Journal of Korean Institute of Industrial Engineers (대한산업공학회지)

Korean Institute of Industrial Engineers (대한산업공학회)
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Estimating Optimized Bidding Price in Virtual Electricity Wholesale Market

가상 전력 도매 시장의 최적 경매 가격 예측

  • Shin, Su-Jin (Department of Industrial and Systems Engineering, KAIST) ;
  • Lee, SeHoon (Department of Industrial and Systems Engineering, KAIST) ;
  • Kwon, Yun-Jung (Department of Industrial and Systems Engineering, KAIST) ;
  • Cha, Jae-Gang (Department of Industrial and Systems Engineering, KAIST) ;
  • Moon, Il-Chul (Department of Industrial and Systems Engineering, KAIST)
  • 신수진 (KAIST 산업 및 시스템공학과) ;
  • 이세훈 (KAIST 산업 및 시스템공학과) ;
  • 권윤중 (KAIST 산업 및 시스템공학과) ;
  • 차재강 (KAIST 산업 및 시스템공학과) ;
  • 문일철 (KAIST 산업 및 시스템공학과)
  • Received : 2013.04.12
  • Accepted : 2013.08.09
  • Published : 2013.12.15
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Abstract

Power TAC (Power Trading Agent Competition) is an agent-based simulation for competitions between electricity brokering agents on the smart grid. To win the competition, agents obtain electricity from the electricity wholesale market among the power plants. In this operation, a key to success is balancing the demand of the customer and the supply from the plants because any imbalance results in a significant penalty to the brokering agent. Given the bidding on the wholesale market requires the price and the quantity on the electricity, this paper proposes four different price estimation strategies: exponentially moving average, linear regression, fuzzy logic, and support vector regression. Our evaluations with the competition simulation show which strategy is better than which, and which strategy wins in the free-for-all situations. This result is a crucial component in designing an electricity brokering agent in both Power TAC and the real world.

Keywords

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