Energy Models

1
Energy Models
86025_11
2
Overview
3
What is a Model?

• A stylized, formalized representation of a
  systemto probe its responsiveness

4
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)

5
System Boundaries in Models

• Demand (final vs. intermediary)
• Supply (end-use vs. energy sector)
• Energy system?economy?emissions ?impacts
  ?feedbacks(?)
• Aggregation leveltop-downbottom-up

6
Energy Systems Boundaries
Supply
Demand
7
(Component) Models of Energy Demand

• Bottom-up (MEDEE, LEAP, WEM)focus on
  quantitiessimulation (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 offew demand and fuel categories
• Hybrids (linked models, solved iteratively, (e.g.
  IIASA-WEC, IIASA-GGI)

8
(Component) Models of Energy Supply

• Bottom-up (MESSAGE, MARKAL)
• Top-down (ETA-MACRO, GREEN)
• Varying degrees oftechnology detailemissions
  (species)regional and sectorial detail
• Increasing integration (coupling to demand and
  macro-economic models)

9
Energy Models Commonalities of Supply and Demand
Perspectives

• Optimization (minimize supply costs, maximize
  utility of consumption)
• Forward looking (perfect informationforesight,n
  o uncertainty)
• Intertemporal choice (discounting)
• Single agent (social planner)
• Backstop technology
• Exogenous changedemand (productivity, GDP
  growth)technology improvements (costs, AEII)

10
Energy Economy Environment Systems
Boundaries of 3 ModelsMESSAGE, ETA-MACRO, DICE
MESSAGE
Taxes
Emissions
Impacts
Damages(monetized)
? ETA-MACRO and MESSAGE Degree of technology
detail
11
Top-Down -- Ex. DICE
12
A Simple Top-down Energy Demand Model
13
Bill Nordhaus DICE Model Overview
Remaining damage
14
Bill Nordhaus DICE Model Illustrative Result
15
DICE Model - Analytically Resolved (99 of all
solutions by 2100). Source A. Smirnov, IIASA,
2006
abatement costs
damage costs
16
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)

17
Attainability Domain of DICE with original
Optimality Point 2100
Source Smirnov, 2006
18
DICE Attainability Domain and Isolinesof
Objective Function Surface
Percent of max. of objective function.Note the
large indifference area
Source Smirnov, 2006
19
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
20
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
21
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
22
More
Nordhaus and Boyer, Warming the WorldEconomic
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
23
Bottom up Ex. MESSAGE
24
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)
• Constraintsphysical balances, load
  curvesmodeling e.g. build-up ratesscenarios
  e.g. climate (emissions) targets

25
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)

26
Basic Structure of MESSAGE(recall energy balance
sheets!)
Energy levels
Pro duction
Storage
Con version
Demand
Resources
Blending
Cogen eration
Energy forms
Technologies
27
A Reference Energy System of a B-U Model (MESSAGE)
2000
Additional by 2020
28
Representation of Technologies

• Installed capacity (capital vintage structure)
• Efficiency (1st Law conversion efficiency)
• Costs
• Investment
• Fixed OM
• Variable OM
• Availability factor
• Plant life (years)
• Emissions

per unit activity (output)
0coefficient1
29
Linear Programming
Production inputs (e.g. Capital, Labor)
x1
cx1dltC
Resource constraintse.g. capital and labor
x1 lt L
Demand constraintsupplydemand
c1x1c2x2?min
ax1bx2gtD
Cost function
minimized
x2
Source Strubegger, 2004.
30
Linear Programming
Solution Space (Simplex)
Optimum Solution at Simplex Corner(defined by
constraints objective function)
Source Strubegger, 2004.
31
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)
32
SummaryT-D and B-U Models
33
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?

34
US Mitigation Costs
35
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?

36
More
e.g. IPCC TAR(intro and summary and implications
on CC mitigation costs) http//www.grida.no/clima
te/ipcc_tar/wg3/310.htm http//www.ipcc.ch/ipccre
ports/tar/wg3/374.htm
37
Integrated Assessment Models
38
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)

39
IIASA-WEC Integrated Scenario Analysis
40
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)

41
GGI IA Framework
Spatially explicit scenario drivers Population,
Income, POP and GDP density(land prices)MESSAGE
demands
Exogenous drivers for CH4 N2O
emissions N-Fertilizer use, Bovine Livestock
Data Sources Fischer Tubiello,LUC
MESSAGE System Engineering Energy Model
Data Sources Obersteiner Rokityanskiy, FOR
Bottom-up mitigation technologies for non-CO2
emissions,
Data SourcesUSEPA,EMF-21
Black Carbon and Organic Carbon Emissions
Data Sources Klimont Kupiano,TAP
Data sources Fischer Tubiello, LUC
Data Sources Obersteiner Rokityanskiy, FOR
42
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)
43
Scenario Characteristics (World, 2000-2100)
2000 A2r B2 B1
Demand (FE), EJ 290 1250 950 800
Technological change - Low Medium High
Energy Intensity Impr., /year -0.7 -0.6 -1.2 -1.7
Carbon 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
44
Emissions Reduction MeasuresMultiple sectors
and stabilization levels
45
Costs Energy-sector (left), and Macro-economic
(right) vs Baseline and Stabilization Target
Uncertainty
46
Costs of Different Baselines and Stabilization
Scenarios
Deployment rate of efficiency and low-emission
technologies
47
Emissions and Reductions by Source in the
Scenarios(for an illustrative stabilization
target of 670 ppmv-equiv)
48
Emissions Reduction MeasuresPrincipal
technology (clusters) and stabilization targets
Improvements incorporated in baselines
Emissions reductions due to climate policies
49
Emission Reduction MeasuresPrincipal technology
(clusters) and stabilization targets
50
More
Technological Forecasting and Social Change
74(2007) Special Issue Available via
ScienceDirect or via http//www.iiasa.ac.at/Rese
arch/GGI/publications/index.html?sb12
51
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)

52
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, RD, technology transfer)

53
From Models to Reality.