Profit analytics in disruption risk for electrical energy supply network considering cost-oriented big data

Abstract

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.