Welcome to the I³ Lab CyEntEE: Cyber Physical Energy Systems - Sustainability, Resilience and Economics
It is common goal to avoid greenhouse gas (GHG) emissions as soon as possible to overcome issues regarding climate change. In Germany, most reduction can be observed in the electricity sector due to intensive expansion of renewables like wind and photovoltaic energy. Renewable energy mostly arises in form of electric energy, therefore the ongoing GHG reduction is mainly located in the electricity sector. To decarbonize the whole energy system, electric energy should be used in other sectors too which leads to the concept of sector coupling. During energy transition, single big producer will be exchanged by geographically dispersed supply-dependent decentralized energy resources (DER) with different energy quality. Because energy conversion, for example from electricity via Power-to-Gas to hydrogen or further to synthetic methane, is always entailed by efficiency loss, direct electrification of these sectors is a part of this transition. However, this conversion path provides long-term energy storage options with the ability to compensate for seasonal intermittency in energy production.
To maintain today’s high standard of supply availability, new concepts for flexibility provision have to be developed in this context. The creation of new flexibility options, which can be provided by generator side as well as demand side, requires sophisticated information and communication technologies (ICT) to achieve the high information density needed. Furthermore, sector coupling happens not only on the primary physical part, but also on the secondary digital part. Fig. 1 below depicts these two coupling paths.
Energy transition and sector coupling requires not only changes on the physical and digital side, but also on the market side. Detailed availability of data, e.g. due to the ongoing roll out of smart meters, and the lack of tradability with small amounts of energy produced by DER will result in possible new market concepts. One example for this are virtual power plants, which make use of geographically distributed producer and pooling effects in form of combination of different technologies and more precise forecast possibilities due to stochastic effects.
Despite a large number of existing energy models, these mostly consider only one sector. As soon as more sectors participate in the modelled energy provision, modeling depth decreases due to complexity and computational cost. Although simplifications led to the possibility of simulating the overall system, this approach diminishes the result accuracy. Market-related considerations are also often considered specifically for individual sectors. The goal of this project is to develop a co-simulation platform that takes dynamic effects on the physical side into account and is capable of simulating event based ICT models and protocols. Furthermore, optimization algorithms are developed and investigated which allow simulation of coupled effects for this holistic approach and derive optimal operating conditions on the background of the German energy policy goal triangle.
Fig. 1: Modeling approach for cyber physical energy systems.