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http://dx.doi.org/10.7232/JKIIE.2013.39.6.562

Estimating Optimized Bidding Price in Virtual Electricity Wholesale Market  

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)
Publication Information
Journal of Korean Institute of Industrial Engineers / v.39, no.6, 2013 , pp. 562-576 More about this Journal
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
Economically-motivated agents; Application; Smart Grid; Trading Agent Competition; Power TAC;
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Times Cited By KSCI : 2  (Citation Analysis)
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