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Strategic bidding in a day-ahead market by coevolutionary genetic algorithms

机译:通过协同进化遗传算法在日前市场中进行战略投标

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In the present work, the problem of energy market price clearing and generation company (Genco) strategic bidding is considered in the framework of existing day-ahead markets with system marginal price auction. The situation of imperfect competition arising when one of the Gencos is large enough to exert market power is considered in detail, showing what bidding behaviors are to be expected when such a market arrangement occurs. The impact that inter-area transmission system congestions may have on the mechanism of system pricing is also addressed. The bidding problem faced by each Genco is formulated as a strategic multi-player game in which the choice between different bidding levels and energy amounts to be sold at the market has to be made. The large size of the problem due to the number of competitors and to the presence of transmission constraints makes the application of classical game theory troublesome. Therefore, an agent based method belonging to the category of coevolutionary genetic algorithm was selected for the solution of this problem. Test cases illustrate the different strategies that the Gencos may implement to optimize their performance at the day-ahead market. Beside some small didactical examples, the situation of the Italian day-ahead market is considered in detail.
机译:在当前的工作中,在系统边际价格拍卖的现有日前市场框架内,考虑了能源市场价格结算和发电公司(Genco)的战略招标问题。详细考虑了其中一个Gencos足够大以发挥市场力量时出现的不完全竞争的情况,表明了在发生这种市场安排时预期的竞标行为。还解决了区域间传输系统拥塞可能对系统定价机制产生的影响。每个Genco面临的竞标问题被表述为战略性多人游戏,其中必须在不同的竞标水平和要在市场上出售的能量之间进行选择。由于竞争者的数量以及传输限制的存在,问题的规模很大,这使得经典博弈论的应用很麻烦。因此,选择了一种基于代理的方法来解决这个问题,该方法属于协同进化遗传算法的范畴。测试案例说明了Gencos可能采取的不同策略,以在日前市场上优化其性能。除了一些小的教学示例外,还详细考虑了意大利日前市场的情况。

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