Electrical energy consumption varies in different markets. Several different types of generators are used to supply electricity for consumers. The balance between supply and demand leads to prevent lack of energy. Nonetheless, with the growing number of markets and consumers, larger amount of data is generated making the analysis harder. Thus, decision support architecture for analytical purposes is significant. Large amount of data, recently called Big Data, is one of the significant sources of gaining and analyzing information as a decision support for electrical energy markets (EEMs). Market policymakers are emphasizing the impact of analytical approaches for business strategy setting in power supply and consumption to mitigate the risk of power failure and disruptions. In this article, the EEM influenced by big data of supply and demand and disruption is investigated to achieve power business continuity. A comprehensive architecture for EEM process is proposed. Supply and demand cost analysis is performed based on disruptions for an EEM. A pricing-based profit scenario optimization in a dynamic supply network having multiple power states is worked out. Numerical experiment is performed to show the effectiveness of the proposed paradigm based on data management.

中文翻译：

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考虑成本导向大数据的供电网络中断风险利润分析

不同市场的电能消耗各不相同。几种不同类型的发电机用于为消费者提供电力。供需平衡可以防止能源短缺。尽管如此，随着市场和消费者数量的不断增加，产生的数据量也越来越大，使得分析变得更加困难。因此，用于分析目的的决策支持架构非常重要。大量数据（最近称为大数据）是获取和分析信息的重要来源之一，可作为电力市场 (EEM) 的决策支持。市场政策制定者强调分析方法对电力供应和消费业务战略制定的影响，以减轻电力故障和电力中断的风险。本文研究了受供需大数据和中断影响的EEM，以实现电力业务连续性。提出了 EEM 流程的综合架构。供需成本分析是根据 EEM 的中断进行的。制定了具有多种功率状态的动态供应网络中基于定价的利润场景优化。进行数值实验以证明所提出的基于数据管理的范例的有效性。