Minimizing carbon footprint of biomass energy supply chain in the Province of Florence
Minimizing carbon footprint of biomass energy supply chain in the Province of Florence

Summary/Abstract: 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 georeferenced Mixed Integer Linear Programming model based on resourcesupply- demand structure for carbon-minimization energy planning has been applied.

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