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GaryHoworth

Founder

July72009EuroXXIIIBonnGermany

The Simulation & Optimization ofTechnology to Address Climate

Change

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Some Thoughts

Carbon, Technology & Politics areinextricably linked but how do werepresent them?

No one right model got to be fit forpurpose (speed vs accuracy)

Models to represent reality but they arenot reality !!

Evolution of and volatility inprices/markets is an important driver ofparticipant investment behavior

Actors are not homogenous eachcompany will view the world differently

Non linear more than one equilibrium

Key Factors but what are they?

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Getting Factor X right !!!

Networkmodels

MultitudeofApproachesbutwhichOneShouldweuse?Integration

Value chains

Real options

Portfolio

Models

Valuations

Companybehavior

timing andmarket impact

Evolution andtrademovements

behavior

Trading

Technology/Coalitions

Data

State Space

Agent basedmodels

SystemDynamics

Networks

Marketanalysis

Marketbehavior

Clusteranalysis

Market forecastSpotprices

Coalitions

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A Technology & Carbon Model

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Oil PriceNat Gas Price

Coal

Elec PricesCorn PricesUranium etc

Emissions

Carbon legislation/rules regulations

Costs

Cash flowscompetition etc

Subsidies/Tax

GDPEx Rates

Investments/Technology

SteelPower

OilChemicals

Paper & Pulp..

Carbon marketStructure

CompanyBehavior

Politicians

Carbon prices

Weather

Jobs, leakage

Networkeffects

Adequatelycapture the nonlineariities in themacro system

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Non Linearity's Everywhere Just One Component

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Marginal Abatement Curve Cement

-40

-20

0

20

40

0 10 20 30 40

mm Tonnes of CO2 avoided

$/CO2te

Industry and Behaviors affect MACs

Lower coal price & lower Clinker %

Company cash-flow constraint

With Power

just cementsavings

Marginal Abatement Curve Cement

-40

-20

0

20

40

0 10 20 30 40

mm Tonnes of CO2 avoided

$/CO2te

Carbon Accounting key issue

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0

5

10

15

20

25

30

1 2 3 4 5 6 7 8 9

Years

PenceperTh

erm

Long Term EquilMean Reversion 1

What Happened

Mean Reversion 2

Long Term Equilibrium. What Supply and Demand

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Supply - Demand

(1998 only)

-1000

-500

0

500

1000

1500

BG Power Generators Marketers

mmsc

fd

Price Volatility +70% -50% Important to model

individual companies positions

Market in Balance but

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Areas of Focus is Different for Each Company Oil Industry Example

Shell

BP

Flaring

GTL

Type of

Development Country

Heavy Oil

LNG

Will affect the way thecompany invests andthe way it trades andultimately the prices

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OtherTechnologies

Conventionaltechnologymainlyaboutcostsandproductionefficiencies

FocusofTechnologists onincrementaltechnologies

Buttechnologiesfromoutsidethebusinesscouldhaveagreateffect

EOR

costs

Plug inHybrids

Smart sensors

Batteries

Downholepower

Platformless

developments

Nano

Change in load curve

less installed capacity

FavoringNuke

Reservoirsimulations

BiotechNew fuels

CarbonconversionCCS IGCCs CTL

with CCS

Quantumcomputing

Reservoiroptimizations

New tradingalgorithms

Conventional

Wind Solar

New Materials

Syngas

Subsea andAUVs

UltradeepwaterArctic andother Frontierareas

Lower

demand

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Invest or Wait: The Effect of Network Effects.

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SpecificSectorEnergyTechnologyInteractions

Inject funding Size ofsphere representsamount of effort/funds

Driven by politicians?

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WheredoesthatFundingEndUp:

Impacts carbon markets

Impacts Technology network

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TechnologicalInteractions

Technologies

Interactions

Stimulus andfunding appliedhere

Under certain conditions stimulus

permeates and cascades to here

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EvolutionoftheNetworkofCoalitionofInterests Politics

National governments

U.S. state governments

Companies and individuals

Organizations

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Degree, K

P(k)

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NetworksInteractandChangeduetootherInteractions

Technologies

Governments/Politics

Institutions/Coalitions

Corporate

Markets/Needs

Environment eg. Oil prices

ABM, multi levelnetwork with

industry andcompany data

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Non Linearity Phase Diagram Regions of Behavior

0 0.2 0.4 0.6 0.8 10

0.2

0.4

0.6

0.8

1

Stability

Oscilla-tion

Incoherence

Partial Stability

Partial Oscillation

NetworkCoupling

Initial Frequency of OscillationsCoupled Oscillators

Different regime characteristics requiring different managementand optimization strategies

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Conclusions Key Messages

Externalities really important eg Network effects

Lots of uncertainty in key data which MAC? Optimizing underuncertainty and multiple goals

Company behavior is an important determinant of priceevolution. Volatility and market structure greatly affectbusiness decisions and participant behavior ie path andprices

Non linearity multiple equilbria

In the real world politicians can help or hinder progress. Thesepolitical networks adapt and evolve.

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Thank You

gary.howorth@energy-

redefined.comwww.Energy-Redefined.com

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Abstract

Climate change when coupled with the interaction of politics isan incredibly complex system. Optimization or control of such asystem using conventional techniques is difficult and fraught

with a myriad of issues. The paper will consider a number ofmodels that the author has explored in Industry to help framekey questions in the current climate debate. This has involvedconsideration of policy, technology and the modeling of carbon

emissions. The author's current approach uses a combinationof methods including complexity approaches. Initial results fromthe model will be presented.

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