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2010 | 11(XI) | nr 1 | 24--36
Tytuł artykułu

Minimizing Carbon Footprint of Biomass Energy Supply Chain in the Province of Florence

Treść / Zawartość
Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
The paper presents an approach for optimal planning of biomass energy system based on carbon footprint minimization. A geographical spatial demand driven approach is applied to assess the feasible ways for transferring energy from renewable sources to district heating plants in the Province of Florence (Italy). The proposed approach has been developed on three levels. In the first one, the Province of Florence is partitioned into a number of Regional Energy Cluster (REC) using a multidimensional algorithm of regionalization called SKATER. The variables used in SKATER model are related in order to realize sustainable policy for forest and agriculture biomass productions. In the second step a geographical fuzzy multiple attribute decision making model was applied to the selection of biomass district heating localization. Finally, in the third step a geo-referenced Mixed Integer Linear Programming model based on resource-supply-demand structure for carbon-minimization energy planning has been applied. (original abstract)
Twórcy
  • University of Florence, Italy
  • University of Florence, Italy
  • University of Florence, Italy
Bibliografia
  • Anderson G.Q.A., Fergusson, M.J. (2006). Energy from biomass in the UK: sources, processes and biodiversity implications. Ibis, 148, 180-183.
  • Assunçao R.M., Neves M.C., Camara, G., Da Costa Freitas C., (2006). Efficient regionalization techniques for socio-economic geographical units using minimum spanning trees. International Journal of Geographical Information Science, 20, 797-811.
  • Bernetti I., Fagarazzi C., Fratini R., (2004). A methodology to analyze the potential development of biomass energy sector: an application in Tuscany. Forest Policy and Economic, 6, 415-432.
  • Cox E., (1993). The fuzzy system handbook. Academic Press, London.
  • Forsberg G. (2000). Biomass energy transport. Analysis of bioenergy transport chains using life cycle inventory method. Biomass and Bioenergy, 19, 17-30.
  • Guo D., (2008). Regionalization with dynamically constrained agglomerative clustering and partitioning (REDCAP). International Journal of Geographical Information Science, 22, 801-823.
  • ISTAT (2001). VIII° Censimento sull'Industria e i Servizi. Available at: www.istat.it
  • ISTAT (2008). Bilancio demografico 2008. Available at: http://demo.istat.it/
  • Lam H.L., Varbanov P., Klemeš J., (2010a). Optimization of regional energy supply chains utilising renewables: P-graph approach. Computers and Chemical Engineering, 34, 782-792.
  • Lam H.L., Varbanov P., Klemeš J., (2010b). Minimising carbon footprint of regional biomass supply chains. Resources, Conservation & Recycling, 54, 303-309.
  • Munda G., (1995). Multicriteria evaluation in a fuzzy environment. Theory and application in ecological economics. Springer-Verlag. Heidelberg.
  • Noon C.E., Daly J.M., (1996). GIS-based biomass resource assessment with BRAVO. Biomass and Bioenergy 10, 101-9.
  • Noon C.E., Zhan F.B., Graham R.L., (2002). GIS-based analysis of marginal price variation with an application in the identification of candidate ethanol conversion plant locations. Networks and Spatial Economics, 2, 79-93.
  • Nord-Larsen T., Talbot B., (2004). Assessment of forest-fuel resources in Denmark: technical and economic availability. Biomass and Bioenergy, 27, 97-109.
  • Panichelli L., Gnansounou E., (2008). GIS-based approach for defining bioenergy facilities location: a case study in northern Spain based on marginal delivery costs and resources competition between facilities. Biomass and Bioenergy, 32, 289-300.
  • Perry S., Klemeš J., Bulatov I., (2008). Integrating waste and renewable energy to reduce the carbon footprint of locally integrated energy sectors. Energy, 33, 1489-1497.
  • Ranta T. (2005) Logging residues from regeneration fellings for biofuel production - a GIS-based availability analysis in Finland. Biomass and Bioenergy, 28, 171-182.
  • Zimmermann H.J., (1987). Fuzzy sets, decision making and expert systems. Kluwer A.P., Boston.
Typ dokumentu
Bibliografia
Identyfikatory
Identyfikator YADDA
bwmeta1.element.ekon-element-000171237351

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