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 system
to 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 systemeconomyemissions
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 quantities
simulation (activitiesdemand) 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 of
few 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 detail
emissions (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
MESSAGE
Taxes
Emissions
Impacts
Damages
(monetized)
Systemsand
Analysis
Arnulf Grubler
ΔEnergy
ETA-MACRO
MESSAGE: Degree of technology
detail
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
(AEEI)
+ Solow
Avoided damage
Remaining damage
Energy Systems Analysis
Arnulf Grubler
Bill Nordhaus’ DICE Model: Illustrative Result
“do nothing”, i.e. ignore climate change
“optimal solution”
balancing costs (abatement)
vs avoided costs (damages)
keep climate constant (no further change)
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)
Energy Systems Analysis
Arnulf Grubler
Attainability Domain of DICE with original
Optimality Point 2100
Source: Smirnov, 2006
DICE Attainability Domain and Isolines
of 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 ºC
Source: 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 ºC
Source: Smirnov, 2007
Attainability Domain, Objective Function, and
Thermohaline Collapse Risk Surfaces
Risk Surface of Thermohaline collapse
(years of exposure 1990-2100)
climate sensitivity = 4 ºC
Source: Smirnov, 2007
More
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 lowtemperature heat, specific electricity,...
• Supply technologies (energy sector and enduse): time vectors of process characteristics,
energy inputs/outputs, costs, emissions,…..
• Resource “supply curves”
(costs vs quantities)
• Constraints:
physical: balances, load curves
modeling: e.g. build-up rates
scenarios: 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!)
Energy levels
Pro
duction
Storage
Con
version
Demand
Blending
Cogen
eration
Energy forms
Technologies
Energy Systems Analysis
Arnulf Grubler
A Reference Energy System of a B-U Model (MESSAGE)
Resources
Primary energy
coal
Secondary energy
Final energy
coal
coal
fuel oil
fuel oil
light oil
light oil
gas
gas
coal
coal_hpl
lignite
meth_coal
Demand
Industrial sector,
non-substitutable uses
sp_el_I sp_liq_I
sp_h2_I sp_meth_I
solar_pv_I h2_fc_I
oil_enh
syn_liq
crude oil
crude oil
gas
gas
coal_ppl_u
coal_ppl
coal_cc
coal_htfc
gas_transport
coal_gas
liq. H2
methanol
hydrogen
hydrogen
biomass
methanol
methanol
dist. heat
waste
electricity
solar
biomass
hydro
uranium
electricity
gas_ppl
gas_cc
gas_htfc
wind
uranium
dist. heat
biomass
solar onsite
backstop
nuc_lc
nuc_hc
nuc_fbr
nuc_htemp
Nuclear
Non-commercial
energy
bioC_nc bio0C_nc
2000
Additional by 2020
Energy Systems Analysis
Industrial sector,
thermal uses
coal_i foil_i
loil_i gas_i
h2_i bioC_i
elec_i heat_i
hp_el_i hp_gas_i
solar_i
Industrial sector,
feedstocks
coal_fs foil_fs
loil_fs gas_fs
methanol_fs
Residential/commercial
sector,
non-substitutable uses
sp_el_RC solar_pv_RC
h2_fc_RC
Residential/commercial
sector, thermal uses
coal_rs foil_rs
loil_rs gas_rs
bioC_rc elec_rc
heat_rc h2_rc
hp_el_rc hp_gas_rc
solar_rc
Transport
coal_trp foil_trp
loil_trp gas_trp
elec_trp meth_ic_trp
meth_fc_trp
lh2_fc_trp h2_fc_trp
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
Energy Systems Analysis
per unit activity (output)
Arnulf Grubler
Linear Programming
Production inputs (e.g. Capital, Labor)
x1
cx1+d<C
Resource constraints
e.g. capital and labor
x1 < L
Demand constraint
supply≥demand
ax1+bx2>D
c1x1+c2x2min
Cost function
minimized
x2
Source: Strubegger, 2004.
Linear Programming
x1
Solution Space (Simplex)
ax1+bx2>D
cx1+d<C
x1 < L
c1x1+c2x2min
x2
Source: Strubegger, 2004.
Optimum Solution at Simplex Corner
(defined by constraints & objective function)
More
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
More
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 Grubler
IIASA MODELING FRAMEWORK FOR
IIASA-WEC Integrated
Scenario Analysis
SCENARIO ANALYSIS
Scenario Definition and
Evaluation
Soft-Linking
Conversion of Scenarios
from World to RAINS Regions
Energy Carriers by
RAINS Region
Economic Development
Demographic Projections
Technological Change
International Prices
Environmental Policies
Energy Intensity
Soft-Linking
Investment
World Market Prices
GDP Growth
Technological Change
SCENARIO
GENERATOR
Economic and Energy
Development Model
RAINS
Regional Air Pollution
Impacts Model
MAGICC
Model for
the Assessment
of GHG Induced
Climate Change
MESSAGE-MACRO
Energy Systems
Engineering and
Macroeconomic
Energy Model
Common Data-Bases
Energy, Economy, Resources
Technology Inventory CO2DB
h:\arnulf96\intas96l.ds4
Energy Systems Analysis
BLS
Basic Linked System
of National
Agricultural Models
GCM
Three Different
General
Circulation
Model Runs
ECS, 1996
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)
Energy Systems Analysis
Arnulf Grubler
GGI IA Framework
Spatially explicit scenario drivers:
Population, Income,
POP and GDP density
(land prices)
MESSAGE demands
Forest Sinks Potential, FSU
Increase in Prices
350
2050
300
250
2000
2100
200
150
Exogenous
drivers for CH4
& N2O emissions:
N-Fertilizer use,
Bovine Livestock
100
50
0
0
100
200
300
400
500
600
700
800
Data Sources: Fischer & Tubiello,LUC
Rate of carbon sequestration MTC
MESSAGE
Data Sources :Obersteiner & Rokityanskiy, FOR
System
Engineering
Energy Model
Agricultural residue potentials
7000
6000
WEU
5000
4000
3000
2000
FSU
PAO
EEU
AFR
LAM
MEA
CPA
SAS
PAS
Data sources: Fischer &Tubiello, LUC
Data Sources:USEPA,EMF-21
12
Bioenergy potential (EJ)
19
90
20
00
20
10
20
20
20
30
20
40
20
50
20
60
20
70
20
80
20
90
1000
0
Biomass supply A2:WEU
10
Biomass from forests
8
6$/GJ
5$/GJ
6
4$/GJ
3$/GJ
4
1$/GJ
Black Carbon and
Organic Carbon
Emissions
2
0
Ag. residues
20
10
20
20
20
30
20
40
20
50
20
60
20
70
20
80
20
90
21
00
PJ
NAM
Bottom-up
mitigation
technologies for
non-CO2
emissions,
Data Sources: Obersteiner & Rokityanskiy, FOR
Data Sources: Klimont & Kupiano,TAP
Biomass Potentials
Dynamic GDP maps (to 2100)
Dynamic population density (to 2100)
Downscaling
Development of bioenergy potentials (to 2100)
Consistency of land-price, urban areas, net primary
productivity, biomass potentials (spatially explicit)
Scenario Characteristics
(World, 2000-2100)
Demand (FE), EJ
2000
290
Technological change
Energy Intensity Impr., %/year
Carbon Intensity Impr., %/year
Fossil energy (PE), EJ
Non-fossil energy (PE), EJ
Emissions (Energy), GtC
ppmv (CO2-equiv)
Stabiliz. levels
A2r
1250
B2
950
B1
800
Low Medium
-0.7*
-0.6
-1.2
-0.3*
-0.3
-0.6
340
1180
690
95
1080
1050
7
27
16
370
1390
980
1090-670 670-520
-
High
-1.7
-1.5
340
1160
6
790
670-480
*Historical development since 1850
Energy Systems Analysis
Arnulf Grubler
Emissions & Reduction Measures
Multiple sectors and stabilization levels
B1
A2r
B1
100%
80%
60%
40%
CH4
20%
Other Gases
N2O
1400
1200
1000
800
600
0%
400
CO2 eq. Concentration in 2100, ppm
Energy Systems Analysis
Share of cumulative emission reductions by
gas (2000-2100)
CO2
100%
Energy & Industry
80%
60%
40%
20%
Agriculture
Forestry
1400
1200
1000
800
600
0%
400
CO2 eq. Concentration in 2100, ppm
Arnulf Grubler
Share of cumulative emission reductions by
sector (2000-2100)
A2r
Costs: Energy-sector (left), and Macro-economic (right)
vs Baseline and Stabilization Target Uncertainty
Energy Systems Analysis
Arnulf Grubler
Costs of Different Baselines and Stabilization Scenarios
Cumulative Discounted System
Costs (1990-2100),
[trillion US$]
1400
450ppmv CO2 stabilization
1200
550ppmv
650ppmv 750ppmv
1000
450ppmv
450ppmv
800
550ppmv
A1G
550ppmv
A1B
450ppmv
600
A1C
750ppmv
550ppmv
Baselines
A1T
400
0
500
1000
1500
2000
2500
Cumulative CO2 Emissions [GtC]
Deployment rate of efficiency and low-emission technologies
Energy Systems Analysis
Arnulf Grubler
40
40
35
35
30
Annual GHG emissions, GtC eq.
A2r
25
20
15
10
5
A2r-670
0
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
30
B2
25
20
15
10
B2-670
5
0
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
Energy conservation and efficiency
improvement
Switch to natural gas
35
Fossil CCS
30
CO2
Nuclear
25
20
Biomass (incl. CCS)
B1
Other renewables
15
Sinks
10
B1-670
20
90
20
70
Energy Systems Analysis
F-gases
20
50
0
1990 2000 2010 2020 2030 2040 2050 2060 2070 2080 2090 2100
N2O
20
30
5
CH4
19
90
Annual GHG emissions, GtC eq.
40
20
10
Annual GHG emissions, GtC eq.
Emissions and Reductions by Source in the Scenarios
(for an illustrative stabilization target of 670 ppmv-equiv)
Arnulf Grubler
Emissions & Reduction Measures
Principal technology (clusters) and stabilization targets
Improvements incorporated in
baselines
Carbon Intensity Improvement (Baseline)
Energy Intensity Improvement (Baseline)
Biomass (incl. CCS)
1390 ppm
Nuclear
1090 ppm
970 ppm
Consevation & efficiency
820 ppm
CH4
670 ppm
Emissions reductions
due to climate policies
Fossil CCS
Other renewables
590 ppm
520 ppm
480 ppm
Sinks
B1
B2
A2
Switch to natural gas
N2O
F-gases
0
500 1000 1500 2000 2500 3000 3500 4000
Cumulative contribution to mitigation (2000-2100), GtC eq.
Energy Systems Analysis
Arnulf Grubler
Emission Reduction Measures:
Principal technology (clusters) and stabilization targets
RF = 0.7
Biomass (incl. CCS)
RF = 0.1
Nuclear
RF = 0.2 (0.9 incl. baseline)
Consevation & efficiency
1390 ppm
1090 ppm
RF = 0.3
CH4
970 ppm
820 ppm
RF = 0.3
Fossil CCS
670 ppm
RF = 0.1
590 ppm
Other renewables
RF = 0.5
Sinks
520 ppm
B1 B2 A2
480 ppm
Switch to natural gas
RF = 0.2
RF = 1.0
N2O
RF = 0.7
F-gases
0
50
100
150
200
250
300
350
Cumulative contribution to mitigation (2000-2100), GtC eq.
Energy Systems Analysis
Arnulf Grubler
More
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….
Energy Systems Analysis
Arnulf Grubler