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Multi-objective resilience enhancement program in smart grids during extreme weather conditions

机译:极端天气条件下智能电网的多目标恢复力增强计划

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Weather based electrical power outages cover a huge part of consumer interruptions. So, reliable-economical operation of grids during extreme weather conditions is one of challenges for grid operators. In this regard, this paper proposes resilience enhancement programs in order to increase resilience and economic profits in a smart grid. In proposed approach, resilience improvement is done by modeling weather effects on branches outages and then re-scheduling of distributed energy resources and energy storages, load shifting and dynamic reconfiguration of distribution network. In this paper, hourly variation of weather depended failure probabilities are considered. Resilience enhancement programs aim to mitigate effects of events which may cause by extreme weather before fault inception by rescheduling of resources and selecting suitable reconfigurations. Also, reconfiguration isolates damaged parts after fault inception. The objectives in the proposed approach are defined as minimizing operational cost of distribution network and energy not supplied penalty costs from the system operator?s viewpoint, as well as, maximizing benefits of energy resources owners by considering weather conditions. A multi-objective optimization algorithm based on genetic algorithm and epsilon constraint method using fuzzy decision maker is employed to choose the best solution from a provided Pareto optimal set. In order to evaluate performance of proposed resilience enhancement programs and its effect, resilience assessment metrics are studied. Various simulations prove the efficiency of proposed model in compare with traditional grid during extreme weather conditions.
机译:天气基于电力停电涵盖了消费者中断的巨大部分。因此,极端天气条件期间网格的可靠 - 经济运行是电网运营商的挑战之一。在这方面,本文提出了抵御振兴增强计划,以提高智能电网中的恢复力和经济利润。在拟议的方法中,通过对分支机构的天气影响建模,然后重新调度分布式能源资源和能量存储,负载转换和分发网络的动态重新配置来完成恢复性改进。在本文中,考虑了天气的每小时变化依赖失败概率。恢复能力增强计划旨在减轻事件的影响,这些事件可能会通过重新安排资源并选择合适的重新配置来减轻故障初始的最终天气。此外,重新配置在故障初始后隔离损坏的部件。所提出的方法的目标被定义为最小化分销网络和能源的运营成本,从系统运营商的观点以及通过考虑天气状况来最大限度地提高能源资源所有者的益处。基于模糊决策者的基于遗传算法和epsilon约束方法的多目标优化算法用于从提供的帕累托最优集中选择最佳解决方案。为了评估拟议的恢复力增强计划及其效果的表现,研究了弹性评估度量。各种模拟在极端天气条件下与传统网格相比,拟议模型的效率。

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