Please use this identifier to cite or link to this item: https://doi.org/10.1109/CEC.2007.4424549
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dc.titleCo-evolutionary algorithms for evolving buyers' bidding strategies in an electrical power market
dc.contributor.authorSrinivasan, D.
dc.contributor.authorTham, C.K.
dc.contributor.authorWu, C.
dc.date.accessioned2014-10-07T04:42:31Z
dc.date.available2014-10-07T04:42:31Z
dc.date.issued2007
dc.identifier.citationSrinivasan, D.,Tham, C.K.,Wu, C. (2007). Co-evolutionary algorithms for evolving buyers' bidding strategies in an electrical power market. 2007 IEEE Congress on Evolutionary Computation, CEC 2007 : 774-781. ScholarBank@NUS Repository. <a href="https://doi.org/10.1109/CEC.2007.4424549" target="_blank">https://doi.org/10.1109/CEC.2007.4424549</a>
dc.identifier.isbn1424413400
dc.identifier.urihttp://scholarbank.nus.edu.sg/handle/10635/83554
dc.description.abstractThis paper presents the application of two co-evolutionary algorithms for evolving buyers' bidding strategies in a restructured pool-type electrical power market. A "greedy" algorithm which always aims to get higher power and pay less locational marginal price, as well as a "demand-driven" algorithm which aims to follow closely the individual demand, have been analyzed and implemented in simulations under different market scenarios. The two distinctive algorithms were compared against each other in a simulated power market of a reasonably large scale with 7 buyers and 20 sellers in an IEEE 14 bus network. The PowerWorld® simulator has been used as a tool to ensure that the system validity and various constraints have been met. The simulation results suggest that a "demand-driven" co-evolutionary algorithm is more effective as it does not only help buyers to save cost when supply in the market is sufficient, but also enables them to outbid their opponents easily during tougher situations, such as when supply is in great shortage. © 2007 IEEE.
dc.description.urihttp://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1109/CEC.2007.4424549
dc.sourceScopus
dc.typeConference Paper
dc.contributor.departmentELECTRICAL & COMPUTER ENGINEERING
dc.description.doi10.1109/CEC.2007.4424549
dc.description.sourcetitle2007 IEEE Congress on Evolutionary Computation, CEC 2007
dc.description.page774-781
dc.identifier.isiutNOT_IN_WOS
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