga, Spain, julian.molina@uma.es

Anna Nagurney Department of Finance and Operations Management, Isenberg

School of Management, University of Massachusetts Amherst, Massachusetts

01003, USA, nagurney@gbfin.umass.edu

Hirotaka Nakayama Konan University, 8-9-1 Okamoto, Higashinada, Kobe

658-8501, Japan, nakayama@konan-u.ac.jp

Bhupendra K Pathak Department of Mathematics, Dayalbagh Educational

Institute, Dayalbagh, Agra 282110, India, pathak111@gmail.com

Fatima Perez Department of Applied Economics (Mathematics), University

of Malaga, Malaga, Spain,f perez@uma.es

Patrice Perny LIP6-UPMC, 104 av du Pr´esident Kennedy, Paris 75016, France,

patrice.perny@lip6.fr

Mike Preuss Chair of Algorithm Engineering, TU Dortmund University,

Otto-Hahn-Str 14, Dortmund 44227, Germany, mike.preuss@cs.uni-dortmund.de

Erik Pruyt Faculty of Technology, Policy and Management, Delft University of

Technology, P.O Box 5015, GA Delft 2600, The Netherlands, E.Pruyt@tudelft.nl

Andrea Raith Department of Engineering Science, The University of Auckland,

Private Bag 92019, Auckland 1142, New Zealand, a.raith@auckland.ac.nz

Pierre Riteau Laboratoire d’Informatique de Nantes Atlantique UMR CNRS

6241, Universit´e de Nantes, 2, rue de la Houssini´ere BP 92208, F-44322 Nantescedex 03, France, Pierre.Riteau@etu.univ-nantes.fr

Thomas L Saaty Joseph Katz Graduate School of Business, University

of Pittsburgh, 322 Mervis Hall, Pittsburgh, PA 15260, USA, saaty@katz.pitt.edu

Perica Savanovic Faculty of Architecture, Building and Planning, Technische

Universiteit Eindhoven, The Netherlands, psavanovic@bwk.tue.nl

Thomas Schlechte Konrad-Zuse-Zentrum f¨ur Informationstechnik Berlin (ZIB),

Takustr 7, Berlin-Dahlem 14195, Germany, schlechte@zib.de

Oliver Sch ¨utze CINVESTAV-IPN, Computer Science Department, M´exico D.F.

07360, Mexico, schuetze@cs.cinvestav.mx

Iniyan Selvarsan Department of Mechanical Engineering, College of Engineering,

Guindy, Anna University, Chennai 600025, India, iniyan777@hotmail.com

Contributors xvii

Yong Shi College of Information Science and Technology, University of Nebraska,

Omaha, NE 68118, USA, yshi@unomaha.edu

and

Chinese Academy of Sciences Research Center on Fictitious Economy And DataScience, Graduate University of Chinese Academy of Sciences, Beijing 100080,China, yshi@gucas.ac.cn

Masakazu Shirakawa Toshiba Corporation, 2-4 Suehirocho, Tsurumi, Yokohama

230-0045, Japan, masakazu1.shirakawa@toshiba.co.jp

Roman Słow´ınski Institute of Computing Science, Pozna´n University of

Technology, 60-965 Poznan, and Systems Research Institute, Polish Academy

of Sciences, Warsaw 01-447, Poland, roman.slowinski@cs.put.poznan.pl

Olivier Spanjaard LIP6-UPMC, 104 av du Pr´esident Kennedy, Paris 75016,

France, olivier.spanjaard@lip6.fr

Kamal Srivastava Department of Mathematics, Dayalbagh Educational Institute,

Dayalbagh, Agra 282110, India, kamalsrivast@yahoo.com

Sanjay Srivastava Department of Mechanical Engineering, Dayalbagh

Educational Institute, Dayalbagh, Agra 282110, India, ssrivastava.engg@gmail.com

El-Ghazaali Talbi INRIA Lille-Nord Europe, LIFL (UMR USTL/CNRS 8022),

Parc Scientifique de la Haute Borne 40, avenue Halley Bˆat.A, Park Plaza,Villeneuve d’Ascq C´edex 59650, France, El-Ghazali.Talbi@lifl.fr

Emilia Tantar INRIA Lille-Nord Europe, LIFL (UMR USTL/CNRS 8022), Parc

Scientifique de la Haute Borne 40, avenue Halley Bˆat.A, Park Plaza, Villeneuved’Ascq C´edex 59650, France, Emilia.Tantar@lifl.fr

Y Ilker Topcu Istanbul Technical University, Management Faculty, Macka,

Istanbul 34367, Turkey, ilker.topcu@itu.edu.tr

Rinus van Houten Faculty of Architecture, Building and Planning, Technische

Universiteit Eindhoven, The Netherlands, m.a.v.houten@bwk.tue.nl

Nandigana V.R Vishal Department of Mechanical Engineering, College

of Engineering, Guindy, Anna University, Chennai 600025, India

Hannele Wallenius Helsinki University of Technology, Department of Industrial

Engineering and Management, P.O Box 5500, TKK 02015, Finland, hannele.wallenius@tkk.fi

Jyrki Wallenius Helsinki School of Economics, Department of Business

Technology, P.O Box 1210, Helsinki 00101, Finland, jyrki.wallenius@hse.fi

Judith Y.T Wang The Energy Centre, The University of Auckland Business

School, Private Bag 92019, Auckland 1142, New Zealand, j.wang@auckland.ac.nz

Diederik Wijnmalen Strategic Choices Department, TNO Organisation

for Applied Research, P.O Box 96864, 2509 JG, The Hague, The Netherlands,diederik.wijnmalen@tno.nl

Matthias Woehrle Computer Engineering and Networks Lab, ETH Zurich,

Zurich 8092, Switzerland, matthias.woehrle@tik.ee.ethz.ch

Trisha Woolley Department of Finance and Operations Management, Isenberg

School of Management, University of Massachusetts Amherst, Massachusetts

01003, USA, twoolley@som.umass.edu

Nian Yan College of Information Science and Technology, University of Nebraska,

Omaha, NE 68182, USA, nyan@mail.unomaha.edu

Yeboon Yun Kagawa University, 2217-20 Hayashicho, Takamatsu 761-0396,

Japan, yun@eng.kagawa-u.ac.jp

Wim Zeiler Faculty of Architecture, Building and Planning, Technische

Universiteit, Eindhoven, The Netherlands, w.zeiler@bwk.tue.nl

Liang Zhu Department of Mathematics, Fudan University, Shanghai, China,

godloveme zhu@hotmail.com

Minyue Zhu Department of Mathematics, University of Hong Kong, Pokfulam,

Road, Hong Kong, China, zhuminyue@gmail.com

Eckart Zitzler Computer Engineering and Networks Lab, ETH Zurich, 8092

Zurich, Switzerland, zitzler@tik.ee.ethz.ch

Optimization in the Design of Sustainable

Energy Systems

Claude Bouvy, Christoph Kausch, Mike Preuss, and Frank Henrich

Abstract A new multi-criterial methodology is introduced for the combined

struc-tural and operational optimization of energy supply systems and production cesses The methodology combines a multi-criterial evolutionary optimizer forstructural optimization with a code for the operational optimization and simula-tion The genotype of the individuals is interpreted with a superstructure Themethodology is applied to three real world case studies: one communal and oneindustrial energy supply system, one distillation plant The resulting Pareto frontsand potentials for cost reduction and ecological savings are discussed

pro-Keywords Communal energy supply concepts  Distillation plants  Evolutionary

algorithms  Industrial energy supply systems  Multi-objective optimization

1 Introduction

Due to the finite resources of fossil fuels, their increasing costs and the rising ness concerning the impact of CO2emissions on the climate, the design of highlyefficient energy supply systems and manufacturing processes is essential for a sus-tainable energy supply in the future Like for most engineering tasks several decisionmaking criteria (i.e., objectives), mostly contradictory, are relevant for this task Inthe design phase of energy systems economic factors (e.g., investment sum, overallyearly costs) are opposed to ecological (e.g., yearly CO2emissions, yearly primaryenergy consumption) and reliability (e.g., availability of a given technology, sup-ply security) criteria The sustainability of an energy supply system will be givenwith minimal ecological impact and maximal availability (as no back-up systembased on fossil fuels will be needed) However the economic range will in general

aware-C Bouvy (B)

Forschungsgesellschaft Kraftfahrwesen mbH Aachen, Steinbachstraß e7,

52074 Aachen, Germany,

e-mail: bouvy@fka.de

M Ehrgott et al., Multiple Criteria Decision Making for Sustainable Energy and

Transportation Systems, Lecture Notes in Economics and Mathematical Systems 634,

DOI 10.1007/978-3-642-04045-0 1,

c

 Springer Physica-Verlag Berlin Heidelberg 2010

3

The design task formulated above is an overall (i.e., operational and structural)optimization problem Such optimization tools were developed at the Chair of Tech-nical Thermodynamics of RWTH Aachen University (cf Bouvy 2007 and Bouvyand Lucas 2007) for energy supply systems and in co-operation with the Chair ofAlgorithm Engineering of the University of Dortmund for distillation plant layout(cf Henrich et al 2008 and Preuss et al 2008).

2 Methodology

It is clear that for highly multi-objective optimization tasks, as the overall tion of energy supply systems and manufacturing processes, an a priori decisionmaking concept (e.g., a priori weighting of decision criteria) is not convenient,because it will not take into account the complex topology of the solution space.Thus in this work a n-dimensional Pareto concept is used to support the planningengineer in the design phase The multi-objective optimization methodology pre-sented in this work combines a multi-objective structural optimization tool, based

optimiza-on evolutioptimiza-onary strategy with an operatioptimiza-onal optimizer and simulator Evolutioptimiza-onarystrategies (specific methodology of evolutionary algorithms) are bionic, proba-bilistic optimization method belonging to the category of metaheuristics (cf e.g.,Schwefel 1995 and Eiben and Smith 2003) Evolutionary algorithms perform adirect search, i.e., no gradient information is required The chosen evolutionaryoptimizers are a modified  C /-evolutionary strategy for the two first exam-ples in Sect 3 (cf Bouvy 2007 and Bouvy and Lucas 2007) and the SMS-EMOA(cf Emmerich et al 2005, Henrich et al 2008, and Preuss et al 2008) for the thirdexample The used methodology is outlined in Fig 1

As evolutionary algorithms in general and evolutionary strategies in particularneed an initialization at least one individual (i.e., a precise energy supply system

or distillation plant) is manually entered (“Initial individual(s)” in Fig 1) The lutionary optimizer then generates an initial population by method of a stochasticalgorithm, which is designed to get a good distribution of the initial population overthe solution space

The generated solutions and later on the newly generated individuals (by the lutionary optimizer) are a vector of real, integer and listed values (i.e., the genotype)

evo-In order to interpret this set of values their interactions have to be defined, which is

Fig 1 Scheme of the used

methodology

realized in this work by means of a superstructure A superstructure is a structurewhich includes all (or at least a large number of) reasonable possibilities of combi-nations of the considered units (e.g., co-generation units, district heating pipe) andhas thus to be adapted to every optimization task

In order to compare the fitness of the different solutions generated by the tionary optimizer, all n decision making criteria are computed by a simulator Forthe design of energy systems the operational optimizer and simulator “eSim” of thetoolbox “TOP-Energy” (cf Augenstein et al 2004 and Augenstein and Herbergs2005) was used, whereas ASPEN PLUSTM was used for the layout of distillationplants

evolu-Based on the determined fitness values the evolutionary optimizer applies theoperators “mutation” and “recombination” (cf e.g., Schwefel 1995) to generate anew set of solutions, which will again be interpreted and computed by the simulator

At each run of the closed cycle shown in Fig 1 only the fittest individuals survive.Similar to the evolutionary process in nature, this methodology will result in theimprovement of the living population

If a predefined termination criterion is reached, the optimization runs result

in n-dimensional Pareto sets When comparing any individual to an individual iaccording to the Pareto criterion all individuals with all fitness values larger thanthose of i (for minimization) are inferior to i In the same way all individuals withall fitness values smaller than those of i are clearly better than i For all other indi-viduals no statement can be made because some criteria are better and other onesworse On the one hand these Pareto sets will identify promising structural alterna-tives, reducing for example the CO2emissions considerably with simultaneous lowcosts On the other hand the ecological criteria are a good indicator of the stability ofthe chosen structural solutions towards rising energy costs Thus this methodologyyields actual potentials for reducing ecological impacts as well as information aboutthe stability towards changing energy supply costs whilst fulfilling all boundaryconditions

6 C Bouvy et al.

3 Real-world Applications

In this section the application of the methodology described above to three different

“real world” case studies is discussed

3.1 Communal Energy System

The communal area considered in this section consists of seven residential districts,which represent the consumers The task is to determine optimal structural solutionsfor a communal energy supplier The goal of this optimization problem is to reduceoverall costs of the system as well as the ecological impact Thus the overall yearlycosts (i.e., sum of the yearly capital costs and the yearly operational costs) and theyearly primary energy consumption were chosen as decision making criteria

On the demand side only electricity and space heating are taken into account Theelectrical peak load for this real world problem is 100 MW whereas a thermal peakload of 176:6 MW was calculated It is important to note that the time dependencies

of the heat and the electricity demand are considered for the optimization runs

A detailed description of the demands can be found in Bouvy and Lucas (2007)

or Bouvy (2007) For cost calculation all relevant shares were considered and currentsupply costs for electricity (6 ct=kWh) and natural gas (2 ct=kWh) were assumed(costs for a regional supplier)

A standard supply system for the considered districts would consist of an tricity grid, fed by distant condensation power plants, and a natural gas rail for spaceheat production by boilers in the various buildings This solution was chosen as startindividual for the optimization run (cf Fig 2)

elec-The structural margin for the optimization, coded in the superstructure, includedheat production on the house level with boilers or heat pumps, co-generation on acentralized level for each district (supported by peak-load boilers), heat distribution

by several possible district heating networks and electricity generation at a plant level (condensation and co-generation plants) A detailed description is found

power-in Bouvy and Lucas (2007) and Bouvy (2007) Due to the high number of ble structural alternatives (nominal powers and crosslinking) the resulting solutionspace is highly complex

possi-The progress of the optimization and the resulting pareto fronts at differentgenerations  are given in Fig 2

As the evaluation in “eSim” of one solution (i.e., one individual) takes about

1 min (8640 h=a) the optimization time for this task was very high (about 720 h

on a Pentium 4 desktop computer with a 3:2 GHz CPU for the results shown inFig 2) and had to be prematurely stopped Thus the evolutionary optimizer did notyet reach the vicinity of the global optimum The large computing times are a wellknown phenomenon of the chosen self adapting evolutionary strategies The mainreason for this were superfluous components (e.g., district heating networks thatwere not used during the operation over the considered year) causing higher yearly

Yearly Primary Energy Demand in GWh/a

Initial individual

Yearly costs in 10 6 €/a

Fig 2 Results and progress of the optimization run for the communal energy system

costs As the optimization runs were stopped early these costs had to be correctedmanually Figure 2 shows the corrected individuals marked as red dots ( D 500).These corrected individuals show, that both primary energy and yearly costs can besaved compared to a non-integrated energy supply system based on heat supply withboilers and electricity supply with a condensation combined gas and steam powerplant This savings are mainly based on the extensive use of electricity driven heatpumps on a decentralized level due to the reasonable average power to heat ratio

of the heat pumps ("  5), resulting from the assumed low temperature heatingsystems (supply temperatures of 55ıC) For the considered boundary conditions areduction in primary energy demand of approximatively 25% compared to a non-integrated energy supply system can be reached with a simultaneous reduction ofyearly overall costs

3.2 Industrial Energy System

The second case study considered in this work is the optimization of an trial energy system Contrary to the communal application, presented above, theenergy demand consists of electricity (peak load 720 kW), low (peak load 1260 kW)and high temperature heat (steam, peak load 910 kW) The superstructure for thiscase study considers steam production with high temperature co-generation units(micro-turbines) or steam boilers Low temperature heat can be supplied by eitherboilers, mechanical heat pumps, motoric co-generation units or by heat exchangefrom the steam grid Besides the considered co-generation units electrical energy

indus-8 C Bouvy et al.

Yearly costs / €/a

Fig 3 Results of the optimization run for the industrial energy system

can be bought from a supplier Again all time dependencies over a year were sidered for all three energies A detailed description of the demand profiles andtheir interactions is given in Bouvy (2007) As decision making criteria again thetotal yearly costs and the yearly primary energy demand were chosen For cost cal-culation current supply costs for electricity (12 ct=kWh) and natural gas (5 ct=kWh)were assumed (costs for an industrial customer)

con-As start individual again a non-integrated energy supply system, based on nal electricity supply, and heat production with a low temperature and a steamboiler

exter-The results of the optimization run for this industrial energy system are shown inFig 3

As only three demand profiles (i.e., consumers) had to be covered, the complexityand thus the solution space of this optimization task is smaller than for the commu-nal energy system Furthermore the evaluation in “eSim” of one precise solution isabout 30 times quicker Better results are thus reached in less calculation time Theresults shown in Fig 3 were reached after 120 h on a Pentium 4 desktop computerwith a 3:2 GHz CPU They should be situated very near to the global optimum asnearly no superfluous units were found in the individuals of the Pareto front shown

in Fig 3

For this optimization run numerous solutions clearly dominating (i.e., with bothlower costs and lower primary energy demand) the start individual were found,thus revealing an important potential for highly integrated energy systems This

is mainly due to the higher energy supply costs compared to the first case studypresented The start individual was not found in the final Pareto front It should bementioned that all Pareto optimal solutions had at least one 80 kWelmicro-turbinefor steam production This technology is very stable for the considered case study as

the recuperator bypass allows an adaption of the exhaust gas temperature and thus

an optimal covering of the demand profile

Figure 3 reveals also another interesting fact Two regions for saving primaryenergy can be identified In the first region (from 11750 to 1260 MWh=a) costs forsaving primary energy can be estimated to about 3:75 ct=kWh (i.e., the slope of theblue line) It is to mention that these saving costs are even less than the supply costsfor natural gas If a further saving in primary energy is intended the costs raise to19:63 ct=kWh (i.e., the slope of the red line) This supports the idea of real multicriterial decision making (i.e., not by a priori weighting) as important potentials canonly be revealed when the knowledge of the solution space is included

3.3 Distillation Plant

Another problem which has been investigated is the layout and operation of a eral distillation sequence for the separation of multi-component feed streams intomulti-component products using non-sharp splits Different objective functions areanalyzed including economic criteria like the total annual cost, investment costand the return on investment as well as ecological criteria like the exergy loss(cf Henrich et al 2008 and Preuss et al 2008) The structural alternatives included

gen-in the superstructure are stream bypassgen-ing and blendgen-ing, the number and sequence

of splits as shown in Fig 4

Together with the operational variables of the distillation columns this results in

a highly complex decision space involving non-convexities and multi-modalities.Each proposed structure and operation is modeled with ASPEN PLUSTMto ensurethat all boundary conditions are met and the solution is thermodynamically sound.The exergy loss and investment cost Pareto front for the case described in detail

in Preuss et al (2008) is shown in Fig 5 In practice generally the solutions of est would be those forming “corners” in the front (e.g., the points at 720 tUSDand 340 kW, at 790 tUSD and 325 kW, at 890 tUSD and 315 kW), whereas solu-tions promising a small gain in the one objective at the cost of a large loss in theother objective would not normally be of interest (e.g., 1060 tUSD and 312 kW)

inter-Fig 4 Structural alternatives

for separation of

3-component feed into multi

component products with

non-sharp splits

2 A

(A), B

(B), C A,B, (C)

A,B,C

P5

P4

1 F

10 C Bouvy et al.

Fig 5 Exergy loss and investment cost Pareto fronts of the best 5 runs

Additionally there are 3 fronts which at first glance seem similar but the solutions

of the fronts stem from very different areas in the variable space thus if points are ofinterest where the fronts overlap or are near identical, then an additional criteria can

be taken into account which is of course vastly beneficial to the design and planningprocess

4 Conclusions and Potential

The applications of the introduced methodology have shown that the introducedconcept is adequate for supporting the planning engineer and the decision maker

in the design phase of energy supply systems and complex production processes.Very promising solutions were found for each real world case Especially the Paretoconcept is very important for multi-criterial decision making as it takes into consid-eration the correlations of the different criteria Thus real potentials for both energyand ecological saving can be estimated and a decision can be taken for example withthe information of relative primary energy saving costs Methodologies that opti-mize only by means of a single criterion or that perform an a priori fixed weighting

of the decision making criteria will in general be computed quicker but on the otherhand will only result in a single solution and not a set

However the very high computing times for highly complex optimization tasksshow that the methodology has to be improved Several possibilities were given inBouvy (2007) On the one hand the parallelization of computers will reduce thecomputational time by distributing the evaluation of the different individuals On

the other hand the improvement or adaption of the evolutionary strategies to the task

is important Density dependent survival selection, niche penalty and kindergartenpopulation models are just three keywords for such algorithm tuning

In general it can be stated that the methodology has a very high potential insupporting planning engineers and decision makers in the design phase of complexenergy supply concepts and production processes Based on the good results it isplanned, beside the improvement of the algorithm, to introduce new indicators forthe sustainability of the resulting design Both for the production processes and theenergy supply systems a life cycle assessment (LCA) approach will be integrated

in the presented methodology, to take into consideration important influences such

as the impact on the environment due e.g., to the construction and dismantlement ofthe conversion units

As the number of criteria influences the speed of convergence of the algorithmand thus the quality of the results for a given calculation time, further effortsshould focus on the definition of new indicators for the sustainability of energysupply systems and production processes in order to keep the necessary criteria few.The proposed LCA approach (e.g., LCA resource-based indicators cf for exampleThomassen and de Boer 2005) is one example for such sustainability indicators.Unfortunately the discussed optimization results have not yet been realized.However the promising results led to the interest of planning engineers for appli-cation of the presented methodology to two communal energy supply systems, one

in Germany and one in Luxembourg

Acknowledgements Authors thankfully acknowledge the financial support of the DFG, the

German Research Foundation, in the context of the project “Mehrkriterielle Struktur- und teroptimierung verfahrenstechnischer Prozesse mit evolution¨aren Algorithmen am Beispiel gewin- norientierter unscharfer destillativer Trennprozesse”.

Parame-References

Augenstein, E & Herbergs, S (2005) Simulation of industrial energy supply systems with

inte-grated cost optimization In 18th International Conference on Efficiency, Cost, Optimization, Simulation and Environmental Impact of Energy Systems, Athens.

Augenstein, E., Wrobel, G., Kuperjans, I., & Plessow, M (2004) Top-energy: computational

support for energy system engineering processes In First international conference from scientific computing to computational engineering, Athens.

Bouvy, C (2007) Kombinierte Struktur- und Einsatzoptimierung von Energieversorgungssystemen mit einer Evolutionsstrategie Ph.D thesis, RWTH Aachen University.

Bouvy, C & Lucas, K (2007) Multicriterial optimisation of communal energy supply concepts.

Energy Conversion and Management, 48(11), 2827–2835.

Eiben, A & Smith, J (2003) Introduction to Evolutionary Computing Natural Computing Series.

Springer.

Emmerich, M., Beume, N., & Naujoks, B (2005) An EMO algorithm using the

hypervol-ume measure as selection criterion In C Coello Coello (Ed.), Evolutionary Multi-Criterion Optimization (EMO 2005) (pp 62–76) Berlin: Springer.

12 C Bouvy et al Henrich, F., Bouvy, C., Kausch, C., Lucas, K., Preuss, M., Rudolph, G., & Roosen, P (2008) Eco- nomic optimization of non-sharp separation sequences by means of evolutionary algorithms.

Computers & Chemical Engineering, 32(7), 1411–1432.

Preuss, M., Kausch, C., Bouvy, C., & Henrich, F (2008) Decision space diversity can be

essen-tial for solving multiobjective real-world problems In M Ehrgott (Ed.), 19th International Conference on Multiple Criteria Decision Making.

Schwefel, H (1995) Evolution and Optimum Seeking New York: Wiley.

Thomassen, M & de Boer, I (2005) Evaluation of indicators to assess the environmental impact

of dairy production systems Agriculture, ecosystems & environment, 111(1–4), 185–199.

Resources in India Using Analytical Hierarchy Process

Joseph Daniel, Nandigana V R Vishal, Bensely Albert, and Iniyan Selvarsan

Abstract A developing country like India encounters challenges like exponential

increase in population and rising per capita energy consumption which demands

an optimum usage of available energy resources Currently the energy demands aremostly met by non renewable energy sources, a system that puts a tremendous pres-sure on the economy and causes a serious threat to the environment, flora and fauna.Hence, the government and other state nodal agencies in India are taking initia-tives to promote the use of the renewable energy sources In the present study, anattempt has been made to arrive at the overall priorities of the renewable energysources available in India using Analytical Hierarchy Process (AHP) The importantparameters like Cost, Efficiency, Environmental impact, Installed capacity, Esti-mated potential, Reliability and Social acceptance are considered in this model toidentify and rank the renewable energy sources like solar, wind and biomass A sur-vey based on Delphi technique was conducted in the previous work from which thescales for the aforementioned parameters are fixed For each attribute, and each pair

of alternatives, the outcome of the survey specifies their preference in the form of

a fraction between 1/9 and 9 The results show the order of merit as Wind energy(0.501), Biomass energy (0.288), and Solar energy (0.2056) with respect to Indianpolicies and conditions to meet the future energy demand

Keywords AHP  Biomass  Renewable energy  Solar  Wind

1 Introduction

Developing countries are in general, countries which have not achieved a significantdegree of industrialization relative to their population and which have a low stan-dard of living India is considered to be among this class for the following valuable

B Albert (B)

Department of Mechanical Engineering, College of Engineering Guindy, Anna University, Chennai 600025, India,

email: benzlee5@yahoo.com

M Ehrgott et al., Multiple Criteria Decision Making for Sustainable Energy and

Transportation Systems, Lecture Notes in Economics and Mathematical Systems 634,

DOI 10.1007/978-3-642-04045-0 2,

c

 Springer Physica-Verlag Berlin Heidelberg 2010

13

14 J Daniel et al.

2 4 6 8 10 12 14 16

1992 1994 1996 1998 2000 2002 2004 2006 2008

Year

TPEP TPEC

Fig 1 India’s TPEP and TPEC, 1993–2003 (in Quads) (1 Quad D 1 quadrillion Btu) to show high

growth rates after its economic reform in 1991

reasons India with a population of over 1 billion and which probably will take China to be the most populous country with about 1.6 billion population by

over-2050 (Hubacek et al 2007) reveals the increase of the population of this country.India has a labor force of 509.3 million, 60% of which is employed in agricultureand related industries; 28% in services and related industries; and 12% in industry.The agricultural sector accounts for 28% of GDP; the service and industrial sectorsmake up 54% and 18% respectively These factors reveal that India is still a devel-oping country but promises to be a developed nation within a due course of time.The rising population has led to an increase in the per capita energy consumption ofthe country The population of India has experienced a transition from ‘poverty’ to

‘adequate food and clothing’ India has become the world’s fourth largest economy

in purchasing power and the twelfth largest economy at market exchange rates With

a GDP growth rate of 9.4% in 2006–07, the Indian economy is among the fastestgrowing in the world India currently ranks as the world’s eleventh greatest energyproducer, accounting for about 2.4% of the world’s total annual energy production,and as the world’s sixth greatest energy consumer, accounting for about 3.3% ofthe world’s total annual energy consumption Despite its large annual energy pro-duction, India is a net energy importer, mostly due to the large imbalance betweenoil production and consumption An historical summary of India’s Total PrimaryEnergy Production (TPEP) and Consumption (TPEC) is shown in Fig 1

2 Nonrenewable Energy Resources and Demands in India

India’s proven oil reserves are currently estimated (as of January 2005) at about

5 billion barrels, or about 4.5% of the world total (Government of India 2007).India presently ranks as the 25th greatest producer of crude oil, accounting for about

1% of the world’s annual crude oil production About 30% of India’s energy needsare met by oil, and more than 60% of that oil is imported A strong growth in oildemand has resulted in India’s annual petroleum consumption increasing by morethan 75% from what it was a decade ago, and petroleum consumption is projected toclimb to about 3 million barrels per day by 2010 India is currently the world’s sixthgreatest oil consumer, accounting for about 2.9% of world’s total annual petroleumconsumption.

India’s natural gas reserves are currently estimated (as of January 2005) at about29–32 trillion cubic feet (tcf), or about 0.5% of the world total Natural gas hasexperienced the fastest rate of increase of any fuel in India’s primary energy supply;demand is growing at about 4.8% per year and is forecasted to rise to 1.2 tcf per year

by 2010 and 1.6 tcf per year by 2015

India is currently the third-largest coal-producing country in the world (behindChina and the United States), and accounts for about 8.5% of the world’s annual coalproduction India is also currently the third-largest coal consuming country behindthe China and the United States and it accounts for nearly 9% of the world’s totalannual coal consumption More than half of India’s energy needs are met by coal,and about 70% of India’s electricity generation is now fueled by coal

3 Government Initiatives to Promote Renewable Energy

As against the estimated 151,500 MW, (GENI 2006) renewable energy based gridconnected power generation potential in the country so far is only about 10,250 MW(MNRE 2008) installed capacity has been realized, giving vast opportunity forexploitation of renewable energy sources for power generation The renewableenergy based power generation capacity presently constitutes 6% of the totalinstalled capacity in the country for power generation from all sources The coun-try is aiming to achieve up to 10% of additional installed capacity to be set uptill 2012 to come from renewable energy sources The per capita consumption ofthis form of energy in India is around 400 kWh/a The government and other statenodal agencies are offering different types of incentives to promote the use of therenewable form of energy and help in overcoming the increasing demands of thepeople The Government’s Common Minimum Program to establish enough renew-able energy sources to electrify all India’s villages by 2010 Under the program,

an additional 4,000 MW of power from renewable sources would be added to thenation’s current power generation by 2007, and the government has set a goal ofelevating the share of renewable energy sources to 10% by 2012 Currently renew-able energy contributes about 5,000 MW of the nation’s power needs That is only4.5% of the total installed generating capacity from all available power sources

in India

a programme to harness wind energy for water pumping, battery charging, powergeneration and suitable incentives are given for the installation of wind mills Themain contributors of wind energy are the states of Tamil Nadu, Gujarat, Maharash-tra, Andhra Pradesh which have fed 15 billion units of electric power to the grid.Initiatives are taken for wind resource survey towards the publication of wind energydata book, wind monitoring and mapping programmes A wind turbine testing sta-tion is set up at Kayathar in the state of Tamil Nadu by the Centre for Wind EnergyTechnology (C-WET) Three demonstration wind farms are installed at Kayathar(6 MW) Muppandal (4 MW) and Lamba (10 MW) The government provides 50%tax exemption for investments made in wind farms There are about 15 manufactur-ers involved in the installation of 850 MW The average capital cost of wind powergeneration projects ranges between Rs 40–50 million/MW including local, civiland electricity works The levelized cost of power generation varies from 1.50 to2.00 Rs/kWh (Jebaraj 2006)

commu-in India, about 32.5 million chulhas are commu-installed so far 3,500 MW of power isgenerated through bagasse based sugar mills (Jebaraj 2006)

3.3 Solar Energy

India has an availability of 8 million MW of solar energy which is equivalent to5,909 million tonnes of oil equivalent Till 2005 about 1 million sq km of collector

area is installed and at present there are about 48 manufacturers of solar water ing systems in India Under MNRE about 2; 500 m2 collector area is installed forair heating Solar heaters save up to 717,373 KWh of electricity per year There areabout 42 manufacturers of solar cookers in the country and subsidies are given forsolar cookers MNRE also supports solar stills programme in the country About1.2 MW aggregate capacities of stand alone thermal power plants and 1.8 MWgrid interactive power plants have been installed in the country so far There areabout 6,818 PV water pumps, 2,891 KW PV power units, 54,795 PV communitylights and street lights, 342,000 PV home lighting systems and 560,000 PV lanternsare installed in the country IREDA promotes and finances private sector invest-ments in solar energy 980,000 PV systems aggregating to about 96 MW have beendeployed in India and a cumulative deployment of 300 MW capacity power systemswould be achieved in 2007 70 MW capacity PV systems comprising of 600,000lanterns, 250,000 home lighting systems, 8,000 solar pumps, 10,000 solar gen-erators, 4 MW stand alone PV plants and 5 MW capacity grid interactive powerplants are expected to be commissioned under MNRE 4,000 villages are also to beelectrified under solar energy (Jebaraj, 2006) Table 1 shows a comparison of thesignificant renewable energy sources in India highlighting the key parameters used

heat-in this study

4 AHP Approach

AHP was first developed by Thomas Saaty in the 1970s The principles and ophy of the theory of this multi criteria decision making technique were explainedgiving background information of the type of measurement utilized, its propertiesand applications (Saaty 1990) It is becoming quite popular in research due to thefact that its utility outweighs other rating methods (Eddi and Hang 2001) The AHPmethodology has been accepted by the international scientific community as a robustand flexible multi-criteria decision-making tool for dealing with complex decisionproblems (Elkarmi and Mustafa 1993) The strength of the AHP approach is based

philos-on breaking the complex decisiphilos-on problem in a logical manner into many small butrelated sub-problems in the form of levels of a hierarchy The hierarchical structure

of the AHP model permits decision- makers (DMs) to compare the different oritization criteria and alternatives more effectively The AHP may involve groupdiscussion and dynamic adjustments to finally arrive to a consensus This methodemploys a consistency test that can screen out inconsistent judgments

pri-Saaty developed the following steps for the application of the AHP

1 The prime objective of the problem and the criteria that influences the primeobjective has to be identified

2 The problem is structured with respect to hierarchy of goal, criteria, sub-criteria,and alternative

3 In the second level of the hierarchy

20 J Daniel et al.

Table 2 Saaty’s scale of preferences in the pair-wise comparison process

between alternatives i and alternatives j

 Local priority vector is obtained by normalizing the elements in each column

of the matrix of judgments by averaging the columns individually and dividingeach member of the column by the column averaged value and calculating theaverage over the rows of the resulting matrix

 The consistency ratio of the matrix of judgments is computed to make surethat the judgments are consistent Average random index as per Table 3 is usedfor this

4 Step 3 is repeated for all elements in a succeeding level but with respect to eachcriterion in the preceding level

5 The local priorities over the hierarchy to get an overall priority for each tive are synthesized

alterna-This AHP approach can be applied to numerous decision problems such asenergy policy, project selection, measuring business performance and evaluation ofadvanced manufacturing technology (Saaty 1980)

5 Application of AHP for the Prioritization of Renewable

Energy Resources in India

The matrix was arranged taking into consideration the parameters like cost, ciency (EFF), Environmental Impact (EI), Installed Capacity (IC), Estimated Poten-tial (ESPT), Reliability (RE), and Social Acceptance (SA) and the Numerical Ratingfor comparing each attribute was assigned from the comparisons presented inTable 2 and the attributes like social acceptance and reliability was rated based onthe Delphi study (Iniyan et al 2001) carried out in the previous work

effi-The matrices of judgments corresponding to the pair-wise comparison of ments at each level of the hierarchy are shown in Fig 2 A brainstorming session

ele-Overall priorities of Energy Sources in

Parameters

Energy

Resources

Fig 2 AHP model for the prioritization of the available renewable energy sources in India

Table 4 Pair-wise comparison matrix of criteria with respect to the goal

is compared with other criteria in the matrix For instance if criteria 2 (EFF) isconsidered four times more important than criteria 7 (SA) then the entry in the.27/th position of the matrix is 4 The entries below the diagonal are the reciprocal

of those entries above the diagonal This implies that only the judgments in theupper triangle of the matrix need to be solicited

A local priority vector (PVE) can be generated for the matrix of judgments inTable 4 by normalizing the vector in each column of the matrix (dividing each entry

of the column by the column total) and averaging over the rows of the resultingmatrix as shown in Table 5 The resulting local priority vector can be given as:(0.264, 0.207, 0.174, 0.119, 0.099, 0.092, 0.046)

22 J Daniel et al.

Table 5 Computing the priority vector from the judgments in Table 2

266666

0:2640:2070:1740:1190:0990:0920:046

3777775D

2666664

2:0121:5451:2940:8820:7300:6590:331

37777752

666664

2:0121:5451:2940:8820:7300:6590:331

3777775

D max

266666

0:2640:2070:1740:1190:0990:0920:046

377777

The above matrix is resolved to obtain the value of λmax/average

1:32 D 0:05:

where the value of RI D 1:32 was read from Table 3 for n D 7 Since the tency ratio is below 10% thus the judgments in Table 3 are considered consistent

consis-If the judgments in Table 3 were inconsistent then the decision maker (DM) wouldhave to revise his judgments until they are consistent The pair-wise comparisonmatrices of the alternatives are carried out with respect to each type of renewableenergy resource mentioned are shown in Tables 6–12 The local priority vectorand the consistency ratio for each matrix were computed and displayed on eachcorresponding table.

Table 6 Pair-wise comparison of energy resources to the cost criterion C.I D 0.03861, R.I D

24 J Daniel et al.

5.1 Synthesizing Judgments

The composite priorities of the alternatives are then determined by aggregating theweights throughout the hierarchy The composite priorities of the alternatives arecomputed by multiplying the local priorities of the alternatives with the local priori-ties of the criteria, which is given in the equation below This equation indicates thatthe global or composite weights for the Renewable energy resources with respect toIndian policies and conditions to meet the future energy demand

2

40:0632 0:0755 0:4545 0:0612 0:7938 0:0637 0:06370:5621 0:5907 0:4545 0:7231 0:1395 0:2674 0:6689

0:3748 0:3338 0:0909 0:2157 0:0667 0:6689 0:2674

35

2666664

0:2640:2070:1740:1190:0990:0920:046

3777775D

2

40:20560:50140:2880

35

to be less than 10% Each attribute that were ranked earlier are used for comparisonwith respect to wind solar and biomass and the judgments proved to be consistent

Table 11 Pair-wise comparison of energy resources to the reliability (RE) criterion C.I D 0.0146,

Table 12 Pair-wise comparison of energy resources to the social acceptance (SA) criterion C.I D

6 Conclusion

The Washington based world watch institute recognizes India as wind superpowerand remains as one of the fastest growing market for wind energy in terms of poten-tial and rate of installation India is placed fourth after Germany, Denmark andthe USA With the available potential and technical expertise and relative ease inpower production wind energy tops all the other forms of renewable energy systems

in India Initiatives taken by government agencies and other private sectors haveencouraged other renewable energy systems also to move forward which motivated

to conduct a technical analysis based on AHP to find the influence of each attributesand not to leave alone the other systems like Biomass and Solar energy just on pre-existing judgments This novel approach revealed as well that wind energy seems

to be the most promising renewable energy resource as compared to other systemsbut it has also thrown some light on the areas that has to be considered for bringingsolar and biomass energy in equal competence with wind energy in providing a sus-tainable, cheap and environmental friendly power to the increasing energy demand

of India

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