Mikhail L. Voskoboinikov, Alexander G. Feoktistov

Matrosov Institute for system dynamics and control theory of SB RAS

Studying and enhancing the resilience of energy infrastructure is an actual problem that is associated with high computational complexity of preparing and conducting experiments. The complexity of experiments is due to the range of important factors. These factors include large sets of scenarios for significant external disturbances affecting the infrastructure under study, identification of its critical elements whose failure could lead to significant disruptions in the energy resource generation, transportation, and supply, as well as planning activities aimed at enhancing this infrastructure's resilience. Solving these problems in a computing environment based on executing scientific workflows using a distributed database in the RAM of the environment nodes can significantly reduce the computation time. However, this approach is not supported by known workflow management systems. In this context, we propose a new approach to implement the analyzing the energy infrastructure resilience using the Framework for Development and Execution of Scientific WorkFlows and distributed databases. Specifically, we created a scientific workflow to analyze energy infrastructure resilience. Next, we developed a method for predicting the required RAM size for workflow execution. Then, we implemented a set of testbeds (system workflows) to perform computing according to this method. This method takes into account key parameters in the subject area that significantly impact changes in data size. Finally, we conducted a computational experiment to demonstrate the accuracy of predicting the required RAM size when studying two test models of energy infrastructures of different complexities.

energy infrastructure, modeling, distributed computing environment, in-memory computing, workflows, automation