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Adaptação às mudanças climáticas no Brasil: cenários e alternativas
Coordenação: Roberto Schaeffer, André Lucena, Alexandre Szklo, Rafael Kelman
Equipe: Bruno Borba, Pedro Rochedo, Larissa Nogueira , Alexandre Koberle, Pedro Avila, Tainá Cunha e Bernardo Bezerra
2015
Adaptação às mudanças climáticas no Brasil: cenários e alternativas
▪ Introdução
▪ Ferramentas e Premissas
▪ Metodologia
▪ Resultados
Adaptação às mudanças climáticas no Brasil: cenários e alternativas
Introdução
Introdução
▪ Projeto contratado pela Secretaria de Assuntos Estratégicos da Presidência da República (SAE-‐PR) em 2015 ▪ Colaboração COPPE-‐PSR
▪ Objetivos: ▪ Quais são os impactos das mudanças climáticas no Sistema interligado
brasileiro? ▪ Quais seriam as melhoras alternativas para compensar a redução de
geração hidroelétrica? ▪ Melhor estratégia de adaptação: operação vs expansão?
▪ Como políticas de mitigação podem afetar estas alternativas?
http://www.ons.org.br/
Sistema Interligado Nacional (SIN)
Sazonalidade hidrológica no Brasil(Séria histórica 1931 – 2009)
Source: Silva (2012)
Adaptação às mudanças climáticas no Brasil: cenários e alternativas
Ferramentas e Premissas
MESSAGE-Brasil 300
▪ Modelo de otimização intertemporal (perfect foresight) ▪ 5 níveis energéticos (+2 intermediários):
▪ Recursos: 4 (não renováveis) ▪ Energia Primária: 8 ▪ Energia Secundária: 18 ▪ Energia Final: 20 ▪ Energia Útil/Serviço Energético: 22 demandas
▪ Cerca de 300 tecnologias de conversão ▪ Ano base: 2010 ▪ Horizonte: 2010-‐2050 em períodos de 5 anos
Model for Energy Supply Systems and their General Environmental Impacts
SDDP
▪ SDDP é um modelo de despacho hidrotérmico estocástico; ▪ Representação da rede de transmissão para estudos de
operação de longo, médio e curto prazo; ▪ Calcula a política operativa de mínimo custo de um sistema
hidrotérmico; ▪ Utilizado em estudos de planejamento operativo em mais de
30 países, incluindo: ▪ Todos os países das Américas Central e do Sul; EUA e Canadá; Áustria,
Espanha, Noruega Balcãs (10 países) e Turquia; Nova Zelândia, China e Shangai.
Premissas
▪ Cenário Macroeconômico: EPE (2014)
Premissas
▪ População: IBGE (2013)
Premissas
▪ General Circulation Models – GCM ▪ MIROC5 (menor impacto) ▪ HadGEM2-‐ES (maior impacto)
▪ Downscaling pelo modelo ETA (Chou et al., 2014)
▪ Modelos climáticos globais foram usados para gerar cenários hidrológicos para as bacias hidrográficas brasileiras ▪ Grupo de recursos hídricos do projeto (FUNCENE-‐UNB)
Premissas
▪ Cenários Climáticos -‐ Representative Climate Pathways (RCP) ▪ RCP 8.5: Assume nenhum esforço adicional para reduzir as emissões de
GEE do setor energético nacional ▪ RCP 4.5: Esforços nacionais para mitigar emissões de GEE ▪ Emissões brasileiras podem ter pouco peso nas emissões globais
Cenário Preço de Carbono 2020 2030 2040RCP 4.5 (2005)$/tCO2e 50 74 110
ETP, 2015
Adaptação às mudanças climáticas no Brasil: cenários e alternativas
Metodologia
Procedimento MetodológicoModelagem Climática e Hidrológica
Projected Riverflow
Projected Riverflow
Scenario Premisses (RCP 4.5 and 8.5)
Operation Model SDDP (PSR)
Expansion Model MESSAGE-‐Brazil
(COPPE)
Operation/ Expansion
Operation Impacts/ adaptation
Adaptation through expansion
Operation
GCM – Dow
scaling – Hy
drology Mod
el
195 riv
erflo
w se
ries
•Hydro generation scenarios •Marginal cost of operation •Deficit probability
•Capacity expansion •Investment costs •Second order effects
Procedimento MetodológicoModelagem Energética
Adaptação às mudanças climáticas no Brasil: cenários e alternativas
Resultados
Somente resultados do HadGEM, com maior impacto hidrológico, serão apresentados.
Scenario Premisses (RCP 4.5 and 8.5)
Operation Model SDDP (PSR)
Expansion Model MESSAGE-‐Brazil
(COPPE)
Operation/ Expansion
Operation Impacts/ adaptation
Adaptation through expansion
Operation
GCM – Dow
scaling – Hy
drology Mod
el
195 riv
erflo
w se
ries
•Hydro generation scenarios •Marginal cost of operation •Deficit probability
•Capacity expansion •Investment costs •Second order effects
Procedimento Metodológico
Scenario Premisses (RCP 4.5 and 8.5)
Operation Model SDDP (PSR)
Expansion Model MESSAGE-‐Brazil
(COPPE)
Operation/ Expansion
Operation Impacts/ adaptation
Adaptation through expansion
Operation
GCM – Dow
scaling – Hy
drology Mod
el
195 riv
erflo
w se
ries
•Hydro generation scenarios •Marginal cost of operation •Deficit probability
•Capacity expansion •Investment costs •Second order effects
Procedimento MetodológicoLinha de Base
Baseline Scenarios
▪ Baseline scenarios: RCP comparison – Electricity
GW year
0.0
35.0
70.0
105.0
140.0
2010 2015 2020 2025 2030 2035 2040
Hydro Coal Gas OilNuclear Biomass Wind Solar
RCP 8.5 •BAU expansion – no explicit mitigation assumed
RCP 4.5 •Energy efficiency •Lower fossil expansion •100$/tCO2 Carbon cost (ETP, 2015)
Baseline Scenarios
▪ Baseline scenarios: RCP comparison – GHG Emissions
Scenario Premisses (RCP 4.5 and 8.5)
Operation Model SDDP (PSR)
Expansion Model MESSAGE-‐Brazil
(COPPE)
Operation/ Expansion
Operation Impacts/ adaptation
Adaptation through expansion
Operation
GCM – Dow
scaling – Hy
drology Mod
el
195 riv
erflo
w se
ries
•Hydro generation scenarios •Marginal cost of operation •Deficit probability
•Capacity expansion •Investment costs •Second order effects
Procedimento MetodológicoAdaptação/Impacto da Operação
Results - Operation
▪ Hydropower Generation
Gwyear
0
23
45
68
90
2010 2015 2020 2025 2030 2035 2040
Base 8.5 HadGEM 8.5 Base 4.5HadGEM 4.5
Results Operation
▪ Risk of Shortage – HadGEM 8.5
Results Operation
▪ Accumulated Operation Cost – HadGEM 8.5
Scenario Premisses (RCP 4.5 and 8.5)
Operation Model SDDP (PSR)
Expansion Model MESSAGE-‐Brazil
(COPPE)
Operation/ Expansion
Operation Impacts/ adaptation
Adaptation through expansion
Operation
GCM – Dow
scaling – Hy
drology Mod
el
195 riv
erflo
w se
ries
•Hydro generation scenarios •Marginal cost of operation •Deficit probability
•Capacity expansion •Investment costs •Second order effects
Procedimento MetodológicoAdaptação/Impacto da Expansão
Results – Adaptation
▪ HadGEM – RCP 8.5
GW year
0.0
35.0
70.0
105.0
140.0
2010 2015 2020 2025 2030 2035 2040
Hydro Coal Gas OilNuclear Biomass Wind Solar
Lower hydro generation led to: •Increased coal •Increased wind (marginal)
Results – Adaptation
▪ HadGEM – RCP 4.5
GW year
0.0
40.0
80.0
120.0
160.0
2010 2015 2020 2025 2030 2035 2040
Hydro Coal Gas OilNuclear Biomass Wind Solar
Lower hydro generation led to: •Increased wind •Increased gas •Increased coal (marginal)
Comentários Finais
▪ Detailed dispatch modeling requires detailed hydrological modeling ▪ Operational impacts can be severe and costly if there is no
adaptation ▪ Mitigation policies do impact optimal adaptation strategies.
▪ Electricity generation in Brazil can become more carbon intensive in a BaU scenario
▪ Adapting to a reduced hydropower availability may further increase Brazil’s emissions if no other actions are taken
▪ Adaptation can be achieved by combination of energy efficiency, renewable energy, etc.
▪ Impacts on other renewable sources?
Obrigado
Cenários mais recentes para o Brasil
Roberto Schaeffer
Center for Energy and Environmental Economics CENERGIA
since 2003
Our team at CENERGIA: ▪ Professors
– Alexandre Szklo – André F P Lucena – Roberto Schaeffer
▪ Researchers – Ten MSc students – Ten DSc students – Three Postdocs one third of our researchers are from abroad
Global IAMsModel Institute Region/Country Classification Algorithm
MESSAGE-‐GLOBIOM IIASA Austria General equilibrium Intertemporal optimization
REMIND-‐MAgPIE PIK Germany General equilibrium Intertemporal optimization
IMAGE/TIMER PBL Netherlands Partial equilibrium Recursive dynamic
GCAM PNNL USA General equilibrium Recursive dynamicAIM/CGE NIES Japan General equilibrium Recursive dynamic
WITCH-‐GLOBIOM FEEM Italy General equilibrium Intertemporal optimization
IGMS-‐EPPA MIT USA General equilibrium Recursive dynamicGEM-‐E3 EU-‐JRC-‐IPTS, ICCS European Union General equilibrium Recursive dynamicIMACLIM SMASH-‐CIRED France General equilibrium Recursive dynamicMERGE EPRI USA General equilibrium Intertemporal optimization
WorldScan CPB Netherlands General equilibrium Recursive dynamicTIAM-‐WORLD ETSAP France Partial equilibrium Intertemporal optimization
POLESCNRS-‐LEPII, Enerdata,
EU-‐JRC-‐IPTS France Partial equilibrium Recursive dynamic
COFFEE/TEA COPPE Brazil General equilibrium Intertemporal optimization
Below Equator line
Model Linkages/Iterations
TEA model is a computational general equilibrium (CGE) model under development, headed by Angelo Gurgel (EESP-‐FGV).
▪ Reference: GTAPinGAMS ▪ Features:
▪ Recursive dynamic: evolution of the global economy until 2100 ▪ Multi-‐regional: the same 18 COFFEE model’s regions ▪ Multi-‐sectorial: agriculture, energy, industry, transport and services represented in 16 sectors
▪ Productive factors: labor, capital, land and resources ▪ Full set of bilateral trades: transport costs, export taxes and tariffs ▪ Detailed energy sector representation: current and backstop techs
The TEA ModelTotal-‐Economy Assessment
The COFFEE Model
COmputable Framework For Energy and the Environment
▪ Optimization in partial equilibrium Bottom-‐up model
▪ Includes Energy System and Land System Completely integrated under one framework
▪ Representation of the land use system: Forest, pastures, cropland, flooded
▪ Emissions Sources: Fossil-‐fuel combustion from all sectors, industrial
processes, waste treatment, fugitive emissions and land-‐related
▪ 18 regions
The COFFEE Model
The COFFEE Model Energy System
The COFFEE Model Land System
The COFFEE Model Scenario Protocol
▪ Current Policies (NPi): based on currently implemented national policies
▪ NPi_1000: NPi + global budget of 1,000 GtCO2 (2°C scenario)
▪ NPi_1000_NoCCS: NPi + global budget of 1,000 GtCO2 (2°C scenario) + Unavailability of CCS (fossil and/or bioenergy)
COFFEE: Current PoliciesGlobal GHG Emissions
Cumulative Emissions: 3,885 GtCO2
COFFEE: Current PoliciesGlobal Primary Energy
COFFEE: Current PoliciesGlobal Land Change (Cumulative)
COFFEE: NPi_1000 (2ºC)Global GHG Emissions
COFFEE: NPi_1000 (2ºC)Global Primary Energy
COFFEE: NPi_1000 (2ºC)Global Carbon Capture and Storage
COFFEE: NPi_1000 (2ºC)Global Land Change (Cumulative)
COFFEE: NPi_1000 (2ºC) without CCSGlobal GHG Emissions
COFFEE: NPi_1000 (2ºC) without CCSGlobal Primary Energy
COFFEE: NPi_1000 (2ºC) without CCSGlobal Land Change (Cumulative)
COFFEE: Scenario ComparisonGlobal CO2 Emissions
Brazil Land-‐Use and Energy Systems Model ▪ Partial equilibrium – the full energy and land-‐use systems ▪ Emissions include fossil-‐fuel combustion from all sectors, industrial
processes, waste treatment, and fugitive emissions
▪ Includes a representation of the land-‐use system: ▪ Forests, savannas, low-‐ and high-‐capacity pastures, integrated systems, cropland,
double cropping, planted forests, protected areas
The BLUES model
Forest Low Cap Pasture
High Cap Pasture
Integrated Systems
Cropland Double Cropping
Planted Forest
Savanna
Recovered Pastures
Managed Forests
The BLUES modelLand Use transitions matrix
▪ Current Policies
▪ INDC forever
▪ INDC – 1,000 GtCO2 (2o C)
Few and fresh results from (but not out of the) Blues…
BLUES: Current Policies ScenarioGHG Emissions
Prim
ary En
ergy (M
toe)
0
125
250
375
500
2010 2015 2020 2025 2030 2035 2040 2045 2050
Oil Gas Coal Biomass Sugarcane Hydro Solar Wind Nuclear
BLUES: Current Policies ScenarioPrimary Energy Consumption
BLUES: INDC ScenarioGHG Emissions
Prim
ary En
ergy (M
toe)
0
125
250
375
500
2010 2015 2020 2025 2030 2035 2040 2045 2050
Oil Gas Coal Biomass Sugarcane Hydro Solar Wind Nuclear
BLUES: INDC ScenarioPrimary Energy Consumption
Results - INDC Scenario
GWa Forest Cropland dbl_Crop Planted Forest
Lo Cap Pasture
Hi Cap Pasture
IntegratedSystem
Savanna
2010 0.0 0.0 0.0 0.0 0.0 0.0 0.0 0.02030 -‐12.9 14.0 5.2 3.0 -‐31.5 30.9 4.0 -‐12.42050 -‐12.9 17.1 9.3 4.3 -‐46.5 37.3 4.0 -‐12.4
By 2030:
• 31.5 Mha reduction in total Lo Cap pastures • 30.9 Mha increase in Hi Cap pastures • 3.0 Mha increase in planted forests • 4.0 Mha integrated systems • 9.3 Mha increase in double Cropping area
• Zero net deforestation post-‐2030 • Less forest loss; more savanna loss than NPi
• Exceeds INDC pasture recuperation target of 15 Mha • Meets 3.0 Mha target for planted forest increase • Meets 4.0 Mha target for integrated systems
Area (Mha)
Area (M
ha)
-‐90
-‐43
5
53
100
2010 2015 2020 2025 2030 2035 2040 2045 2050Forest Forest_PA Forest_Mg Croplanddbl_Crop Forest_Pl Low Cap Pasture High Cap PastureIntegratedSystem Recovered_Pasture Savanna Savanna_PA
BLUES: INDC_1000 Scenario (2ºC)GHG Emissions
Prim
ary En
ergy (M
toe)
0
125
250
375
500
2010 2015 2020 2025 2030 2035 2040 2045 2050
Oil Gas Coal Biomass Sugarcane Hydro Solar Wind Nuclear
BLUES: INDC_1000 Scenario (2ºC)Primary Energy Consumption
Power Generaaon
TWh
0.0
350.0
700.0
1050.0
1400.0
2010 2020 2030 2040 2050
Hydro Imp. Coal Nat. Coal Gas OilBio Bagasse Nuclear Wind DGPV CHP
BLUES: Scenario Comparison
Power Generaaon Emissions – GHG Emission
MtCO2eq
0.0
17.5
35.0
52.5
70.0
2010 2015 2020 2025 2030 2035 2040 2045 2050
Npi -‐ Emissions INDC -‐ EmissionsINDC_MB -‐ Emissions
BLUES: Scenario Comparison
Biofuel Use
PJ
0.0
750.0
1500.0
2250.0
3000.0
2010 2020 2030 2040 2050Ethanol_1stGen Ethanol_2ndGen Biodiesel Biocoal Ethanol_BioCCS AdvBioKerosene_Oil_HEFA AdvBioKerosene_Bio_BTL AdvBioDiesel_BTLAdvBioDiesel_BTL_CCS
BLUES: Scenario Comparison
Transport Emissions
MtCO2eq
0.0
65.0
130.0
195.0
260.0
2010 2015 2020 2025 2030 2035 2040 2045 2050
Current Policies INDC INDC_1000
BLUES: Scenario ComparisonTransport Sector – GHG Emissions
Passenger Transport -‐ Energy use
PJ
0
750
1500
2250
3000
2010 2015 2020 2025 2030 2035 2040 2045
Gasoline Diesel Gas Biodiesel Ethanol_final Kerosene Bunker Elect
BLUES: Scenario Comparison (INDC_1000)Transport Sector – Passenger Energy Use
Obrigado
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