Evaluation of alternative future energy scenarios for Brazil using an energy mix model

The purpose of this study is to model and assess the performance and the emissions impacts of electric energy technologies in Brazil, based on selected economic scenarios, for a time frame of 40 years, taking the year of 1995 as a base year.; A Base scenario has been developed, for each of three economic development projections, based upon a sectoral analysis. Data regarding the characteristics of over 300 end-use technologies and 400 energy conversion technologies have been collected. The stand-alone MARKAL technology-based energy-mix model, first developed at Brookhaven National Laboratory, was applied to a base case study and five alternative case studies, for each economic scenario.; The alternative case studies are: (1)  minimum increase in the thermoelectric contribution to the power production system of 20 percent after 2010; (2) extreme values for crude oil price; (3) minimum increase in the renewable technologies contribution to the power production system of 20 percent after 2010; (4) uncertainty on the cost of future renewable conversion technologies; and (5) model is forced to use the natural gas plants committed to be built in the country.; Results such as the distribution of fuel used for power generation, electricity demand across economy sectors, total CO2 emissions from burning fossil fuels for power generation, shadow price (marginal cost) of technologies, and others, are evaluated and compared to the Base scenarios previous established.; Among some key findings regarding the Brazilian energy system it may be inferred that: (1) diesel technologies are estimated to be the most cost-effective thermal technology in the country; (2) wind technology is estimated to be the most cost-effective technology to be used when a minimum share of renewables is imposed to the system; and (3) hydroelectric technologies present the highest cost/benefit relation among all conversion technologies considered. These results are subject to the limitations of key input assumptions and key assumptions of modeling framework, and are used as the basis for recommendations regarding energy development priorities for Brazil.