Resiliency Analysis of Hybrid Energy Systems within Interconnected Infrastructures

There are world tendencies to implement interconnected infrastructures of energy-water-waste-transportation-food-health-social systems to enhance the overall performance in normal and emergency situations where there are multiple interactions among them with possible conversions and improved efficiencies. Hybrid energy systems are core elements within interconnected infrastructures with possible conversions among electricity, thermal, gas, hydrogen, waste, and transportation networks. This could be improved with storage systems and intelligent control systems. It is important to study resiliency of hybrid energy systems within interconnected infrastructures to ensure reduced risks and improved performance. This paper presents framework for the analysis of resiliency layers as related to protection layers. Case study of hybrid energy system as integrated with water, waste, and transportation infrastructures is presented where different resiliency and protection layers are assessed. Performance measures are modeled and evaluated for possible interconnection scenarios with internal and external factors that led to resiliency demands. Resiliency layers could trigger protection layers under certain conditions, which are evaluated to achieve high performance hybrid energy systems within interconnected infrastructures. The proposed approach will support urban, small, and remote communities to achieve high performance interconnected infrastructures for normal and emergency situations.

Analysis and Development Potential of Predictive Models for Energy Flows of Autonomous Hybrid Energy Systems

The article is devoted to the analysis and development potential for predictive models of energy flows of autonomous hybrid energy systems. The article considers the results of the analysis in the form of a morphological table and TRIZ–evolutionary map. The research defined the most promising in predicting energy flows are hybrid models that include more than one architecture. For example, DCNN + LSTM or MLP + LS–SVR. The authors intend to continue research in the direction of creating predictive models of wind energy flows for autonomous hybrid energy systems.