NextHyb2 – Next generation hybrid2 modeling for the analysis and optimization of integrated, intelligent energy systems

Future intelligent and integrated energy systems must have a high degree of flexibility and efficiency to ensure reliable and sustainable operation. The tools of modelling and simulation play an integral part in optimising the design and operation of energy communities. To do so, the combination of continuous and discrete event modelling is required. On the other hand, as the complexity of the integrated energy systems increases, high computational effort of these (traditional) modelling techniques emerges. To eliminate this problem, efforts on approximating physical models via data models have been recently popular thanks to the increasing number of data due to the sensors and structures of IoT networks. These approximated models or surrogates, which use the input-output data of the physical simulation to teach the behaviour of the model, are developed.

The combination of continuous, discrete event and machine learning modelling in analysis of future intelligent energy systems poses new challenges for tool developers and users alike. The arguments for the “why” in the field of intelligent, integrated energy systems have been clearly established; however, the “how” remains an open research question. The research community sees a need for research in two hybrid areas: Hybrid in the sense of coupling continuous-time and discrete-event and hybrid in the sense of coupling these two models with machine learning models. The exploratory project NextHyb2 addresses this research gap by exploring the concept of hybrid-hybrid system simulations for future intelligent and integrated energy systems. Methods, tools and systemic solutions will be developed and evaluated together with experts. Furthermore, the solution will be implemented and tested on the basis of a proof of concept. NextHyb2 will use co-simulation, a promising approach for the analysis of this combination.