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.
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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
Amarawickrama HA, Hunt LC (2008) Electricity demand for Sri Lanka: a time series analysis. Energy 33(5):724–739
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
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
Arnesano M, Carlucci AP, Laforgia D (2012) Extension of portfolio theory application to energy planning problem—The Italian case. Energy 39(1):112–124
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
Bergey PK, Ragsdale CT, Hoskote M (2003) A simulated annealing genetic algorithm for the electrical power districting problem. Ann Oper Res 121:33–55
Bianco V, Manca O, Nardini S (2009) Electricity consumption forecasting in Italy using linear regression models. Energy 34(9):1413–1421
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
BNEF (2012) The future of energy 2012 Results Book befsummit.com: Bloomberg New Energy Finance
Born FJ (2001) Aiding renewable energy integration through complimentary demand-supply matching. Ph.D. Thesis, University of Strathclyde, France
Bourjolly JM, Laporte G, Rousseau JM (1981) Decoupage electoral automatise: Application a I’Ile de Montreal. INFOR 19:113–124
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
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
Choi KH, Ang BW (2001) A time-series analysis of energy-related carbon emissions in Korea. Energy Policy 29(13):1155–1161
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
CRA (2005) Primer on demand-side management, with an emphasis on price-responsive programs. Charles River Associates California (USA)
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
Drozdz M (2003) An optimisation model of geothermal-energy conversion. Appl Energy 74(1–2):75–84
Easingwood C (1973) Heuristic approach to selecting sales regions and territories. Oper Res Q 24:527–534
EPDK (2010) Elektrik Piyasası Raporu 2010, Enerji piyasası düzenleme kurumu, Turkey
Fleischrnann B, Paraschis JN (1988) Solving a Large Scale Districting Problern: A Case Report. Computers and Operations Research 15:521–533
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)
Forrest E (1964) Apportionment by computer. Am Behav Sci 23(7):23–35
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
George JA, Larnar BW, Wallace CA (1997) Political district determination using large-scale network optimization. Socio-Econ Plann Sci 31(11):28
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
Hess SW, Weaver JB, Siegfeldt HJ, Whelan JN, Zitlau PA (1965) Nonpartisan political redistricting by computer. Oper Res 13:998–1008
Hess SW, Samuels SA (1971) Experiences with a sales districting model: criteria and implementation. Manage Sci 18:41–54
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
Howick RS, Pidd M (1990) Sales force deployment models. Eur J Oper Res 48(295):310
Hutchingson A (2011) The new energy fixes: 10 fixes. Popular mechanics. June 2011: 73. Print
IEA (2005) Energy statistics manual. International Energy Agency
IEA (2010) Trends in photovoltaic applications—Survey report of selected IEA countries between 1992 and 2009. International Energy Agency
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
Kalcsics J, Melo T, Nickel S, H Gundra (2001) Planning sales territories—A facility location approach. Operations Research Proceedings 2001, pp 141–148
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
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)
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
Lodish LM (1975) Sales territory alignment to maximize profit. J Mark Res 12:30–36
Lodish LM (1976) Assigning salesmen to accounts to maximize profit. J Mark Res 8:440–444
Marlin PG (1981) Application of the transportation model to a large scale districting problem. Comput Oper Res 8:83–96
Matthews RW (2001) Modelling of energy and carbon budgets of wood fuel coppice systems. Biomass Bioenergy 21(1):1–19
Mazur A (1994) How does population growth contribute to rising energy consumption in America? Popul Environ: J Interdiscip Stud 15(5):371–378
Midilli A, Dincer I, Ay M (2006) Green energy strategies for sustainable development. Energy Policy 34(18):3623–3633
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
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
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
Parikh JK, Painuly JP (1994) Population, consumption patterns and climate change: A socioeconomic perspective from the South. Ambio 23(7):434–437
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
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
Ricca F, Simeone B (1997) Political districting: traps, criteria, algorithms and trade off. Ricerca Operativa AIRO 27(81):119
Ricca F (1996) Algorthmi di ricerca locale per la distrettizzazione elettorale. Atti Giornate AIRO 634–637
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
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
Scott JA, Ho W, Dey PK (2012) A review of multi-criteria decision-making methods for bioenergy systems. Energy 42(1):146–156
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
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
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
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
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
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
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
Weisser D (2003) A wind energy analysis of Grenada: an estimation using the ‘Weibull’ density function. Renewable Energy 28(11):1803–1812
Wiser R, Bolinger M (2007) Annual report on U.S. Wind power installation, cost, and performance trends: 2007. Lawrence Berkeley National Laboratory
Wüstenhagen R, Menichetti E (2012) Strategic choices for renewable energy investment: conceptual framework and opportunities for further research. Energy Policy 40:1–10
Yüksel İ, Kaygusuz K (2011) Renewable energy sources for clean and sustainable energy policies in Turkey. Renew Sustain Energy Rev 15:4132–4144
Zoltners AA, Sinha P (1983) Sales territory alignment: a review and model. Manage Sci 29:1237–1256
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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
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