energy systems analysisarnulf grubler energy models 86025_11

Energy Systems Analysis Arnulf Grubler

Energy Models

86025_11


Overview


What is a Model?

A stylized, formalized

representation of a systemto probe its responsiveness


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)


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 Boundaries

Supply

Demand


(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)


(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 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 – 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


Top-Down -- Ex. DICE


A Simple “Top-down” Energy Demand Model


Bill Nordhaus’ DICE Model: Overview

Avoided damage

- (AEEI)

+ Solow

Remaining damage


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)


DICE Model - Analytically Resolved (99% of all solutions by 2100). Source: A. Smirnov, IIASA, 2006

abatement costs

damage costs


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



climate sensitivity = 3.5 ºCSource: Smirnov, 2007



climate sensitivity = 4 ºCSource: 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


Bottom up – Ex. MESSAGE


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


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)


Basic Structure of MESSAGE(recall energy balance sheets!)

Reso

urces

Conversion

Cogeneration

Blending

Demand

Energy levels

Energy forms

Storage

Production

Technologies


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)


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)


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)


Summary

T-D and B-U Models


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?”


US – Mitigation Costs


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:?


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


Integrated Assessment Models


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


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


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


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


Costs: Energy-sector (left), and Macro-economic (right) vs Baseline and Stabilization Target Uncertainty


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


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


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


Sinks

Other renewables

Fossil CCS

CH4


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


Emission Reduction Measures:Principal technology (clusters) and stabilization targets

0 50 100 150 200 250 300 350

F-gases

N2O


Sinks

Other renewables

Fossil CCS

CH4


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)


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


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)


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)


From Models to Reality….

energy systems analysisarnulf grubler energy models 86025_11

Documents

energy demand model

final energy demand

higher energy costs

dice slide

damage slide

models demand final

price demand

aeii slide