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基于双边合同二次交易的高低匹配竞价机制Swarm仿真
引用本文:刘贞,任玉珑,王恩创,唐皓阳. 基于双边合同二次交易的高低匹配竞价机制Swarm仿真[J]. 电力系统自动化, 2007, 31(18): 26-29
作者姓名:刘贞  任玉珑  王恩创  唐皓阳
作者单位:重庆大学经济与工商管理学院,重庆市,400030;重庆大学经济与工商管理学院,重庆市,400030;重庆大学经济与工商管理学院,重庆市,400030;重庆大学经济与工商管理学院,重庆市,400030
摘    要:在发电侧电力市场双边竞价过程中,为了约束发电商的市场力,提出一种基于双边合同二次交易的高低匹配竞价机制。为了验证该机制的正确性,构建了基于Swarm的多主体仿真模型。在模型中考虑了报价接受风险因素,从而使模型与现实中的报价行为更加接近。在多主体博弈过程中,主体具有自治能力,可以采取学习博弈方法,充分利用所获取的信息,在竞价过程中不断修正其价格和电量的申报策略。通过对南方某电力市场进行仿真,发现基于双边合同二次交易的竞价机制可以把发电商的市场力约束在一个较小的范围内。

关 键 词:发电侧电力市场  市场力  高低匹配竞价机制  Swarm仿真  双边合同  学习博弈
收稿时间:2006-12-10
修稿时间:2007-09-05

Swarm Simulation on High-low Matching Auction Mechanism Based on Secondary Trade of Bilateral Contract
LIU Zhen,REN Yulong,WANG Enchuang,TANG Haoyang. Swarm Simulation on High-low Matching Auction Mechanism Based on Secondary Trade of Bilateral Contract[J]. Automation of Electric Power Systems, 2007, 31(18): 26-29
Authors:LIU Zhen  REN Yulong  WANG Enchuang  TANG Haoyang
Affiliation:Chongqing University, Chongqing 400030, China
Abstract:In order to restrict market power of power generators in bilateral bidding process at generation-side electricity market,a high-low matching auction mechanism based on secondary trade of bilateral contract is proposed.A Swarm based multi-agent simulation model was constructed to test the validity of the mechanism.As the bidding risk has been considered in the model,the simulation model is much closer to the bidding behaviors in reality.In the multi-agent game process,the agents have the autonomous ability,and could adopt the learning game method,and make full use of the obtained information to modify their power-price bidding strategies and output decisions continually.A simulation is conducted on an electricity market in South China.The results show that the auction mechanism based on the secondary trade of bilateral contract could restrict the market power of power generators within a smaller scope.
Keywords:generation-side electricity market   market power   high-low match auction mechanism   Swarm simulation   bilateral contract   learning game
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