click to edit master subtitle style complex systems science and csiro: into the future rydges hotel,...
Post on 22-Dec-2015
217 views
TRANSCRIPT
Click to edit Master subtitle styleComplex Systems Science and CSIRO: Into the Future
Rydges Hotel, Melbourne
10-12 August 2005
NEMSIM: Practical Challenges for Agent-based Simulation of
Energy Markets
George Grozev and David BattenCSIRO Manufacturing and Infrastructure Technology
www.csiro.au
Presentation Overview
History and concepts (NEM as a CAS)
NEMSIM overview and key features
Practical challenges for agent-based simulation
www.csiro.au
Brief Historical Review of the Project
July 2002: A postdoc position awarded. Dr. Xinmin Hu started in Nov. 2002.
January 2003: Commenced as a CSS project: “Top-up” funding from CSIRO’s Centre for Complex Systems Science
October 2003: Commenced as a Theme 1 project in CSIRO’s Energy Transformed Flagship Program
April 2004: Flagship Science e-Seminar Series 2: Energy Transformed (John Wright, David Batten)
April 2005: NEMSIM Industry Focus Group Meeting (Mercure Hotel, Melbourne)
www.csiro.au
NEMSIM: National Electricity Market Simulator
Agents in NEMSIM:• 27 Scheduled Generator Companies
• 12 Non-scheduled Generator Companies
• 20 Network Service Providers
• 29 Market Customers
• 9 Traders
• An Independent System Operator (NEMMCO)
Potential Clients:• Regulators (ACCC, AER, AEMC)
• Government (DITR, SA, Tasmania)
• TNSPs (Powerlink, Transgrid)
• Customers (EUAA, ERAA, Origin, AGL)
www.csiro.au
The NEM is a Complex Adaptive System
Evolving markets on an interconnected grid about 100 interacting, autonomous agents (firms) + others
about 300 grid-connected, generating units.
Agents are intelligent, adaptive & behave differently pursue goals unique to their firms’ interests
make decisions on the basis of their own knowledge/beliefs
change strategies in the light of their and others’ experiences.
No agent knows what all the other agents are doing each agent has access to only a limited amount of information.
Some act more conservatively than others e.g. they are more constrained (e.g. by debt) than others.
www.csiro.au
Our Science – Agent-based Simulation
Equations-based models too static, aggregate or stylized to handle this complexity
Agent-based simulation computational experiments
software agents, environments and rules
agents learn and adapt strategies over simulated time
evolutionary computation and equations-based methods
can explore impacts of rule changes before their introduction
can evolve adaptive responses of competitors
collective outcomes can be unexpected, even undesirable
www.csiro.au
Presentation Overview
History and concepts (NEM as a CAS)
NEMSIM overview and key features
Practical challenges for agent-based simulation
www.csiro.au
FuelResources
GHGEmissions
Companies
Contract Market
Spot Market
Market Operator
InterconnectorsGeneratingPlants
GeneratingUnits
TransmissionLines
Demand Prices
DispatchBidding
Power Losses
Sim
ula
ted
Sce
nar
io
Simulation Engine
Reports
0
5
10
15
20
25
30
1 2 3
Graphs
HistoricalData
Environment
TechnicalInfrastructure
Agents&
Markets
SimulationLogTables
ElectricitySupplied
DailyBidding
SpotPrices
DemandEvolution
ContractPrices
SupplyEvolution
InvestmentDecisions
GHGEmissions
Tim
eH
orizo
ns
30 min Dispatch
DailyDecisions
WeeklyDecisions
MonthlyDecisions
YearlyDecisions
Longer TermDecisions
User
Scenario Evaluation
Data Input
Input
FuelResources
GHGEmissions
Companies
Contract Market
Spot Market
Market Operator
InterconnectorsGeneratingPlants
GeneratingUnits
TransmissionLines
Demand Prices
DispatchBidding
Power Losses
Sim
ula
ted
Sce
nar
io
Simulation Engine
Reports
0
5
10
15
20
25
30
1 2 3
Graphs
0
5
10
15
20
25
30
1 2 3
Graphs
HistoricalData
Environment
TechnicalInfrastructure
Agents&
Markets
SimulationLogTablesTables
ElectricitySupplied
DailyBidding
SpotPrices
DemandEvolution
ContractPrices
SupplyEvolution
InvestmentDecisions
GHGEmissions
GHGEmissions
Tim
eH
orizo
ns
30 min Dispatch
DailyDecisions
WeeklyDecisions
MonthlyDecisions
YearlyDecisions
Longer TermDecisions
30 min Dispatch
DailyDecisions
WeeklyDecisions
MonthlyDecisions
YearlyDecisions
Longer TermDecisions
UserUser
Scenario Evaluation
Data Input
Input
NEMSIM Overview
www.csiro.au
NEMSIM Overview - continued
www.csiro.au
NEMSIM – Generating Units Displays
Bid Stacks
Revenue GHG Emissions
Dispatch
www.csiro.au
Key Features
Includes all key players in the NEM
Models individual agent’s behaviour
Weather model and data from 100 years
Wholesale market model and extending to other markets, e.g. contract market
Potential effect of distributed generation
Transmission modelling
Bid strategies – e.g. lookaheads
Scenario investigations – new plants, maintenance, emergency shutdown, blackouts, new rules
Scenario comparisons
Reports – dispatch, revenue, CO2, by regions, by companies, by plants, weekly, monthly, yearly
Environmental markets, e.g. carbon trading
www.csiro.au
Other Important Features
XML editor
Simulation time control
Lookaheads
Scenario comparison
Distributed generation
New plants
Maintenance & shutdown
Reports
www.csiro.au
Area of Applications
Short-term trading analyse market bidding data
analyse “what-if” bidding scenarios
Medium-term hedging and contract markets (retailers, generators)
Long-term investment (new generators, transmission lines, distributed generation, renewables)
Greenhouse gas emissions estimates
Carbon trading (when rules are proposed)
Explore the impact of new technologies, new market rules, new grid structures, new participants
www.csiro.au
Presentation Overview
History and concepts (NEM as a CAS)
NEMSIM overview and key features
Practical challenges for agent-based simulation
www.csiro.au
Selection of Agent-based Simulation Platform
Develop our own platform
EMCAS - Argonne National Lab
DIAS/FACET – Argonne National Lab
RePast
Swinburne’s simulation framework – agent implementation of the Victorian Gas Market
www.csiro.au
Other Practical Challenges for NEMSIM
Adequately reflecting all the subtleties inherent in market-to-network interdependencies (DITR)
Developing efficient heuristic algorithms for interactive decision-making e.g. adaptive learning procedures e.g. multi-criteria decision-making
Distinguishing between counterintuitive results and programming errors
Keeping running times reasonable while adding more dynamic features
Developing confidence and trust among potential users and the market operator (NEMMCO)
www.csiro.au
Challenges of Learning in the NEM
Depending on their own competitive position, each generator behaves differently
Bidding strategies differ between states, but even more so between generators within states
Although strategies differ, we may be able to develop a generic bid function for all of them (just varying parameters/markups)
Most generators change bid capacities, occasionally changing bid prices (or price increments)
Thus each firm that owns generating units will need to be examined, if we wish to approximate reality
www.csiro.au
Potential Learning Algorithms
Genetic algorithms (see e.g. Goldberg, 1989, Mitchell, 1998, Chattoe, 1998, Dawid, 1999)
Genetic programming (see e.g. Koza, 1992)
Reinforcement learning algorithms (see e.g. Erev and Roth, 1998; Sutton and Barto, 1998)
Q-learning (see e.g. Watkins, 1989; Tesauro and Kephart, 2002)
Classifier systems (see. e.g. Holland, 1992)
Learning algorithms for automated markets (see e.g. Gjerstad and Dickhaut, 1998; Tesauro and Kephart, 1998)
www.csiro.au
• Bid acceptances/rejections• Unit utilization• Unit profitability• Market price vs. bid price• Weather and Load
NEMSIM agents can look ahead, sideways and back
• Own unit availability• Price trends/peak loads• Hedging strategy• Weather• Load forecasts
• Competing unit availability• Competing bids• Market rules
LOOK BACK (Short and Long Term Memory)
TIM
E
LOOK SIDEWAYS (Bidding rules)
LOOK AHEAD (Strategy evaluation)
Agent
www.csiro.au
Look-aheads in NEMSIM
Agents have look-ahead capabilities
• Run the simulation forward for various periods
• Test & compare a range of available strategies and plans
• Agent adopts strategy showing best possible outcome
• Strategies retested at start of each new period
• Plans/strategies changed to counter changes of others
Does a look-ahead capability add value to the existing (comparative static) approaches?
www.csiro.au
Value of a Look-ahead Capability
www.csiro.au
FG Meeting: Challenges for NEMSIM
Focus more, refine agents adaptive behaviour How agents think and interact, not just bid
Explore demand-side management options Locational issues Customers as agents
Differentiate between short and long-term Treat GHG/carbon tax/emissions trading Explore DG/wind/green power Talk to appropriate potential users
Regulators Government policy makers Network companies
www.csiro.au
Practical Advantages of NEMSIM
Practical application of a CSS methodology To a real world complex adaptive system (the NEM) Socio-economic/physical/environmental interactions
Each and every agent’s adaptive behaviour can be represented and modified
Different collective outcomes can be generated and performances compared in advance (“look-aheads”)
Conditions when unattractive outcomes occur (like price volatility & market power) can be identified
This kind of simulation goes beyond the classical simulation models in energy economics
User-friendly human-machine interface
www.csiro.au
Acknowledgments
Research and Development Group
Energy Transformed Flagship
Swinburne University of Technology
CMIT:
• George Grozev
• David Batten
• John Mo
• Miles Anderson
• Geoff Lewis
• Mario Sammut
CMAR:
• Jack Katzfey
• Marcus Thatcher
• Paul Graham – Theme Leader “Energy Futures”
• Terry Jones - Theme Leader “Low Emission Distributed Energy”
• Prof. Myles Harding
• Neale Taylor
UNSW:
• Xinmin Hu
Click to edit Master subtitle style
Thank you