Abstract
Nowadays, determining critical components of energy systems is a relevant problem. The complexity of its solving increases significantly when it is necessary to take into account the simultaneous failures of such components. Usually, in problem-solving, processing a large number of failure variants and their consequences is required. Processing such data using traditional relational database management systems does not allow us to quickly identify the most critical components. In the paper, our successful practical experience in applying an in-memory data grid within large-scale analyzing of the energy system vulnerability is provided. The experimental analysis showed the good scalability of distributed computing and significant reduction in data processing time compared to using an open-source SQL relational database management system. In developing and applying the distributed applied software package for solving the aforementioned problem we have used the Orlando Tools framework. Within its applying, we have implemented continuous integration of the package software taking into account the preparing and processing of subject-oriented data through the in-memory data grid.
Highlights
Nowadays, high-performance computing technologies, including software and hardware infrastructures for distributed computing, continue to develop
We propose a new approach based on applying an in-memory data grid within largescale analysis of the energy system vulnerability
Scientific application for analyzing the energy system vulnerability We have developed a scientific application for analyzing the energy system vulnerability using the Orlando Tools framework
Summary
High-performance computing technologies, including software and hardware infrastructures for distributed computing, continue to develop. Even when using high-performance computing, studying the simultaneous failure combinations of more than four components is difficult For both problems of the vulnerability analysis, the significant problem-solving time is largely owing to the processing model data using traditional relational database management systems. That the number of possible failure sets grows rapidly with increasing k This is one of the main problems of the storing and processing model data for the traditional relational database management systems. Using this scheme, we find the energy system critical components applying Apache Ignite data grid to process Monte Carlo simulation data. The resource configurations, information about installing and testing modules on these resources, and Apache Ignite configuration are stored on the Orlando Tools server
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