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Integrated Planning


Applying pioneering integrated planning modeling tools, CenergiaLab supports energy and climate policymakers in understanding the synergies and trade-offs of technological innovation pathways towards a low-carbon transition in Brazil, in Latin America and in the world.


CenergiaLab team has developed integrated optimization energy models, especially with the MESSAGE platform since 2003 and, more recently, with TIMES and REMIX-CEM-B. Over time, these top-down integrated assessment models (IAMs) have been upgraded and tailored to better represent the Brazilian, Latin American countries and global regions. These models incorporate over 10,000 energy supply and demand technological options, and evaluate their interactions with economic development, climate change, local air pollution, water footprint, land use change and climate policies.


The IAMs developed in CenergiaLab are integrated with sectoral models to better understand specificities of energy planning from a bottom-up perspective. It also includes softlinks with other tools based on life cycle assessment (LCA), process analysis, econometrics and input-output matrix methods to identify interlinkages between energy planning and other multiple dimensions of sustainability.

Global Models

Global Models

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Computable Framework For Energy and the Environment

Platform: MESSAGE

  •  Perfect foresight linear programming optimization model

  •  Global model with 18 regions

Time Horizon:From 2010 to 2100, in 14 steps

  •  Includes all the Energy and the Land-use Systems

Main Purpose:

  • Assessment of potential synergies/trade-offs in energy, environmental and climate policies

  •  Completely integrated (hard-link) between energy and land

  •  First global IAM built in a developing country


Total Economy Assessment

Platform: GAMS

  • Recursive dynamic general equilibrium model

  • Global model with 18 regions

Time Horizon: From 2010 to 2100, in 14 steps

  • All economic sectors (GTAP database), with focus on Energy and Land-use Systems

Main purpose:

  • Assessment of climate change mitigation and economic impacts

  • Based on the frameworks of the GTAPinGAMS and EPPA models

  • Soft-linked with COFFEE and BLUES


National Models

National Models

Brazilian Land-Use and Energy Systems model

Platform: MESSAGE

  •  Perfect foresight linear programming optimization model

  •  Main Region: Brazil

  •  5 regions

Time Resolution: From 2010 to 2060 (11 steps), with 288 time steps (monthly/hourly) per year

  •  Includes all the Energy and the Land-use Systems

Main Purpose:

  •  Assessment of technology, energy, environmental and climate policies for Brazil

  •  Enhanced regional, sectorial and land-cover characterizations

  •  Very high-level of technological detail (bottom-up) with country-specific parameters


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Bioenergy and Land Optimization spatially Explicit Model

Platform: GAMS

  • Perfect foresight, spatially explicit, linear programming optimization model

  • BLOEM-Brazil: 2912 square grid cells of 50 km length

  • Currently applied to Brazil, but can be adapted to other countries/regions (according to data availability)

Time Resolution:

  • BLOEM-Brazil: From 2010 to 2050, 10-year time steps

  • Easily adaptable to other time resolutions: 1-year step, 5-year steps, etc

Main Purpose:

  • Identify and quantify bioenergy pathways in terms of: costs, feedstocks and optimal location of conversion units, logistical constraints and system expansion projections, and the corresponding emissions implications

  • Evaluate the role of bioenergy in mitigation strategies and its interactions with the land system, and provide contributions to long-term climate policy design


Ecuador Land Use and Energy Network Analysis Model

Platform: MESSAGE

  •  Perfect foresight linear programming optimization model

  • Main Region: Ecuador

  •  4 regions

Time Resolution: from 2015 to 2050 (8 steps), with 60 time steps (monthly with 5-periods daily) per year

  •  Includes all the Energy and the Land-use Systems

Main Purpose:Assessment of energy and climate policies for Ecuador

  •  Based on the BLUES platform, with specific considerations for Ecuador

  •  Suitable for assessing long-term scenarios


  • Currently in development (under review). Developer: Rafael Soria.

Image by Thomas Richter
Sectoral Models

Sectoral Models

Oil Refining Industry Optimization and syNergies
Multi-regional optimization model for refining industries

Platform: GAMS

  •  MIP optimization model

  • Regions:Custom (Default: multi-regional for Brazil)

Time Resolution: From 2015 to 2040

Main Purpose:

  • Optimization of refinery capacity expansion, production profile and GHG emissions

  •  High level of technological detail in refining processes

  •  Details fourteen processing units, Hydrogen generation and Cogeneration units

  •  Considers three crude oil options and nine types of oil derivatives


Short-Term power system OpeRation Model
Optimization dispatch model for power sector

Platform: GAMS

  •  Deterministic optimization model (with Robust optimization option)

  • Regions:Custom (Default: Brazilian sub-systems)

Time Resolution: Custom (Default: hourly resolution)

Main Purpose:

  • Dispatch of power sector, dealing with hydrothermal Unit Commitment

  •  Deals with the hydrothermal Unit Commitment problem

  •  Considers technical restrictions of power plants, as well as transmission capacity limitations

  •  Modelling of wind generation and hydro production cascade effects


  • Viviescas Latorre C. C., 2019. "Robust optimization applied to a hydrothermal unit commitment problem with dispatchable wind power". DSc thesis, Programa de Planejamento Energético, COPPE/UFRJ.

Image by Charlotte Venema
Carbon and Energy Strategy Analysis for Refineries
Simulation of oil refineries with focus on energy efficiency and carbon emission

Platform: Excel

  •  Simulation, with LP optimization option for energy consumption and mitigation of GHG

  • Regions:Custom single region (eg: single refinery, refinery block, country)

Time Resolution:5 time steps

Main Purpose:

  • Assessment of production profile, energy and consumption, and GHG emissions

  •  Includes fifteen processing units with estimated utilities and water consumption coefficients

  •  Includes over 200 energy efficiency and mitigation options, such as CCS


Historical tRends for Mobility assESsmentT
Multinomial logit model to simulate passenger transportation demand

Platform: RStudio

  • Simulation, with travel time constraint

  • Regions:Custom (Default: Brazilian, European Union, Japan and United States)

Time Resolution: Custom (Default: year resolution)

Main Purpose:

  • Assess the per capita and total transportation trends

  • Explore how mode share trends in the transportation sector may evolve

  • Assess the impact of the informal economy on demand estimation

  • Conduct scenario exercises to look at medium- to longer-term alternative scenarios and their impact on achieving travel time budget goals



Model for Evaluation of Solar Heat
Optimization Model for Solar Thermal Energy Dimensioning

Platform: GAMS

  • Mixed-Interger Nonlinear Programming

  • Regions: Custom single region (defined by solar resource input) 

Time Resolution: hourly 

Main Purpose:

  • Definition of optimal solar collector, heat transfer fluid and solar field configuration

  • Optimization of heat sources dispatch

  • Considers hourly variability of solar resource and collector efficiency

  • Modelling of solar collector and heat transfer fluid as function of solar irradiance and working temperature


Agent-based Model of beef consumption in Brazil
Agent-based model of food choices and behavioural drivers in
Evaluates beef consumption habits under different price shocks and environmental
interactions of agents.
ABM 2.jpg

Platform: NetLogo

  • Simulates red meat consumption in Brazil between 2017 and 2020

  • Time Resolution: daily


Main Purpose:

  • Simulation of individual behaviours in relation to beef consumption in Brazil

  • Capture the heterogeneity and irrationality of consumers when modeling changes in demand for sustainable diets 

  • Analyse the impact of policy interventions, specifically price increases, on different segments of the population


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