Skip to main content

Territorial Design for Matching Green Energy Supply and Energy Consumption: The Case of Turkey

  • Chapter
  • First Online:
  • 1612 Accesses

Part of the book series: Green Energy and Technology ((GREEN,volume 129))

Abstract

Green energy (GE) refers to energy sources that have no undesired consequences such as carbon emissions from fossil fuels or hazardous waste from nuclear energy. Alternative energy sources are renewable and are thought to be “free” energy sources. These include biomass energy, wind energy, solar energy, geothermal energy, and hydroelectric energy sources. GE supply is viewed as an option for satisfying the increased energy demand with the prospect of carbon accountability. However, geographical areas have diverse GE resources and different levels of energy consumptions. Territory design is defined as the problem of grouping geographic areas into larger geographic clusters called territories in such a way that the grouping is acceptable according to the planning criteria. The aim of this study is to group geographic areas in such a way that energy requirement in a geographic cluster matches the available GE potential in the same cluster. In this way, investments may be supported through region specific policies.

This is a preview of subscription content, log in via an institution.

Buying options

Chapter
USD   29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD   129.00
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Softcover Book
USD   169.99
Price excludes VAT (USA)
  • Compact, lightweight edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info
Hardcover Book
USD   169.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Learn about institutional subscriptions

References

  • Alsamamra H, Ruiz-Arias JA, Pozo-Vazquez D, Tovar-Pescador J (2009) A comparative study of ordinary and residual kriging techniques for mapping global solar radiation over southern Spain. Agric For Meteorol 149:1343–1357

    Article  Google Scholar 

  • Amarawickrama HA, Hunt LC (2008) Electricity demand for Sri Lanka: a time series analysis. Energy 33(5):724–739

    Article  Google Scholar 

  • Amjady N, Keynia F (2008) Day ahead price forecasting of electricity markets by a mixed data model and hybrid forecast method. Int J Electr Power Energy Syst 30(9):533–546

    Article  Google Scholar 

  • Angelis-Dimakis A, Biberacher M, Dominguez J, Fiorese G, Gadocha S, Gnansounou E, Guariso G, Kartalidis A, Panichelli L, Pinedo I, Robba M (2011) Methods and tools to evaluate the availability of renewable energy sources. Renew Sustain Energy Rev 15:1182–1200

    Article  Google Scholar 

  • Arnesano M, Carlucci AP, Laforgia D (2012) Extension of portfolio theory application to energy planning problem—The Italian case. Energy 39(1):112–124

    Article  Google Scholar 

  • Ayoub N, Elmoshi E, Seki H, Naka Y (2009) Evolutionary algorithms approach for integrated bioenergy supply chains optimization. Energy Convers Manage 50(12):2944–2955

    Article  Google Scholar 

  • Bergey PK, Ragsdale CT, Hoskote M (2003) A simulated annealing genetic algorithm for the electrical power districting problem. Ann Oper Res 121:33–55

    Article  MathSciNet  MATH  Google Scholar 

  • Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34(9):1413–1421

    Article  Google Scholar 

  • Bitzer B, Papazoglou TM, Rosser F, (1997) Intelligent Load Forecasting for the Electrical Power System on Crete, Proceedings 33rd Universities Power Engineering Conference (UPEC), pp 891–894

    Google Scholar 

  • BNEF (2012) The future of energy 2012 Results Book befsummit.com: Bloomberg New Energy Finance

    Google Scholar 

  • Born FJ (2001) Aiding renewable energy integration through complimentary demand-supply matching. Ph.D. Thesis, University of Strathclyde, France

    Google Scholar 

  • Bourjolly JM, Laporte G, Rousseau JM (1981) Decoupage electoral automatise: Application a I’Ile de Montreal. INFOR 19:113–124

    Google Scholar 

  • Bozkaya B, Erkut E, Laporte G (2003) A tabu search heuristic and adaptive memory procedure for political districting. Eur J Oper Res 144(1):12–26

    Article  MATH  Google Scholar 

  • Chen WB, Liu WC, Huang LT (2012) Measurement of sediment oxygen sa for modeling the dissolved oxygen distribution in a Subalpine lake. Int J Phys Sci 7(27):5036–5048

    Google Scholar 

  • Choi KH, Ang BW (2001) A time-series analysis of energy-related carbon emissions in Korea. Energy Policy 29(13):1155–1161

    Article  Google Scholar 

  • Cormio C, Dicorato M, Minoia A, Trovato M (2003) A regional energy planning methodology including renewable energy sources and environmental constraints. Renew Sustain Energy Rev 7(2):99–130

    Article  Google Scholar 

  • CRA (2005) Primer on demand-side management, with an emphasis on price-responsive programs. Charles River Associates California (USA)

    Google Scholar 

  • Dagdougui H, Ouammia A, Sacile R (2011) A regional decision support system for onsite renewable hydrogen production from solar and wind energy sources. Int J Hydrogen Energy 36:14324–14334

    Article  Google Scholar 

  • Drozdz M (2003) An optimisation model of geothermal-energy conversion. Appl Energy 74(1–2):75–84

    Article  Google Scholar 

  • Easingwood C (1973) Heuristic approach to selecting sales regions and territories. Oper Res Q 24:527–534

    Article  Google Scholar 

  • EPDK (2010) Elektrik Piyasası Raporu 2010, Enerji piyasası düzenleme kurumu, Turkey

    Google Scholar 

  • Fleischrnann B, Paraschis JN (1988) Solving a Large Scale Districting Problern: A Case Report. Computers and Operations Research 15:521–533

    Google Scholar 

  • Forman SL, Yue Y (2003) Congressional districting using a TSP–based genetic algorithm. In: Cant u-Paz E et al (eds) Genetic and evolutionary computation—GECCO 2003. Genetic and evolutionary computation conference, Chicago, IL, (USA)

    Google Scholar 

  • Forrest E (1964) Apportionment by computer. Am Behav Sci 23(7):23–35

    Article  Google Scholar 

  • Frei CW, Haldi PA, Sarlos G (2003) Dynamic formulation of a top-down and bottom-up merging energy policy model. Energy Policy 31(10):1017–1031

    Article  Google Scholar 

  • George JA, Larnar BW, Wallace CA (1997) Political district determination using large-scale network optimization. Socio-Econ Plann Sci 31(11):28

    Google Scholar 

  • Giatrakos GP, Tsoutsos TD, Mouchtaropoulos PG, Naxakis GD, Stavrakakis G (2009) Sustainable energy planning based on a stand-alone hybrid renewableenergy/hydrogen power system: Application in Karpathos island, Greece. Renewable Energy 34(12):2562–2570

    Article  Google Scholar 

  • Hess SW, Weaver JB, Siegfeldt HJ, Whelan JN, Zitlau PA (1965) Nonpartisan political redistricting by computer. Oper Res 13:998–1008

    Article  Google Scholar 

  • Hess SW, Samuels SA (1971) Experiences with a sales districting model: criteria and implementation. Manage Sci 18:41–54

    Article  Google Scholar 

  • Hiremath RB, Kumar B, Balachandra P, Ravindranath NH (2010) Bottom-up approach for decentralised energy planning: case study of Tumkur district in India. Energy Policy 38(2):862–874

    Article  Google Scholar 

  • Howick RS, Pidd M (1990) Sales force deployment models. Eur J Oper Res 48(295):310

    Google Scholar 

  • Hutchingson A (2011) The new energy fixes: 10 fixes. Popular mechanics. June 2011: 73. Print

    Google Scholar 

  • IEA (2005) Energy statistics manual. International Energy Agency

    Google Scholar 

  • IEA (2010) Trends in photovoltaic applications—Survey report of selected IEA countries between 1992 and 2009. International Energy Agency

    Google Scholar 

  • IPCC (2012) Special report of the intergovernmental panel on climate change. In: Ottmar Edenhofer O, Madruga RP, Sokona Y, Seyboth K, Eickemeier P, Matschoss P, Hansen G, Kadner S, Schlömer S, Zwickel T, Von Stechow C (eds) Renewable energy sources and climate change mitigation. Intergovernmental panel on climate change

    Google Scholar 

  • Kalcsics J, Melo T, Nickel S, H Gundra (2001) Planning sales territories—A facility location approach. Operations Research Proceedings 2001, pp 141–148

    Google Scholar 

  • Kalcsics J, Nickel S, Schrsder M (2005) "Towards a Unified Territorial Design Approach - Applications, Algorithms and GIS Integration." Sociedad de Estadlstica e Investigación Operativa Top 13(1):1–74

    Google Scholar 

  • Kermanshahi B, Akiyama Y, Yokoyama R, Asari M, Takahashi K (1997) Recurrent Neural Network for Forecasting Next 10 Years Loads of 9 Japanese Utilities, Proceedings 33rd Universities Power Engineering Conference (UPEC)

    Google Scholar 

  • Lin QG, Huang GH (2010) An inexact two-stage stochastic energy systems planning model for managing greenhouse gas emission at a municipal level. Energy 35(5):2270–2280

    Article  Google Scholar 

  • Lodish LM (1975) Sales territory alignment to maximize profit. J Mark Res 12:30–36

    Article  Google Scholar 

  • Lodish LM (1976) Assigning salesmen to accounts to maximize profit. J Mark Res 8:440–444

    Article  Google Scholar 

  • Marlin PG (1981) Application of the transportation model to a large scale districting problem. Comput Oper Res 8:83–96

    Article  Google Scholar 

  • Matthews RW (2001) Modelling of energy and carbon budgets of wood fuel coppice systems. Biomass Bioenergy 21(1):1–19

    Article  MathSciNet  Google Scholar 

  • Mazur A (1994) How does population growth contribute to rising energy consumption in America? Popul Environ: J Interdiscip Stud 15(5):371–378

    Article  Google Scholar 

  • Midilli A, Dincer I, Ay M (2006) Green energy strategies for sustainable development. Energy Policy 34(18):3623–3633

    Article  Google Scholar 

  • MENR, 2010. Ministry of energy and natural resources, http://www.enerji.gov.tr.

  • NEMMCO (2000) Operating Procedure: Load Forecasting, Document Number: SO_OP3710, March 2000, URL: http://www.nemmco.com.au/operating/systemops/so_op522v004.pdf

  • Ogston E, Zeman A, Prokopenko M, James G (2007) Clustering distributed energy resources for large-scale demand management. First international conference on self-adaptive and self-organizing systems, IEEE

    Google Scholar 

  • Özveren CS, Fayall L, Birch AP (1997) A Fuzzy Clustering and Classification Technique For Customer Profiling. Proceedings of the 32nd University Power Engineering Conference. pp 906–909

    Google Scholar 

  • Papadopoulos A, Karagiannidis A (2008) Application of the multi-criteria analysis method Electre III for the optimisation of decentralised energy systems. Omega 36(5):766–776

    Article  Google Scholar 

  • Parikh JK, Painuly JP (1994) Population, consumption patterns and climate change: A socioeconomic perspective from the South. Ambio 23(7):434–437

    Google Scholar 

  • Persaud AJ, Kumar U (2001) An eclectic approach in energy forecasting: a case of Natural Resources Canada’s (NRCan’s) oil and gas outlook. Energy Policy 29(4):303–313

    Article  Google Scholar 

  • Poggi P, Muselli M, Notton G, Cristofari C, Louche A (2003) Forecasting and simulating wind speed in Corsica by using an autoregressive model. Energy Convers Manage 44(20):3177–3196

    Article  Google Scholar 

  • Ricca F, Simeone B (1997) Political districting: traps, criteria, algorithms and trade off. Ricerca Operativa AIRO 27(81):119

    Google Scholar 

  • Ricca F (1996) Algorthmi di ricerca locale per la distrettizzazione elettorale. Atti Giornate AIRO 634–637

    Google Scholar 

  • Sadeghi M, Hosseini HM (2008) Integrated energy planning for transportation sector—A case study for Iran with techno-economic approach. Energy Policy 36(2):850–866

    Article  Google Scholar 

  • Saito H, McKenna SA, Zimmerman DA, Coburn TC (2005) Geostatistical interpolation of object counts collected from multiple strip transects: Ordinary kriging versus finite domain kriging. Stoch Env Res Risk Assess 19(1):71–85

    Article  MATH  Google Scholar 

  • Scott JA, Ho W, Dey PK (2012) A review of multi-criteria decision-making methods for bioenergy systems. Energy 42(1):146–156

    Article  Google Scholar 

  • Shafie-Khah M, Moghaddam MP, Sheikh-El-Eslami MK (2011) Price forecasting of day-ahead electricity markets using a hybrid forecast method. Energy Convers Manage 52(5):2165–2169

    Article  Google Scholar 

  • Shen YC, Chou CJ, Lin GTR (2011) The portfolio of renewable energy sources for achieving the three E policy goals. Energy 36(5):2589–2598

    Article  Google Scholar 

  • Shrestha, Lie TT, (1993) Qualitative use of Forecast Variables in Hybrid Load Forecasting Techniques, IEE 2nd International Conference in Power System Control, Operation and Management

    Google Scholar 

  • Silva Herran D, Nakata T (2012) Design of decentralized energy systems for rural electrification in developing countries considering regional disparity. Appl Energy 91(1):130–145

    Article  Google Scholar 

  • Terrados J, Almonacid G, Perez-Higueras P (2009) Proposal for a combined methodology for renewable energy planning. Application to a Spanish region. Renew Sustain Energy Rev 13:2022–2030

    Article  Google Scholar 

  • Tsoutsos T, Drandaki M, Frantzeskaki N, Iosifidis E, Kiosses I (2009) Sustainable energy planning by using multi-criteria analysis application in the island of Crete. Energy Policy 37(5):1587–1600

    Article  Google Scholar 

  • Weber C, Koyama M, Kraines S (2006) CO2-emissions reduction potential and costs of a decentralized energy system for providing electricity, cooling and heating in an office-building in Tokyo. Energy 31(14):3041–3061

    Article  Google Scholar 

  • Weisser D (2003) A wind energy analysis of Grenada: an estimation using the ‘Weibull’ density function. Renewable Energy 28(11):1803–1812

    Article  Google Scholar 

  • Wiser R, Bolinger M (2007) Annual report on U.S. Wind power installation, cost, and performance trends: 2007. Lawrence Berkeley National Laboratory

    Google Scholar 

  • Wüstenhagen R, Menichetti E (2012) Strategic choices for renewable energy investment: conceptual framework and opportunities for further research. Energy Policy 40:1–10

    Article  Google Scholar 

  • Yüksel İ, Kaygusuz K (2011) Renewable energy sources for clean and sustainable energy policies in Turkey. Renew Sustain Energy Rev 15:4132–4144

    Article  Google Scholar 

  • Zoltners AA, Sinha P (1983) Sales territory alignment: a review and model. Manage Sci 29:1237–1256

    Article  MATH  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Seda Uğurlu .

Editor information

Editors and Affiliations

Rights and permissions

Reprints and permissions

Copyright information

© 2013 Springer-Verlag London

About this chapter

Cite this chapter

Uğurlu, S., Öztayşi, B., Kahraman, C. (2013). Territorial Design for Matching Green Energy Supply and Energy Consumption: The Case of Turkey. In: Cavallaro, F. (eds) Assessment and Simulation Tools for Sustainable Energy Systems. Green Energy and Technology, vol 129. Springer, London. https://doi.org/10.1007/978-1-4471-5143-2_6

Download citation

  • DOI: https://doi.org/10.1007/978-1-4471-5143-2_6

  • Published:

  • Publisher Name: Springer, London

  • Print ISBN: 978-1-4471-5142-5

  • Online ISBN: 978-1-4471-5143-2

  • eBook Packages: EnergyEnergy (R0)

Publish with us

Policies and ethics