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.