Model Documentation - BLUES

From IAMC-Documentation
Revision as of 12:34, 21 September 2017 by Alexandre Koberle (talk | contribs) (Created page with "{{ModelDocumentationTemplate |IsEmpty=No |IsDocumentationOf=IMAGE |DocumentationCategory=Model Documentation |HasLevel=0 |HasSeq=0 }} The Brazilian Land Use and Energy System...")
(diff) ← Older revision | Latest revision (diff) | Newer revision → (diff)
Jump to navigation Jump to search

Model Documentation - BLUES

Corresponding documentation
Previous versions
Model information
Model link
Institution COPPE/UFRJ (Cenergia), Brazil, http://www.cenergialab.coppe.ufrj.br/.
Solution concept General equilibrium (closed economy)
Solution method Optimization
Anticipation

The Brazilian Land Use and Energy System (BLUES) model is a perfect-foresight, least-cost optimization model for Brazil. It chooses the energy system configuration with the least total system cost over the entire time horizon of the study, in this case 2010 to 2050. The model minimizes costs of the entire energy system, including electricity generation, agriculture, industry, transport and the buildings sectors. BLUES finds optimized mixes for the energy system as a whole, rather than evaluating sectorial optimal solutions. It includes CO2, CH4 and N2O emissions associated with land use, agriculture and livestock, fugitive emissions, fuel combustion, industrial processes and waste treatment.

BLUES has six native regions. One main overarching region into which five sub-regions are nested following the geopolitical division of the country. The energy system is represented in detail across sectors, with over 1500 technologies available in and customized for each of its six native regions. The representation of the land-use system includes forests, savannas, low- and high-capacity pastures, integrated systems, cropland, double cropping, planted forests, and protected areas. Cropland is made up of Land useis also regionalized and customized for each subregion, with yields and costs varying from region to region. Demand is exogenous but endogenous energy efficiency measures permit demand responses through technological options.