Simulation and Optimization of Technology
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7/31/2019 Simulation and Optimization of Technology
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5/25/2012 2009 Energy-Redefined. All Rights Reserved.
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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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