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.

Currently, the development of approaches that enhance the resilience of integrated energy systems is a highly relevant research direction. Such approaches are based on the structural and parametric optimization of integrated energy systems. Typically, these approaches are closely tied to a specific spatio-temporal scope and a particular optimization method. The application of developed approaches at other scopes often leads to a significant increase in computation time and a possible reduction of solution accuracy. This problem is due to the complexity of energy system optimization models and the differences between them. To solve this problem, we have developed a methodology for selecting the most suitable methods for the design of system resilience at a given spatio-temporal scope. The proposed methodology is based on testing methods within a specialized testbed and a multi-criteria analysis of test results. The indicators for evaluating the methods include both summary metrics of resilience and efficiency parameters of computational resources. The benefits of the proposed methodology are illustrated for the resilient design with respect to national and local integrated energy systems. Several dozen methods from the well-known Parallel Global Multiobjective Optimizer library were efficiently tested in up to 10 hours. The analysis of the testing results was performed with different multi-criteria algorithms regarding the prioritization of the indicators.

The nature of multi-energy systems requires the integration of interdisciplinary methodologies and complementary tools in a coherent and systematic manner. Therefore, it is imperative to develop an efficient experimental environment to effectively implement the interaction between different tools for structural and parametric optimization (synthesis) of multi-energy systems and analysis of their operation. Due to the inherent complexity of the energy system optimization model, solving the synthesis problem often leads to significant challenges in terms of required computational resources and runtime. Known synthesis approaches do not fully address these challenges. In this context, we propose a subject-oriented environment for the multi-energy system synthesis 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. The execution of the specialized tools for modeling and optimization is done through web services. The pre-selection of these tools, depending on the level of model detail of the systems under study, and the data validation are performed on the specially developed testbeds. Such testbeds are constructed as system workflows. This is one of the distinguishing features of our approach. The application of the proposed environment is demonstrated through a case study on the synthesis of a microgrid (a special case of a multi-energy system). As a result of the study, an optimal microgrid configuration was proposed. It is based on the cogeneration of heat and electricity generation using natural gas.