Department of Telecommunications MASTER THESIS Nr. 608 MASTER THESIS Nr. 608 INTELLIGENT TRADING...
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Transcript of Department of Telecommunications MASTER THESIS Nr. 608 MASTER THESIS Nr. 608 INTELLIGENT TRADING...
Department of Telecommunications
MASTER THESIS Nr. 608
INTELLIGENT TRADING AGENT FOR POWER TRADING THROUGH
WHOLESALE MARKET
Ivo Buljević
2012/2013
Zagreb, July 2013
Department of Telecommunications
Contents
¨ Introduction¨ Smart grid¨ Wholesale market¨ CrocodileAgent 2013¨ Conclusion
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Department of Telecommunications
Introduction
¨ Characteristics of the traditional energy market: Centralized Vertically integrated market structure No competition
¨ Liberalization and deregulation of the traditional energy market
¨ Increased number of renewable energy sources ¨ Progressive transformation of traditional power
systems into evolved systems called smart grids
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Department of Telecommunications
Smart grid
¨ A modernization concept of the electricity delivery system¨ Enables real-time banacing of energy supply and demand¨ A two-way flow of electricity and information
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¨ Multi-agent market models Entities are represented by
intelligent software agents Opportunity to test software
solutions in order to prevent market crashes (California 2001)
Department of Telecommunications
Wholesale market
¨ Result of liberalization and deregulation of the traditional energy market, enables energy trade between market entities
¨ Power exchanges and power pools¨ Day-ahead market¨ Examples of wholesale markets:
Chile Great Britain and Wales Nord Pool California
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Department of Telecommunications
Wholesale market (2)
¨ Energy load forecasting Statistical approach
Similar-day method Exponential smoothing Regression methods
Artifficial intelligence – based tecniques Reinforcement learning
¨ Energy price forecasting Spike preprocessing Time series models with exogenous variables Interval forecasts
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Department of Telecommunications
CrocodileAgent 2013
¨ Intelligent software agent developed at University of Zagreb
¨ Participant of PowerTAC 2013¨ Main emphasis:
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Development of wholesale bidding strategy which will minimize negative effects on the balancing market
Responsive and context-aware agent design
Department of Telecommunications
CrocodileAgent 2013Modular architecture
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CLEARINGINFORMATION
WHOLESALE MARKET
CUSTOMER MARKET
GENCOS OTHER BROKERS
C1 C2 C3
WEATHER
PAST ENERGY USAGE
ALL FORECASTED DATA
PAST CLEARING PRICES
BID/ASK TARIFFS
CONSUMPTION PRODUCTION INTERUPTABLE CONSUMPTION
Office complexVillage types
Centerville homes
Solar
Wind
Frosty storageHeat Pump
FORECAST MANAGER
TARIFF MANAGER MARKET MANAGER
MARKET REPOSITORY
TARIFF REPOSITORY
MAIN SERVICE (MESSAGE SENDER/
RECEIVER)
OTHER TARIFF SPECIFICATION, TRANSACTION
PUBLISH TARIFFS
PASTUSAGE
FUTURE ENERGY USAGE/PRICES
CURRENT WHOLESALESTATE
BIDS/ASKS
SEND TO SERVER
CrocodileAgent 2013
LEARNING MODULE
BIDDING STRATEGIES
GENERATEDORDERS
ENERGYPRICES
NEEDEDENERGY
Contribution of this master thesis
Department of Telecommunications
CrocodileAgent 2013Learning module
¨ Based on reinforcement learning Erev-Roth method specially adapted for PowerTAC
wholesale market¨ Enables broker to adapt to various market
conditions¨ Key features:
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Multiple strategies Advanced strategy
evaluation based on its efficiency
RL module Simulator
InitializationChoose strategy
ExecuteResults
Set rewards
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CrocodileAgent 2013Learning module (2)
¨ Uses basic order as an input Generated by forecast module, based on past usage of
subscribers on the retail market Holt-Winters method
¨ Life cycle:
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Initialization Choose strategy Place order Set reward
¨ Strategies used to model amount of energy and unit price
Department of Telecommunications
CrocodileAgent 2013Results
¨ Broker progressively learns to adapt to current market conditions – manifestation of the learning period Minimization of balancing cost
¨ Broker buys an excessive amount of energy on the wholesale market
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Results from May trial indicates that broker buys 125% of energy needed on the retail market
A need to optimize basic order generation (energy load forecasting)
Department of Telecommunications
Conclusion
¨ Robustness of the CrocodileAgent’s wholesale module Broker is able to adapt to changes in competition
environment¨ Adapted Erev-Roth algorithm was proved to be
suitable for the PowerTAC wholesale market¨ Future work:
Improvement of energy load forecasting Improvement in unit price calculation Design of intelligent strategies
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