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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


Arnulf Grubler


Arnulf Grubler

What is a Model?

A stylized, formalized representation of a systemto probe its responsiveness

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)

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”

Arnulf Grubler

Energy Systems Boundaries



Arnulf Grubler

(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

  • 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

  • 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 ModelsMESSAGE, ETA-MACRO, DICE






ΔETA-MACRO and MESSAGE: Degree of technology detail

Arnulf Grubler

Top-Down -- Ex. DICE

Arnulf Grubler

A Simple “Top-down” Energy Demand Model

Arnulf Grubler

Bill Nordhaus’ DICE Model: Overview



+ Solow

Avoided damage

Remaining damage

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)

Arnulf Grubler

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

  • 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

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 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


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 :

Arnulf Grubler

Bottom up – Ex. MESSAGE

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

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)

Arnulf Grubler

Basic Structure of MESSAGE(recall energy balance sheets!)

Energy levels











Energy forms


Arnulf Grubler

A Reference Energy System of a B-U Model (MESSAGE)


Additional by 2020

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

per unit activity (output)


Arnulf Grubler

Linear Programming

Production inputs (e.g. Capital, Labor)



Resource constraintse.g. capital and labor

x1 < L

Demand constraintsupply≥demand



Cost function



Source: Strubegger, 2004.

Linear Programming



x1 < L




Solution Space (Simplex)

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

Source: Strubegger, 2004.


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

SummaryT-D and B-U Models

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?”

Arnulf Grubler

US – Mitigation Costs

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:?

Arnulf Grubler


e.g. IPCC TAR(intro and summary and implications on CC mitigation costs)

Arnulf Grubler

Integrated Assessment Models

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)

Arnulf Grubler

IIASA-WEC Integrated Scenario 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)

Arnulf Grubler

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


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

Dynamic GDP maps (to 2100)

Dynamic population density (to 2100)


Development of bioenergy potentials (to 2100)

Consistency of land-price, urban areas, net primary

productivity, biomass potentials (spatially explicit)

Scenario Characteristics (World, 2000-2100)

*Historical development since 1850

Arnulf Grubler

Emissions & Reduction MeasuresMultiple sectors and stabilization levels

Arnulf Grubler

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

Arnulf Grubler

Costs of Different Baselines and Stabilization Scenarios

Deployment rate of efficiency and low-emission technologies

Arnulf Grubler

Emissions and Reductions by Source in the Scenarios(for an illustrative stabilization target of 670 ppmv-equiv)

Arnulf Grubler

Emissions & Reduction MeasuresPrincipal technology (clusters) and stabilization targets

Improvements incorporated in


Emissions reductions

due to climate policies

Arnulf Grubler

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

(0.9 incl. baseline)

Arnulf Grubler


Technological Forecasting and Social Change74(2007) Special Issue

Available via ScienceDirect or via:

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)

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)

Arnulf Grubler

From Models to Reality….

Arnulf Grubler

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