An agent-based negotiation platform for power generating and power consuming (purchasing) companies in contract electricity market is presented. An intelligent agent implements the negotiation process by selecting a strategy based on learning algorithm in an interactive manner with the user. Two kinds of learning algorithm---fuzzy logic controller modification of basic Genetic Algorithm for negotiation strategy optimization, and reinforced learning algorithm for negotiation tactics parameter modification---are provided for the agent. Protocol Operation Semantics that is flexible and can handle sequential message exchange is used as agent communication mechanism. The paper presents the architecture and negotiation strategy of agents. The software implementation of the platform is discussed in detail.
Abstract--The restructured electricity market is a complex adaptive system and worldwide experiences show that market design is a complicated task. Recently, under the paradigm of Agent-based computational economics (ACE) a new research focus is forming and a large number of literatures are springing up, but there is still no the discussion on ACE's theoretical value and insufficiency in the research of electricity market in the hierarchy of methodology, therefore the author's research is an attempt in this aspect. By means of analyzing the evolution of economics methodology from mathematical deduction to simulation induction and their inherent relevance, the unique superiority of ACE on the level of methodology is expounded. The further selective survey on existing literatures shows that with the ACE model the marketization process can be understood clearly in deeper level and wider scope. Finally to give a reference to theoretical progress, the prospect application of ACE, especially its potential in China's electric sector restructuring is discussed.
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