Effective solving complex mathematical modeling problems is based on the use of high-performance computing. Clouds, grids, and public access supercomputer centers are commonly used platforms. Their integration into a unified environment provides possibilities for carrying out mass large-scale scientific experiments and efficient scalable resource allocation at different stages of the application design and execution. However, end-users have to carefully select optimization criteria such as completion time, deadlines, reliability, cost, etc. It is a complicated problem due to integrated resources differ significantly in their computing capabilities, hardware and software platforms, system architectures, user interfaces, etc. The paper presents new features of the Orlando Tools framework for the development of distributed applied software packages (scalable scientific applications) that mitigates various types of uncertainties arising from the job distribution in the integrated computing environment. It provides continuous integration, delivery, and deployment of applied and system software to significantly mitigate the negative impact of uncertainty on problem-solving time, computation reliability, and resource efficiency. An experimental analysis of the sustainable design and development of the real energy sector clearly demonstrates the advantages of the tools.

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.

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.

A new approach to creating a domain-specific heterogeneous distributed computing environment is considered. It is used to support decision-making on urgent problems of increasing the survivability of energy systems. The approach is based on the use of high-performance computing, multiagent scheduling of computations and resource assignment, tools for processing semistructured information, and visualization of subject data using electronic maps. Decision-making alternatives are evaluated using combinatorial modeling and multicriteria optimization. Orlando Tools is used as the base for the environment’s integrated software. It implements flexible modular construction of scalable scientific applications (distributed application packages). The advantages of using the environment are demonstrated by the example of solving practical problems.