基于大数据的电网停电承载力分析与应用

为避免调度计划安排超出电网停电承载力,平衡繁重的电网停电任务与电网运行安全裕度之间的关系,保证调度计划刚性执行,有必要在安排停电计划前进行电网停电承载力分析。大数据理论和技术的发展为电网停电承载力分析提供了理论依据和技术条件。本文归纳了进行电网停电承载力分析所需的大数据来源,通过研究历史大数据对影响电网停电承载力的主要因素进行了分析,提出了基于神经网络的电网停电承载力分析模型和方法,通过绿色安全区、黄色预警区和红色危险区直观评价停电数量是否合理,并给出了该方法在山东电网的应用案例。案例表明,本文提出的模型和方法能够有效指导调度计划编制工作,显著提升工作效率并减轻对个人经验的依赖,对调度计划刚性执行和电网停电风险管控具有重要意义。

Analysis and application of power outages safety bearing capacity based on big data

It is necessary to analyze the safety bearing capacity of power outages before arranging dispatch plans to prevent dispatch plans from overload, balance the heavy work of power outages with security margin of power grid operation, and ensure the execution of dispatch plans. The development of big data theories and technologies provide theoretical basis and technical conditions for safety bearing capacity analysis of power outages. Firstly, this paper introduced the big data sources for safety bearing capacity analysis of power outages, and analyzed the major factors which impacted the safety bearing capacity of power outages by studying historical big data. Secondly, this paper presented the model and method to analyze the safety bearing capacity of power outages based on neural network, in which the number of dispatch plans was directly evaluated through green safety region, yellow early-warning region and red dangerous region. Finally, an application case in Shandong power grid was given. The case shows that the method presented by this paper could guide dispatch plans arrangement effectively, improve work efficiency significantly, alleviate the dependence on personal experience, and have great significance for strict execution of dispatch plans and risk control of power grid outages.