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Energy Models. 86025_11. Overview. What is a Model?. A stylized, formalized representation of a system to probe its responsiveness. Classification of Energy Models. Energy systems boundaries (energy sector vs. economy, demand vs. supply, (final) energy demand vs. IRM)

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energy models

Energy Models

86025_11

Arnulf Grubler

overview
Overview

Arnulf Grubler

what is a model

What is a Model?

A stylized, formalized representation of a systemto probe its responsiveness

Arnulf Grubler

classification of energy models
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)

Arnulf Grubler

system boundaries in models
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”

Arnulf Grubler

energy systems boundaries
Energy Systems Boundaries

Supply

Demand

Arnulf Grubler

component models of energy demand
(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)

Arnulf Grubler

component models of energy supply
(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)

Arnulf Grubler

energy models commonalities of supply and demand perspectives
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 changedemand (productivity, GDP growth)technology improvements (costs, AEII)

Arnulf Grubler

energy economy environment systems boundaries of 3 models message eta macro dice
Energy – Economy – Environment: Systems Boundaries of 3 ModelsMESSAGE, ETA-MACRO, DICE

MESSAGE

Taxes

Emissions

Impacts

Damages(monetized)

ΔETA-MACRO and MESSAGE: Degree of technology detail

Arnulf Grubler

top down ex dice
Top-Down -- Ex. DICE

Arnulf Grubler

bill nordhaus dice model overview
Bill Nordhaus’ DICE Model: Overview

-

(AEEI)

+ Solow

Avoided damage

Remaining damage

Arnulf Grubler

bill nordhaus dice model illustrative result
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)

Arnulf Grubler

dice model analytically resolved 99 of all solutions by 2100 source a smirnov iiasa 2006
DICE Model - Analytically Resolved (99% of all solutions by 2100). Source: A. Smirnov, IIASA, 2006

abatement costs

damage costs

Arnulf Grubler

dice assumptions determining results
DICE – Assumptions Determining Results
  • Modeling paradigm:-- utility maximization (akin cost minimization)-- perfect foresight (akinno 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)

Arnulf Grubler

dice attainability domain and isolines of objective function surface
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
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

slide20

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

slide21

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

slide22
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

Arnulf Grubler

structure of a typical bottom up model
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

Arnulf Grubler

example message model of energy supply systems alternatives their general environmental impacts
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)

Arnulf Grubler

basic structure of message recall energy balance sheets
Basic Structure of MESSAGE(recall energy balance sheets!)

Energy levels

Pro

duction

Storage

Con

version

Demand

Resources

Blending

Cogen

eration

Energy forms

Technologies

Arnulf Grubler

a reference energy system of a b u model message
A Reference Energy System of a B-U Model (MESSAGE)

2000

Additional by 2020

Arnulf Grubler

representation of technologies
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

per unit activity (output)

0≥coefficient≤1

Arnulf Grubler

linear programming
Linear Programming

Production inputs (e.g. Capital, Labor)

x1

cx1+d<C

Resource constraintse.g. capital and labor

x1 < L

Demand constraintsupply≥demand

c1x1+c2x2min

ax1+bx2>D

Cost function

minimized

x2

Source: Strubegger, 2004.

linear programming1
Linear Programming

x1

cx1+d<C

x1 < L

ax1+bx2>D

c1x1+c2x2min

x2

Solution Space (Simplex)

Optimum Solution at Simplex Corner(defined by constraints & objective function)

Source: Strubegger, 2004.

slide31
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)

Arnulf Grubler

top down vs bottom up different questions and answers
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?”

Arnulf Grubler

top down vs bottom up strengths and weaknesses
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:?

Arnulf Grubler

slide36
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

Arnulf Grubler

iiasa wec global energy perspectives hybrid ia model
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)

Arnulf Grubler

iiasa ggi climate stabilization scenarios
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)

Arnulf Grubler

ggi ia framework
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 Sources:USEPA,EMF-21

Black Carbon and Organic Carbon Emissions

Data Sources: Klimont & Kupiano,TAP

Data sources: Fischer &Tubiello, LUC

Data Sources: Obersteiner & Rokityanskiy, FOR

biomass potentials
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
Scenario Characteristics (World, 2000-2100)

*Historical development since 1850

Arnulf Grubler

emissions reduction measures multiple sectors and stabilization levels
Emissions & Reduction MeasuresMultiple sectors and stabilization levels

Arnulf Grubler

costs energy sector left and macro economic right vs baseline and stabilization target uncertainty
Costs: Energy-sector (left), and Macro-economic (right) vs Baseline and Stabilization Target Uncertainty

Arnulf Grubler

slide46

Costs of Different Baselines and Stabilization Scenarios

Deployment rate of efficiency and low-emission technologies

Arnulf Grubler

slide47
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
Emissions & Reduction MeasuresPrincipal technology (clusters) and stabilization targets

Improvements incorporated in

baselines

Emissions reductions

due to climate policies

Arnulf Grubler

emission reduction measures principal technology clusters and stabilization targets
Emission Reduction Measures:Principal technology (clusters) and stabilization targets

(0.9 incl. baseline)

Arnulf Grubler

slide50
More

Technological Forecasting and Social Change74(2007) Special Issue

Available via ScienceDirect or via:

http://www.iiasa.ac.at/Research/GGI/publications/index.html?sb=12

Arnulf Grubler

integrated assessment models what they can do
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)

Arnulf Grubler

integrated assessment models what they cannot do
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)

Arnulf Grubler

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