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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

86025_11

Arnulf Grubler


Overview

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

Supply

Demand

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

MESSAGE

Taxes

Emissions

Impacts

Damages(monetized)

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

-

(AEEI)

+ 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


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


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

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)

2000

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)

0≥coefficient≤1

Arnulf Grubler


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 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.


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


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


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


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

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

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)

*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

baselines

Emissions reductions

due to climate policies

Arnulf Grubler


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

(0.9 incl. baseline)

Arnulf Grubler


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

  • 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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