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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)
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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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