Search

Nowadays, multi-energy systems play an important role in satisfying the ever-increasing demand for different energy resources. At the same time, the sustainable development of such systems is usually based on the structural and parametric optimization (synthesis) of their infrastructures. There is a large spectrum of specialized optimization tools for the study of single energy systems. At the same time, the problem of modeling the interaction between single energy systems remains challenging. Therefore, it is imperative to develop an efficient experimental environment to effectively implement the synthesis of optimal configurations of multi-energy systems. Microgrids are a special case of multi-energy systems. They provide a higher level of energy supply compared to the main grids and enhance their reliability and resilience. In this context, we propose a framework and subject-oriented environment for the synthesis of optimal microgrid configurations in a reasonable time considering the available computational resources. The basis of the environment is a service-oriented application. The modeling and optimization of the studied systems is performed by means of scientific workflows. Our results complement and develop known approaches to automate the modeling of multi-energy systems using their typical models and specially selected optimization algorithms corresponding to these models. We have successfully tested our approach for the synthesis of optimal microgrid configurations on the case study of a specific microgrid providing heat and electricity to a small settlement. [ABSTRACT FROM AUTHOR]

Implementing high-performance computing (HPC) to solve problems in energy infrastructure resilience research in a heterogeneous environment based on an in-memory data grid (IMDG) presents a challenge to workflow management systems. Large-scale energy infrastructure research needs multi-variant planning and tools to allocate and dispatch distributed computing resources that pool together to let applications share data, taking into account the subject domain specificity, resource characteristics, and quotas for resource use. To that end, we propose an approach to implement HPC-based resilience analysis using our Orlando Tools (OT) framework. To dynamically scale computing resources, we provide their integration with the relevant software, identifying key application parameters that can have a significant impact on the amount of data processed and the amount of resources required. We automate the startup of the IMDG cluster to execute workflows. To demonstrate the advantage of our solution, we apply it to evaluate the resilience of the existing energy infrastructure model. Compared to similar approaches, our solution allows us to investigate large infrastructures by modeling multiple simultaneous failures of different types of elements down to the number of network elements. In terms of task and resource utilization efficiency, we achieve almost linear speedup as the number of nodes of each resource increases. [ABSTRACT FROM AUTHOR]

This article presents a package for analyzing the energy system vulnerability developed with new technology for continuous integration, delivery, and deployment of applied software. It implements a framework that allows combining and optimally using various methods for modelling energy systems and provides the comprehensive assessment of their vulnerability with regard to various uncertainties. The essential principles to identify and rank critical elements of an energy system are considered in the article. The investigations made with the package shown that the principles seem to be logical for the subsequent construction of the invariant set of measures for improving the energy system resilience.