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Copyright 2004 Argonne National Laboratory
Can Complexity Be Captured with AgentCan Complexity Be Captured with Agent--Based Modeling and Simulation?Based Modeling and Simulation?
Michael Northnorth@anl.gov
www.cas.anl.gov
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Copyright 2004 Argonne National Laboratory
The “Name Game”The The ““Name GameName Game””
• ABMS is known by many names:– ABM: “Agent-based modeling” or “anti-ballistic missile?”– ABS: Agent-based simulation or “anti-lock breaks?”– IBM: Individual-based modeling or “International Business
Machines Corporation?”• ABM, ABS, and IBM are all widely-used acronyms, but
“ABMS” will be used throughout the lectures to avoid confusion with the above mentioned terms
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Copyright 2004 Argonne National Laboratory
What is ABMS?What is ABMS?What is ABMS?
• ABMS seeks to create electronic laboratories (“e-laboratories”) that allow experimentation with simulated complex systems:– ABMS uses sets of agents and frameworks for simulating the agent’s
decisions and interactions – ABMS can show how a system could evolve through time in a way that
is difficult to predict from knowledge of the behaviors of the individual agents alone
• ABMS focuses on individual behavior with the agent rules are often based on theories of the individual such as Rational Individual Behavior, Bounded Rationality or Satisficing
• Based on these simple types of rules, ABMS can be used to study how patterns emerge
• ABMS may reveal behavioral patterns at a macro (system) level that are not obvious from an examination of the underlying agentrules alone – these patterns are called “emergent behavior”
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Copyright 2004 Argonne National Laboratory
ABMS is Often Used to ModelComplex Adaptive Systems
ABMS is Often Used to ModelABMS is Often Used to ModelComplex Adaptive SystemsComplex Adaptive Systems
• A Complex Adaptive System (CAS) is made up of agents that interact and reproduce while adapting to a changing environment
• Researchers such as John Holland are trying to isolate fundamental causes of adaptation and emergence of system-wide properties – in any CAS
• John Holland has identified the following properties and mechanisms that are common to all CAS:– Nonlinearity– Diversity– Aggregation– Flows– Tagging– Internal models– Building blocks
• ABMS incorporates some of the properties and mechanisms of CAS
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Copyright 2004 Argonne National Laboratory
There Are Many Examples of SystemsComprised of Interacting Individuals
There Are Many Examples of SystemsThere Are Many Examples of SystemsComprised of Interacting IndividualsComprised of Interacting Individuals
• Economic markets:– Producers– Distributors– Consumers
• Human immune system:– Antibodies– Bacteria– Viruses
• Social Systems:– People– Factions– Countries
• Ecosystems:– Species– Individuals– Hives– Flocks
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Copyright 2004 Argonne National Laboratory
Where Did ABMS Come From?Where Did ABMS Come From?Where Did ABMS Come From?
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Copyright 2004 Argonne National Laboratory
Repast is the One of the Most PopularAmong a Range of Available ABMS Toolkits
Repast is the One of the Most PopularRepast is the One of the Most PopularAmong a Range of Available ABMS ToolkitsAmong a Range of Available ABMS Toolkits
IMT flock.cbl.umces.edu/imt
Ease of Model Development
Mod
elin
g Po
wer
Easy Hard
Low
Hig
h
StarLogo www.media.mit.edu/starlogo
Participatory Simulation
Spreadsheets
Structured Languages (C, Pascal, etc.)
RePast repast.sourceforge.netAscape www.brook.edu/es/dynamics/models/ascape
Swarm www.swarm.org
Object Oriented Languages (Java, C++, etc.)
Mathematics Packages (Mathematica®, etc.)
Selected ExampleABMS Toolkits
NetLogo ccl.northwestern.edu/netlogo/
DIAS www.dis.anl.gov/DIAS/
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Copyright 2004 Argonne National Laboratory
What Useful InformationCan ABMS Provide?
What Useful InformationWhat Useful InformationCan ABMS Provide?Can ABMS Provide?
• ABMS can help to provide insight into and predictions of agent behaviors
• ABMS can help to anticipate system dynamics, structures, and possible evolutionary paths including suggesting answers to a variety of questions including the following:– What agent rules influence emergent behavior and how do
they do so?– Will a some types of agents tend to dominate?– Will changes come quickly or slowly?– Will some systems always be in a state of turbulence?
• ABMS can be used to help identify disequilibrium situations and their causes
• ABMS can be used to help identify sources of uncertainty in the underlying system
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Copyright 2004 Argonne National Laboratory
ABMS is Complementaryto Traditional TechniquesABMS is ComplementaryABMS is Complementaryto Traditional Techniquesto Traditional Techniques
• Analytics: Analytical modeling seeks to develop rigorous, provable statements about systems
• Statistical Methods: Statistical modeling specifies how outputs depend on inputs – systems are represented as a “black boxes”
• Optimization: Optimization modeling seeks to find optimal solutions relative to well-defined objectives and subject to specific constraints
• Discrete Event Simulation: Traditional discrete event simulation modeling represents the inner workings of dynamic processes and moves those representations forward through time at a system level
Out
put
Input-10 -5 0 5 10
xi1
-10-5
05
10
xk1
-4000
-3000
-2000
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0
QP
-4000
-3000
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-1000
0
QP
Wait for firstavailable server
CustomerCustomer Customer
Customerin Transit
Server 1Server 2Customer
Being Served
Customer
Customerin Transit
Customer
CustomerCustomer
Customer
Queue
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Copyright 2004 Argonne National Laboratory
Analytical Modeling Seeks to Develop Provable Statements About Systems
Analytical Modeling Seeks to Develop Provable Analytical Modeling Seeks to Develop Provable Statements About SystemsStatements About Systems
• An example is solving a well-posed problem in classical mechanics
• Difficulties:– Analytical models usually focus on global descriptions– Analytical models of complex systems can be extremely
unwieldy– Excessively “heroic” assumptions are required to create
analytically solvable models of many systems– Many systems cannot be analytically solved at all
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Copyright 2004 Argonne National Laboratory
Statistical Methods SeekRelationships Between Inputs and Outputs
Statistical Methods SeekStatistical Methods SeekRelationships Between Inputs and OutputsRelationships Between Inputs and Outputs
• Output = f(Input1, Input2, Input3, …., Inputn) where f is a statistically derived relationship
• Difficulties
SystemInput Output
– The derived relationships can be brittle– Not sensitive to many assumptions or
amenable to “what-if” scenarios– Insight into underlying causes is often
limited
Output
Input
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Copyright 2004 Argonne National Laboratory
Optimization Modeling Seeks “Best Values”Optimization Modeling Seeks Optimization Modeling Seeks ““Best ValuesBest Values””
• Max x1 and x2: (1 - e(x1+x2)) (Fitness)Subject to : 3 x1 + 5 x2 < 100 (Size constraint)
• Difficulties -10 -5 0 5 10
xi1
-10-5
05
10
xk1
-4000
-3000
-2000
-1000
0
QP
-4000
-3000
-2000
-1000
0
QP
– Optimization models usually focus on global descriptions
– Even for well defined problems, finding optimal solutions can be extremely difficult
– Brittle formulations and solution points can result
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Copyright 2004 Argonne National Laboratory
Wait for firstavailable server
CustomerCustomer Customer
Customerin Transit
Server 1Server 2Customer
Being Served
Customer
Customerin Transit
CustomerBeing Served
CustomerCustomer
Customer
Customer
Queue
1. Customer Arrives to System,Customer Enters Queue (ifservers busy)
2. Customer Begins Transitto Server
3. Customer ArrivesServer,Customer BeginsService,Server BecomesBusy
4. CustomerCompletes Serviceand DepartsSystem,Server BecomesFree
The Queueing Simulation Has Four Types of Eventsand Two Activities
Discrete Event Simulation Modeling Represents the Detailed Steps in a Process As It Unfolds Over TimeDiscrete Event Simulation Modeling Represents the Discrete Event Simulation Modeling Represents the Detailed Steps in a Process As It Unfolds Over TimeDetailed Steps in a Process As It Unfolds Over Time
• Difficulties– The emphasis is on fixed processes instead of adaptive
actors– Defining the process representation can be difficult since
there is no a clear delineation between too little and too much detail
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Copyright 2004 Argonne National Laboratory
ABMS is Being Applied to a Wide Range of CASABMS is Being Applied to a Wide Range of CASABMS is Being Applied to a Wide Range of CAS
• Social systems:– Political systems– Small groups– Businesses
• Markets:– Financial markets– Energy markets
• Industrial supply chains• Infrastructure systems• Ecosystems• Immune systems• Electrical power markets
are an example…
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH
FUNCTIONS
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
BUSINESS LAYERS
PHYSICAL LAYER
REGULATORY LAYER
Consumer Contracts and Tariffs
Demand Agents
Generation Companies Generators
Generator Ownership
Bilateral Contracts
GENE
RATI
ON C
OMPA
NIES
and
DEM
AND
AGEN
TS
Consumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information System
REGULATOR
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH
FUNCTIONS
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH
FUNCTIONS
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
BUSINESSLAYERS
PHYSICAL LAYER
REGULATORY LAYER
Consumer Contracts and Tariffs
Demand Companies
Generation Companies Generators
Generator Ownership
Bilateral Contracts
GENE
RATI
ON C
OMPA
NIES
and
DEM
AND
COM
PANI
ESConsumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information System
Consumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information SystemMarket Information System
REGULATORREGULATORREGULATORSpecial Event
Generator
Special Event
Generator
Special Event
Generator
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Copyright 2004 Argonne National Laboratory
Electric Utility Systems Are EvolvingElectric Utility Systems Are EvolvingElectric Utility Systems Are Evolving
• Until recently, most electric power systems were managed by regulated, vertically integrated monopolies
• Several systems, such as those in California and the UK, have implemented open electricity markets that seek to:– Promote competition among suppliers – Provide consumers with a choice of services
• The results have been, at best, mixed• Many places throughout the nation are planning such changes
despite the initial outcomes found in places such California• In the old systems, decision-making was centralized within the
managing monopolies (constrained system)• However, in deregulated systems decision-making is distributed
among many competing organizations (agents)
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Copyright 2004 Argonne National Laboratory
The Electricity Market CAS Model (EMCAS) Applies ABMS to Model Decentralized Electricity Markets
The Electricity Market CAS Model (EMCAS) Applies ABMS to The Electricity Market CAS Model (EMCAS) Applies ABMS to Model Decentralized Electricity MarketsModel Decentralized Electricity Markets
• EMCAS is an agent-based electricity market model
• EMCAS agents take on the roles of individual market participants (generators, distributors, transmission system operators, demand aggregators, customers, regulators)
• The agents operate in multiple layers within nested time scales (hourly, daily, weekly, monthly, yearly, multi-year)
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH FUNCTIONS
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
BUSINESS LAYERS
PHYSICAL LAYER
REGULATORY LAYER
Consumer Contracts and Tariffs
Demand Agents
Generation Companies Generators
Generator Ownership
Bilateral Contracts
GENE
RATI
ON C
OMPA
NIES
and
DEM
AND
AGEN
TS
Consumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information System
REGULATOR
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH FUNCTIONS
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH FUNCTIONS
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
BUSINESSLAYERS
PHYSICAL LAYER
REGULATORY LAYER
Consumer Contracts and Tariffs
Demand Companies
Generation Companies Generators
Generator Ownership
Bilateral Contracts
GENE
RATI
ON C
OMPA
NIES
and
DEM
AND
COM
PANI
ESConsumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information System
Consumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information SystemMarket Information System
REGULATORREGULATORREGULATORSpecial Event
Generator
Special Event
Generator
Special Event
Generator
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Copyright 2004 Argonne National Laboratory
EMCAS Is Designed to Explore System PossibilitiesEMCAS Is Designed to Explore System PossibilitiesEMCAS Is Designed to Explore System Possibilities
• “Prediction” is not a goal• ECMAS is intended to provide ranges of possibilities rather
than “point answers:”– The ranges of possibilities are created through multiple
simulations– The ranges are intended to discover potential weaknesses in
electricity markets rather than say whether or not a given agent (company) will actually exploit a given weakness
• These results can be used by decision makers to form better market policies (market rules) and make better market decisions
• The focus is on supporting decisions by exploring potential consequences of different market conditions (initial and changing)
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Copyright 2004 Argonne National Laboratory
EMCAS Operates atSix Time Scales or Decision Levels
EMCAS Operates atEMCAS Operates atSix Time Scales or Decision LevelsSix Time Scales or Decision Levels
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Copyright 2004 Argonne National Laboratory
Live Simulations Were Used toPrototype EMCAS
Live Simulations Were Used toLive Simulations Were Used toPrototype EMCASPrototype EMCAS
• To better understand the requirements of decentralized electricity market modeling, a live electricity market simulation was created
• The live simulation that was developed used individuals to play the role of generation companies:– Each generation company in the market simulation game had three
generators– Players submitted bids electronically based on publicly posted:
Prices
Demands
Supplies
Weather• One additional person played the role of the system operator
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Copyright 2004 Argonne National Laboratory
The Live Simulation includedExogenous Variability
The Live Simulation includedThe Live Simulation includedExogenous VariabilityExogenous Variability
• The system operator collected the players’ bids on a periodic basis and used to them to simulate the operation of an electricity spot market:– The simulation calculated market prices and player profits
based on internally derived demands, supplies, and weather– The actual simulation demands, supply, and weather differed
from the publicly posted projections by small random amounts– Generating units also suffered from unannounced random
outages• Several versions of the live simulation where run• Results from the live simulation contributed to the design
of EMCAS
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Copyright 2004 Argonne National Laboratory
Several Types of Agents Illustrate the ABMS Approach Resulting from the Live Simulation
Several Types of Agents Illustrate the ABMS Approach Several Types of Agents Illustrate the ABMS Approach Resulting from the Live Simulation Resulting from the Live Simulation
• Generation company agents illustrate ECMAS’approach to modeling competitive decision-making
• Independent System Operator/Regional Transmission Organization (ISO/RTO) agents illustrate ECMAS’approach to modeling coordinated behavior
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH FUNCTIONS
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
BUSINESS LAYERS
PHYSICAL LAYER
REGULATORY LAYER
Consumer Contracts and Tariffs
Demand Agents
Generation Companies Generators
Generator Ownership
Bilateral Contracts
GENE
RATI
ON C
OMPA
NIES
and
DEM
AND
AGEN
TS
Consumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information System
REGULATOR
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH FUNCTIONS
Consumers
Transmission Link
Transmission Node Generators
ISO/RTO/ITP DISPATCH FUNCTIONS
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
TRAN
SMIS
SION
and
DIST
RIBU
TION
COMPA
NIES
DistributionCompanies
TransmissionCompanies
DistributionService Territory
BUSINESSLAYERS
PHYSICAL LAYER
REGULATORY LAYER
Consumer Contracts and Tariffs
Demand Companies
Generation Companies Generators
Generator Ownership
Bilateral Contracts
GENE
RATI
ON C
OMPA
NIES
and
DEM
AND
COM
PANI
ESConsumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information System
Consumers
POOL
MARKETS
ENERGY MARKET ANCILLARY SERVICES MARKET
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
ISO/RTO/ITP MARKET
OPERATION FUNCTIONS
Market Information SystemMarket Information System
REGULATORREGULATORREGULATORSpecial Event
Generator
Special Event
Generator
Special Event
Generator
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Copyright 2004 Argonne National Laboratory
Generation Company Agents Facea Difficult Situation
Generation Company Agents FaceGeneration Company Agents Facea Difficult Situationa Difficult Situation
• Generation company agents sell generation into each of several markets, with different rules in each market:– There is a bilateral contract market that is privately negotiated
between individual buyers and sellers– There is a “spot” energy futures market that is centrally cleared– There are four backup generation options markets that are each
centrally cleared• The decision-making process for generation company agents is
difficult:– The commodity they produce (electric power) cannot typically be
stored– The power “transportation” system (the electric grid) follows well
understood, but highly complicated, rules– Generation company agents have limited knowledge about the other
players in the market
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Copyright 2004 Argonne National Laboratory
The Power “Transportation” System FollowsHighly Complicated Rules
The Power The Power ““TransportationTransportation”” System FollowsSystem FollowsHighly Complicated RulesHighly Complicated Rules
GeneratorCustomer 2
Customer 1
Customer 3
3
1
1
0.5
0.5
1
233
•All power in tens of MW•Notional example
$10/MWh
$25/MWh
$15/MWh
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Copyright 2004 Argonne National Laboratory
Generation Company Agent Decisionsare Based On Several Factors (1 of 2)
Generation Company Agent DecisionsGeneration Company Agent Decisionsare Based On Several Factors (1 of 2)are Based On Several Factors (1 of 2)
• The success of generation company agent decisions are not guaranteed
• Agents “weigh” the relative rewards of success against the costs and risks of failure
• The anticipated success or failure rate is based on experience:– Each generation company agent keeps an ongoing private
record of historical events (i.e., private memory) including a history of decisions made in the past and these results of those decisions under various supply and demand conditions
– Information such as system outages, loads, location-based market prices are posted by the ISO on publicly available bulletin board
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Copyright 2004 Argonne National Laboratory
Generation Company Agent Decisionsare Based On Several Factors (2 of 2)
Generation Company Agent DecisionsGeneration Company Agent Decisionsare Based On Several Factors (2 of 2)are Based On Several Factors (2 of 2)
• The level of risk that an agent is willing to take is an integral part of its decision-making:– More conservative agents that have a lower tolerance for risk
may have lower profits but have a steady stream of income– More aggressive agents may have the potential for higher
profits but experience financial failure if anticipated market behaviors do not come into fruition
• Some business choices that the generation company agent can consider– Bid on contracts or bid into the pool market– Bid into the energy market and/or the ancillary services market– Adjust/change bid price strategy (production cost, low bid to
ensure acceptance, bid high on last portion of capacity, withhold capacity)
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Copyright 2004 Argonne National Laboratory
Generation Company Agents Use a Sophisticated Decision-Making Process
Generation Company Agents Use a Sophisticated Generation Company Agents Use a Sophisticated DecisionDecision--Making ProcessMaking Process
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Copyright 2004 Argonne National Laboratory
The Decision-Making Processes ofGeneration Company Agents Allows Learning
The DecisionThe Decision--Making Processes ofMaking Processes ofGeneration Company Agents Allows LearningGeneration Company Agents Allows Learning
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Copyright 2004 Argonne National Laboratory
ISO/RTO Agents Act as Coordinators (1 of 2)ISO/RTO Agents Act as Coordinators (1 of 2)ISO/RTO Agents Act as Coordinators (1 of 2)
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Copyright 2004 Argonne National Laboratory
ISO/RTO Agents Act as Coordinators (2 of 2)ISO/RTO Agents Act as Coordinators (2 of 2)ISO/RTO Agents Act as Coordinators (2 of 2)
• ISO/RTO agents match buyers and sellers in each of the five public markets
• ISO/RTO agents approve bilateral contracts to insure physical stability
• ISO/RTO agents manage payments for the public markets• ISO/RTO agents post information on a public bulletin board:
– Historical generation, outages, weather, loads, and location-based prices are posted
– Projected outages, weather, and loads are posted
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Copyright 2004 Argonne National Laboratory
An EMCAS Case has been CreatedBased on the Live Simulation
An EMCAS Case has been CreatedAn EMCAS Case has been CreatedBased on the Live SimulationBased on the Live Simulation
• Specific agents representing individual live simulation players were implemented by using EMCAS’ agent architecture:– The strategies of the individual players were determined by asking
them to write short descriptions of their approaches after the completion of the live simulation and then following up the writing with a series of focused interviews
– Once the strategies were determined, agents implementing each of the strategies were programmed
• The individual agents developed to emulate the live simulation players were run using the same dataoriginally used for the live simulation
• EMCAS closely matched the resultsof the six player live simulation
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Copyright 2004 Argonne National Laboratory
EMCAS Is Undergoing Thorough Verification and Validation Using a Variety of Techniques
EMCAS Is Undergoing Thorough Verification and EMCAS Is Undergoing Thorough Verification and Validation Using a Variety of TechniquesValidation Using a Variety of Techniques
• Unit testing has and is being preformed for the main components currently in use
• EMCAS’ internal algorithms have been and are being reviewed in detail by domain experts
• EMCAS output has and is being compared to analytically solvable special cases
• EMCAS output has and is being compared to some historical cases
• EMCAS is being expanded so the verification and validation is continuing!
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Copyright 2004 Argonne National Laboratory
Complexity Can Be Captured with Agent-Based Modeling and Simulation!
Complexity Can Be Captured with AgentComplexity Can Be Captured with Agent--Based Based Modeling and Simulation!Modeling and Simulation!
• CAS are structures composed of many components that interact and reproduce while adapting to a changing environment
• CAS often have numerous nested levels of interaction that span many scales of measurement
• ABMS can be used to build models for both single-scale and multi-scale CAS
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Copyright 2004 Argonne National Laboratory
Can Complexity Be Captured with Agent-Based Modeling and Simulation?
Can Complexity Be Captured with AgentCan Complexity Be Captured with Agent--Based Based Modeling and Simulation?Modeling and Simulation?
Are there additional questions?
Michael Northnorth@anl.gov
www.cas.anl.gov
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