Energy Systems Analysis Arnulf Grubler
Energy Models
86025_11
Energy Systems Analysis Arnulf Grubler
Overview
Energy Systems Analysis Arnulf Grubler
What is a Model?
A stylized, formalized
representation of a systemto probe its responsiveness
Energy Systems Analysis Arnulf Grubler
Classification of Energy Models
• Energy systems boundaries (energy sector vs. economy, demand vs. supply, (final) energy demand vs. IRM)
• Aggregation level (“top-down” vs “bottom-up”)
• Science perspectives: Natural (climate), Economics (typical T-D, demand), Engineering (typical B-U, supply),Social science (typical B-U, demand)Integrated Assessment Models (all of above)
Energy Systems Analysis Arnulf Grubler
System Boundaries in Models
• Demand (final vs. intermediary)
• Supply (end-use vs. energy sector)
• Energy systemeconomyemissions impacts feedbacks(?)
• Aggregation level:“top-down”“bottom-up”
Energy Systems Analysis Arnulf Grubler
Energy Systems Boundaries
Supply
Demand
Energy Systems Analysis Arnulf Grubler
(Component) Models of Energy Demand
• Bottom-up (MEDEE, LEAP, WEM)focus on quantitiessimulation (activitiesdemand) and/or econometric (income, price demand)many demand and fuel categories
• Top-down (ETA-MACRO, DICE, RICE)focus on price-quantity relationships (cf econometric B-U models) and feedbacks to economy (equilibrium): higher energy costs = less consumption (GDP); T-D because offew demand and fuel categories
• Hybrids (linked models, solved iteratively, (e.g. IIASA-WEC, IIASA-GGI)
Energy Systems Analysis Arnulf Grubler
(Component) Models of Energy Supply
• Bottom-up (MESSAGE, MARKAL)• Top-down (ETA-MACRO, GREEN)• Varying degrees of:
technology detailemissions (species)regional and sectorial detail
• Increasing integration (coupling to demand and macro-economic models)
Energy Systems Analysis Arnulf Grubler
Energy Models: Commonalities of Supply and Demand Perspectives
• Optimization (minimize supply costs, maximize “utility of consumption”)
• Forward looking (perfect information&foresight,no uncertainty)
• Intertemporal choice (discounting)• Single agent (social planner)• “Backstop” technology• Exogenous change
demand (productivity, GDP growth)technology improvements (costs, AEII)
Energy Systems Analysis Arnulf Grubler
Energy – Economy – Environment: Systems Boundaries of 3 Models
MESSAGE, ETA-MACRO, DICE
Emissions
Impacts
Taxes
MESSAGE
Damages(monetized)
Δ ETA-MACRO and MESSAGE: Degree of technology detail
Energy Systems Analysis Arnulf Grubler
Top-Down -- Ex. DICE
Energy Systems Analysis Arnulf Grubler
A Simple “Top-down” Energy Demand Model
Energy Systems Analysis Arnulf Grubler
Bill Nordhaus’ DICE Model: Overview
Avoided damage
- (AEEI)
+ Solow
Remaining damage
Energy Systems Analysis Arnulf Grubler
Bill Nordhaus’ DICE Model: Illustrative Result
“do nothing”, i.e. ignore climate change
keep climate constant (no further change)
“optimal solution”balancing costs (abatement)vs avoided costs (damages)
Energy Systems Analysis Arnulf Grubler
DICE Model - Analytically Resolved (99% of all solutions by 2100). Source: A. Smirnov, IIASA, 2006
abatement costs
damage costs
Energy Systems Analysis Arnulf Grubler
DICE – Assumptions Determining Results
• Modeling paradigm:-- utility maximization (akin cost minimization)-- perfect foresight (akin no uncertainty)-- social planner (when-where flexibility, strict separation of equity and efficiency)
• Abatement cost and damage functions,calibrated as %GWP vs. GMTC (°C)
• Discount rate (for inter-temporal choice, 5%)matters for damages (long-term) vs abatement costs (short-term)
• No discontinuities (catastrophes)
Attainability Domain of DICE with original Optimality Point 2100
Source: Smirnov, 2006
DICE Attainability Domain and Isolinesof Objective Function Surface
Percent of max. of objective function.Note the large “indifference” area
Source: Smirnov, 2006
Attainability Domain, Objective Function, and Thermohaline Collapse Risk Surfaces
Risk Surface of Thermohaline collapse(years of exposure 1990-2100)
climate sensitivity = 3 ºCSource: Smirnov, 2007
Attainability Domain, Objective Function, and Thermohaline Collapse Risk Surfaces
Risk Surface of Thermohaline collapse(years of exposure 1990-2100)
climate sensitivity = 3.5 ºCSource: Smirnov, 2007
Attainability Domain, Objective Function, and Thermohaline Collapse Risk Surfaces
Risk Surface of Thermohaline collapse(years of exposure 1990-2100)
climate sensitivity = 4 ºCSource: Smirnov, 2007
Energy Systems Analysis Arnulf Grubler
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Nordhaus and Boyer, Warming the World:Economic Models of Global Warming, MIT Press, Cambridge, Mass, 2000.
Online documentation and .xls and GAMS versions of model :
http://www.econ.yale.edu/~nordhaus/homepage/dicemodels.htm
Energy Systems Analysis Arnulf Grubler
Bottom up – Ex. MESSAGE
Energy Systems Analysis Arnulf Grubler
Structure of a typical “Bottom-up” model• Demand categories (ex- or endogeneous):
time vectors, e.g. industrial high- and low-temperature heat, specific electricity,...
• Supply technologies (energy sector and end-use): time vectors of process characteristics, energy inputs/outputs, costs, emissions,…..
• Resource “supply curves” (costs vs quantities)
• Constraints:physical: balances, load curvesmodeling: e.g. build-up ratesscenarios: e.g. climate (emissions) targets
Energy Systems Analysis Arnulf Grubler
Example MESSAGE (Model of Energy Supply Systems Alternatives & their General Environmental Impacts)
Model structure:– Time frame (horizon, steps)– Load regions (demand/supply regions)– Energy levels (primary to final)– Energy forms (fuels)
Model variables:– Technologies (conversion): main model entities– Resources (supply curves modeling scacity)– Demands (exogenous GDP, efficiency, and lifestyles)
– Constraints (restrictions, e.g. CO2 emissions):ultimately determine solution (ex. TECH, RES, DEM)
Energy Systems Analysis Arnulf Grubler
Basic Structure of MESSAGE(recall energy balance sheets!)
Reso
urces
Conversion
Cogeneration
Blending
Demand
Energy levels
Energy forms
Storage
Production
Technologies
Energy Systems Analysis Arnulf Grubler
coal
lignite
coal
crude oil crude oil
gas gas
uranium uranium
methanol
biomass
waste
solar
wind
hydro
Resources Primary energy
coal
light oil
gas
hydrogen
methanol
dist. heat
electricity
biomass
solar onsite
fuel oil
backstop
coal
light oil
gas
hydrogen
methanol
dist. heat
electricity
fuel oil
biomass
gas_transport
gas_pplgas_cc
gas_htfc
coal_ppl_ucoal_pplcoal_cccoal_htfc
coal_gas
coal_hpl
syn_liq
meth_coal
oil_enh
Nuclearnuc_lcnuc_hcnuc_fbrnuc_htemp
liq. H2
Secondary energy Final energyIndustrial sector,
non-substitutable usessp_el_I sp_liq_I
sp_h2_I sp_meth_I solar_pv_I h2_fc_I
Industrial sector,thermal usescoal_i foil_iloil_i gas_i h2_i bioC_ielec_i heat_i
hp_el_i hp_gas_isolar_i
Residential/commercialsector,
non-substitutable usessp_el_RC solar_pv_RC
h2_fc_RC
Residential/commercialsector, thermal uses
coal_rs foil_rs loil_rs gas_rs
bioC_rc elec_rc heat_rc h2_rc
hp_el_rc hp_gas_rcsolar_rc
Industrial sector,feedstocks
coal_fs foil_fs loil_fs gas_fs methanol_fs
Transportcoal_trp foil_trp loil_trp gas_trp
elec_trp meth_ic_trpmeth_fc_trp
lh2_fc_trp h2_fc_trp
Non-commercial energy
bioC_nc bio0C_nc
Demand
2000 Additional by 2020
A Reference Energy System of a B-U Model (MESSAGE)
Energy Systems Analysis Arnulf Grubler
Representation of Technologies
– Installed capacity (capital vintage structure)– Efficiency (1st Law conversion efficiency)– Costs
• Investment• Fixed O&M• Variable O&M
– Availability factor– Plant life (years)– Emissions
0≥coefficient≤1 per unit activity (output)
Linear Programming
x1
x1 < L
cx1+d<C
ax1+bx2>D
x2
c1x1+c2x2min
Resource constraintse.g. capital and labor
Demand constraintsupply≥demand
Source: Strubegger, 2004.
Production inputs (e.g. Capital, Labor)
Cost functionminimized
Linear Programming
x1
x1 < L
cx1+d<C
ax1+bx2>D
x2
c1x1+c2x2min
Source: Strubegger, 2004.
Solution Space (Simplex)
Optimum Solution at Simplex Corner(defined by constraints & objective function)
Energy Systems Analysis Arnulf Grubler
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Eric V. Denardo, The Science of Decision Making. A Problem-based Approach Using Excel. Wiley, 2002.
Good introduction and CD with excel macros and solvers.(see Arnulf or Denardo at ENG for a browse copy)
Energy Systems Analysis Arnulf Grubler
Summary
T-D and B-U Models
Energy Systems Analysis Arnulf Grubler
Top-down vs. Bottom-up: Different Questions and Answers
• T-D: “How much a given energy price (environmental tax) increase will reduce demand (emissions) and consumption (GDP growth)?”
• B-U: “How can a given energy demand (emission reduction target) be achieved with minimal (energy systems) costs?”
Energy Systems Analysis Arnulf Grubler
US – Mitigation Costs
Energy Systems Analysis Arnulf Grubler
Top-down vs. Bottom-up: Strengths and Weaknesses
• Top-down (equilibrium):+ transparency, simplicity, data availability+ prices & quantities equilibrate- ignores (externalizes) major structural changes (dematerialization, lifestyles, TC)
• Bottom-up (status-quo):+ detail, clear decision rules- main drivers remain exogenous (demand,
technology change, resources)- quality does not matter- invisible costs:?
Energy Systems Analysis Arnulf Grubler
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e.g. IPCC TAR(intro and summary and implications on CC mitigation costs)
http://www.grida.no/climate/ipcc_tar/wg3/310.htm
http://www.ipcc.ch/ipccreports/tar/wg3/374.htm
Energy Systems Analysis Arnulf Grubler
Integrated Assessment Models
Energy Systems Analysis Arnulf Grubler
IIASA-WEC Global Energy Perspectives: Hybrid IA Model
• Top-down, bottom-up combination (soft-linking)
• Top-down scenario development (aggregates)
• Decomposition into sectorial demands (useful energy level)
• Alternative supply scenarios• Iterations to balance prices & quantities
(macro-module)• Calculation of emissions (no feedbacks)
Energy Systems Analysis Arnulf Grublerh:\arnulf96\intas96l.ds4
IIASA MODELING FRAMEWORK FORSCENARIO ANALYSIS
Common Data-BasesEnergy, Economy, Resources Technology Inventory CO2DB
Soft-Linking
Scenario Definition andEvaluation
Soft-Linking
GCMMAGICC
Conversion of Scenariosfrom World to RAINS RegionsEnergy Carriers byRAINS Region
Economic DevelopmentDemographic ProjectionsTechnological ChangeInternational PricesEnvironmental PoliciesEnergy Intensity
InvestmentWorld Market PricesGDP GrowthTechnological Change
RAINSRegional Air PollutionImpacts Model
MESSAGE-MACROEnergy SystemsEngineering andMacroeconomicEnergy Model
BLSBasic Linked Systemof NationalAgricultural Models
Model for the Assessmentof GHG Induced Climate Change
Three DifferentGeneralCirculationModel Runs
ECS, 1996
SCENARIO
Economic and EnergyDevelopment Model
GENERATOR
IIASA-WEC Integrated Scenario Analysis
Energy Systems Analysis Arnulf Grubler
IIASA GGI Climate Stabilization Scenarios
• Capturing uncertainty: 3 baselines (demand, technology innovation and costs), stabilization targets
• Energy, agriculture, forestry sectors and all GHGs
• Spatially explicit analysis (11 world regions, ~106 grid cells)
• Stabilization targets: Exogenous• Methodology: Inter-temporal cost
minimization (global)
GGI IA Framework
MESSAGE
System Engineering
Energy Model
Exogenous drivers for CH4
& N2O emissions:
N-Fertilizer use, Bovine Livestock
Bottom-up mitigation
technologies for non-CO2
emissions,
Black Carbon and Organic Carbon
Emissions
Forest Sinks Potential, FSU
0
50
100
150
200
250
300
350
0 100 200 300 400 500 600 700 800
Rate of carbon sequestration MTC
Incr
ease
in P
rices
21002000
2050
Data Sources :Obersteiner & Rokityanskiy, FOR
Data Sources: Fischer & Tubiello,LUC
Data Sources:USEPA,EMF-21
Data Sources: Klimont & Kupiano,TAP
Agricultural residue potentials
0
1000
2000
3000
4000
5000
6000
7000
PJ
NAM
WEU
PAO
FSU
EEU
AFR
LAM
MEA
CPA
SAS
PAS
Data sources: Fischer &Tubiello, LUC
Data Sources: Obersteiner & Rokityanskiy, FOR
Biomass supply A2:WEU
0
2
4
6
8
10
12
Bio
ener
gy
po
ten
tial
(E
J)
Ag. residues
Biomass from forests
1$/GJ
6$/GJ
4$/GJ
5$/GJ
3$/GJ
Spatially explicit scenario drivers:Population, Income,POP and GDP density(land prices)MESSAGE demands
Biomass Potentials
Dynamic GDP maps (to 2100) Dynamic population density (to 2100)
Development of bioenergy potentials (to 2100)
Consistency of land-price, urban areas, net primaryproductivity, biomass potentials (spatially explicit)
Downscaling
Energy Systems Analysis Arnulf Grubler
Scenario Characteristics (World, 2000-2100)
2000 A2r B2 B1
Demand (FE), EJ 290 1250 950 800
Technological change - Low Medium HighEnergy Intensity Impr., %/year -0.7* -0.6 -1.2 -1.7Carbon Intensity Impr., %/year -0.3* -0.3 -0.6 -1.5
Fossil energy (PE), EJ 340 1180 690 340
Non-fossil energy (PE), EJ 95 1080 1050 1160
Emissions (Energy), GtC 7 27 16 6
ppmv (CO2-equiv) 370 1390 980 790
Stabiliz. levels - 1090-670 670-520 670-480
*Historical development since 1850
Energy Systems Analysis Arnulf Grubler
Emissions & Reduction MeasuresMultiple sectors and stabilization levels
0%
20%
40%
60%
80%
100%
400600800100012001400
CO2 eq. Concentration in 2100, ppm
Sh
are
of
cum
ula
tiv
e e
mis
sio
n r
ed
uc
tio
ns
by
sec
tor
(20
00
-21
00)
B1A2r
Energy & Industry
Forestry
Agriculture
0%
20%
40%
60%
80%
100%
400600800100012001400
CO2 eq. Concentration in 2100, ppm
Sh
are
of
cum
ula
tive
em
issi
on
red
uct
ion
s b
y g
as (
2000
-210
0)
B1A2r
CO2
CH4
N2O
Other Gases
Energy Systems Analysis Arnulf Grubler
Costs: Energy-sector (left), and Macro-economic (right) vs Baseline and Stabilization Target Uncertainty
Energy Systems Analysis Arnulf Grubler
Costs of Different Baselines and Stabilization Scenarios
400
600
800
1000
1200
1400
0 500 1000 1500 2000 2500
Cumulative CO2 Emissions [GtC]
Cu
mu
lati
ve
Dis
co
un
ted
Sy
ste
mC
os
ts (
19
90
-21
00
),
[tri
llio
n U
S$
]
A1CA1G
A1B
A1T
450ppmv CO2 stabilization
750ppmv650ppmv550ppmv
450ppmv
450ppmv
450ppmv
550ppmv
550ppmv
550ppmv
Baselines
750ppmv
Deployment rate of efficiency and low-emission technologies
Energy Systems Analysis Arnulf Grubler
Emissions and Reductions by Source in the Scenarios(for an illustrative stabilization target of 670 ppmv-equiv)
0
5
10
15
20
25
30
35
40
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
An
nu
al G
HG
em
issi
on
s, G
tC e
q.
A2r
A2r-670
1990
2010
2030
2050
2070
2090
Energy conservation and efficiencyimprovementSwitch to natural gas
Fossil CCS
Nuclear
Biomass (incl. CCS)
Other renewables
Sinks
CH4
N2O
F-gases
CO2
0
5
10
15
20
25
30
35
40
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
An
nu
al G
HG
em
issi
on
s, G
tC e
q.
B2
B2-670
0
5
10
15
20
25
30
35
40
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
An
nu
al G
HG
em
issi
on
s, G
tC e
q.
B1
B1-670
Energy Systems Analysis Arnulf Grubler
0 500 1000 1500 2000 2500 3000 3500 4000
F-gases
N2O
Switch to natural gas
Sinks
Other renewables
Fossil CCS
CH4
Consevation & efficiency
Nuclear
Biomass (incl. CCS)
Energy Intensity Improvement (Baseline)
Carbon Intensity Improvement (Baseline)
Cumulative contribution to mitigation (2000-2100), GtC eq.
1390 ppm
1090 ppm
970 ppm
820 ppm
670 ppm
590 ppm
520 ppm
480 ppm
Emissions & Reduction MeasuresPrincipal technology (clusters) and stabilization targets
Emissions reductions due to climate policies
Improvements incorporated inbaselines
0 500 1000 1500 2000 2500 3000 3500 4000
F-gases
N2O
Switch to natural gas
Sinks
Other renewables
Fossil CCS
CH4
Consevation & efficiency
Nuclear
Biomass (incl. CCS)
Energy Intensity Improvement (Baseline)
Carbon Intensity Improvement (Baseline)
B1
B2
A2
820 ppm
670 ppm
590 ppm
520 ppm
480 ppm
Energy Systems Analysis Arnulf Grubler
Emission Reduction Measures:Principal technology (clusters) and stabilization targets
0 50 100 150 200 250 300 350
F-gases
N2O
Switch to natural gas
Sinks
Other renewables
Fossil CCS
CH4
Consevation & efficiency
Nuclear
Biomass (incl. CCS)
Cumulative contribution to mitigation (2000-2100), GtC eq.
1390 ppm
1090 ppm
970 ppm
820 ppm
670 ppm
590 ppm
520 ppm
480 ppmA2B1 B2
RF = 0.7
RF = 0.3
RF = 0.2
RF = 0.1
RF = 0.5
RF = 0.1
RF = 0.3
RF = 0.7
RF = 1.0 RF = 0.2
(0.9 incl. baseline)
Energy Systems Analysis Arnulf Grubler
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Technological Forecasting and Social Change 74(2007) Special Issue
Available via ScienceDirect or via:
http://www.iiasa.ac.at/Research/GGI/publications/index.html?sb=12
Energy Systems Analysis Arnulf Grubler
Integrated Assessment Models: What they can do
• Full cycle analysis: Economy – Energy – Environment
• Multiple scenarios (uncertainties)
• Multiple environmental impacts (but aggregation only via monetarization)
• Cost-benefit, cost-effectiveness analysis
• Value and timing of information (backstops)
Energy Systems Analysis Arnulf Grubler
Integrated Assessment Models: What they cannot do
• Resolve uncertainties (LbD)
• Optional “hedging” strategies vis à vis uncertainty (→stochastic optimization)
• Resolve equity-efficiency conundrum(→agent based, game theoretical models)
• Address implementation issues(e.g. building codes, C-trade, R&D, technology transfer)
Energy Systems Analysis Arnulf Grubler
From Models to Reality….