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首页> 外文会议>2010 IEEE 11th International Conference on Probabilistic Methods Applied to Power System >A new solution for maintenance scheduling in deregulated environment applying Genetic Algorithm and Monte-Carlo Simulation
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A new solution for maintenance scheduling in deregulated environment applying Genetic Algorithm and Monte-Carlo Simulation

机译：基于遗传算法和蒙特卡洛模拟的无管制环境下维修计划的新解决方案

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This paper presents a new comprehensive solution for maintenance scheduling of generating units in deregulated environments by applying an independent market, based on Genetic Algorithm (GA) and Monte-Carlo Simulation (MCS). In a deregulated environment each Generation Company (GENCO) desires to optimize the payoffs while independent system operator (ISO) has its reliability solicitudes. Mostly, these two points of view create many contests. Therefore, the paper proposes a competitive area based on GA for maintenance scheduling. In this method, GENCOs are set their strategies to participate in Maintenance Market (MM) by considering load and fuel uncertainties besides considering the behaviours of other companies. On the other hand, ISO manages the MM based on reliability and offers incentives/ penalties for companies relying on its policy through MCS. For disclosing the accuracy and the applicability of this mentioned solution for maintenance scheduling of power generation units, IEEE reliability test system (RTS) has been studied.
机译：本文基于遗传算法（GA）和蒙特卡洛模拟（MCS），通过应用独立市场，提出了一种新的综合解决方案，用于在管制解除的环境中对发电机组进行维护调度。在放松管制的环境中，每个发电公司（GENCO）都希望优化收益，而独立系统运营商（ISO）具有其可靠性要求。通常，这两种观点会引起很多竞争。因此，本文提出了一种基于遗传算法的维修计划竞争区域。在这种方法中，GENCO通过考虑负荷和燃料的不确定性，同时考虑其他公司的行为，设定了参与维修市场（MM）的策略。另一方面，ISO基于可靠性来管理MM，并通过MCS为依赖其政策的公司提供激励/惩罚。为了公开该解决方案在发电设备维护计划中的准确性和适用性，已经研究了IEEE可靠性测试系统（RTS）。

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