Climate Change and Energy Security in Brazil: Understanding the Impact of Climate Change on the Energy Sector Prof. Roberto Schaeffer (with Alexandre Szklo and Andre Lucena) Energy Planning Program, COPPE Federal University of Rio de Janeiro, Brazil World Bank Energy Week 2009
Introduction Renewable energy represent, on one hand, an important alternative for mitigating global climate change (GCC) On the other hand, because it is strongly dependent on climate conditions, renewable energy is vulnerable to the very problem it wants avoid
Introduction Brazil: strong dependence on renewable energy Renewable energy responsible for 46.4% of all energy consumed in the country in 2007 Hydro responsible for 85.6% of all electricity generated that same year Gross wind potential of 1.26TW, capable of generating more than 3,000TWh/yr compared to a consumption of some 450 TWh/yr as of today Ethanol responsible for 15.1% of all fuels consumed in the transportation sector, already surpassing gasoline
Impacts of GCC on the Supply and Demand of Energy in Brazil GCC can affect the production/consumption of energy by changing: Precipitation regimes hydro and biofuels production Temperature hydro (due to higher evaporation in reservoirs and greater competition for water use), thermal electricity efficiency, biofuels production and energy demand Wind regimes wind power potential What would be, then, the impact of GCC on the Brazilian energy system by 2035?
GCC Scenarios Utilized IPCC A2 and B2 emission scenarios Transformed in long term climate projections by CPTEC/INPE
Hydro - Methodology A2 and B2 climate scenarios: Precipitation and temperature Natural water flows to the hydro reservoirs New group of water flow series Montly series for 2025-2100 195 hydro plants of the Brazilian interconnected power system Operation model of the hydro system in the country SUISHI-O Firm energy (guaranteed) Capacity factor Average energy (average generation over time)
Example for 2071-2100: A2 scenario variation in relation to reference projections
Example for 2071-2100: B2 scenario variation in relation to reference projections
Hydro Results SUISHI-O Histórico Variação em Relação ao Cenário Referência Bacia MWmédio* A2 B2 E. Firme E. Média E. Firme E. Média E. Firme E. Média Amazonas 9425 10628-36% -11% -29% -7% Tocantins Araguaia 7531 10001-46% -27% -41% -21% São Francisco 5026 5996-69% -45% -77% -52% Parnaiba 236 293-83% -83% -88% -82% At. Leste 496 565-82% -80% -82% -80% At. Sudeste 1937 2268-32% 1% -37% -10% At. Sul 1739 2037-26% 8% -18% 11% Uruguai 1715 1996-30% 4% -20% 9% Paraguai 375 426-38% 4% -35% -3% Paraná 22903 29038-8% 43% -7% 37% TOTAL 51382 63247-31,5% 2,7% -29,3% 1,1% Nota: Com base na configuração do sistema projetada para 2016 (EPE, 2007b). *: MWmédio indica a quantidade de energia gerada supondo o fator de capacidade médio.
Hydro Results SUISHI-O Histórico A2 B2 Fator de Variação Fator de Variação Fator de Subsistema Capacidade vs Ref Capacidade vs Ref Capacidade S/SE/CO <30MW 58.0% -30.7% 40.2% -31.7% 39.7% entre 30 e 300MW 48.5% -34.9% 31.6% -33.9% 32.1% >300MW 44.6% -13.2% 38.7% -11.4% 39.5% N/NE <30MW 58.0% -25.2% 43.4% -15.8% 48.8% entre 30 e 300MW 42.4% -49.4% 21.5% -45.1% 23.3% >300MW 49.6% -48.5% 25.5% -45.9% 26.8%
Wind Energy - Methodology Wind speed projections 50x50km number of occurrences with average annual speeds higher than 6m/s (excluding preservation and aquifer areas) Gross wind potential: wind distribution vs. power curve Fonte: Dutra (2007)
Wind Energy - Methodology Baseline adjustment spatial compatibilization with the Brazilian Wind Atlas (CEPEL, 2001) and application of the average wind speed variations between the A2 and B2 scenarios projections and the baseline Assumptions for calculating the wind potential: No assumption with regard to changes in the distribution of wind speed with respect to its average Wind technology assumed to be kept fixed No changes in the rugosity of the terrain assumed
Wind Energy Results A2 2071-2080 A2 2071-2080 A2 Variação média < -20% Variação média >-20; <-15% Variação média >-15; <-10% Variação média >-10; <-5% Variação média >-5; <0% Variação média >0; < 5% Variação média >5; <10% Variação média >10; <15% Variação média >15; < 20% Variação média >20% 2081-2090 A2 2001 2081-2090 A2 Velocidade média < 6,0 Velocidade média >6,0; < 6,5 Velocidade média >6,5; < 7,0 Velocidade média >7,0; < 7,5 Velocidade média >7,5; < 8,0 Velocidade média >8,0; < 8,5 Velocidade média > 8,5 2091-2100 A2 2091-2100 A2
Wind Energy Results B2 2071-2080 B2 2071-2080 B2 Variação média < -20% Variação média >-20; <-15% Variação média >-15; <-10% Variação média >-10; <-5% Variação média >-5; <0% Variação média >0; < 5% Variação média >5; <10% Variação média >10; <15% Variação média >15; < 20% Variação média >20% 2081-2090 B2 2001 2081-2090 B2 Velocidade média < 6,0 Velocidade média >6,0; < 6,5 Velocidade média >6,5; < 7,0 Velocidade média >7,0; < 7,5 Velocidade média >7,5; < 8,0 Velocidade média >8,0; < 8,5 Velocidade média > 8,5 2091-2100 B2 2091-2100 B2
Wind Energy - Results Average wind speeds increase substantially in the coastal areas in general and in the North/Northeast regions in particular in both scenarios, mainly in the A2 scenario Average capacity factors by region and in Brazil as a whole increase as a function of a greater relative share of high speed winds As a result wind energy presents an even greater opportunity in the future
Thermal Power - Methodology Impact on the thermodynamic efficiency of natural gas turbines 103.0% 102.0% c y n 101.0% ie fic 100.0% l E a in 99.0% m o f N 98.0% o n 97.0% rtio o p 96.0% ro P 95.0% 94.0% 15º C 0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 oc Fonte: Szklo et al. (2000)
Thermal Power - Results Eficiência Operacional T média ( o C) Eficiência Nominal TGN-CA TGN-CC S/SE/CO N/NE TGN-CA TGN-CC S/SE/CO N/NE S/SE/CO N/NE 2005-2010 23.03 26.24 0.55 0.45 0.54 0.54 0.44 0.44 2011-2015 23.35 26.31 0.55 0.45 0.54 0.54 0.44 0.44 2016-2020 23.30 26.15 0.55 0.45 0.54 0.54 0.44 0.44 2021-2025 23.54 26.53 0.55 0.45 0.54 0.54 0.44 0.44 2026-2030 23.72 26.80 0.55 0.45 0.54 0.54 0.44 0.44 2031-2035 23.72 26.98 0.55 0.45 0.54 0.54 0.44 0.44
Liquid Biofuels Production Methodology Edafoclimatic conditions (temperature and precipitation) can impact the growth of biomass for biofuels production Results based on EMBRAPA (Pinto and Assad, 2008)
Liquid Biofuels Production Results Biodiesel two crops examined: soya and sunflower. Areas with low production risks will be reduced for both crops Lack of water in the Northeast may lead to a migration of both crops to the South of the country Ethanol sugarcane No negative impacts expected, although sugarcane may migrate to other regions
Electricity Demand - Methodology Increase in electricity consumption due to extra air conditioning Residential and Services Sectors 2 factors considered: Increase in the average temperature: COP effect Increase in the number of warm days: Degree-Days effect
Electricity Demand - Results Increase in the electricity consumptions of the appliances Ano Residencial Serviços Cenário A2 Cenário B2 Cenário A2 Cenário B2 2030 17% 13% 9% 4% 2035 20% 26% 12% 8% Increase in the electricity consumption of the sector Ano Residencial Serviços Cenário A2 Cenário A2 Cenário B2 Cenário B2 Cenário A2 Cenário A2 Cenário B2 Cenário B2 2030 3,0% 7.723 2,4% 5.452 3,1% 7.121 1,5% 3.612 2035 4,4% 13.085 6,1% 16.068 4,7% 12.464 3,0% 8.387
Impacts Conclusions Decrease in the reliability of the hydro system due to GCC Significant negative impact on hydro generation and biofuels production in the North/Northeast regions Gross wind potential positively affected Negligible impact on thermal power generation and on ethanol production from sugarcane Increase in the demand for electricity in the residential and services sectors due to expected higher temperatures GCC needs to be embeded in the planning of the energy sector from now on