## 2 State-of-the-Art
### Energy Flexibility for Industrial Companies  
The identification of flexibility measures, the technical and economic assessment, as well as the subsequent marketing on the energy market present a complex challenge for companies [31]. On the one hand, the different use cases for flexibility are characterized by an individual product design and compensation methods. On the other hand, on the manufacturing level, different machines or manufacturing processes inherit different forms of flexibility measures and therefore a divergent level of flexibility potential. The characteristics are most commonly distinguished by their controllability. Hence, the potential measures are categorized into uncontrollable (no flexibility), curtailable, shiftable, buffered, and freely controllable loads (full flexibility) [32]. Curtailable loads, for example, are those that do not need to recover the curtailed energy once they are reconnected. In contrast to that, shiftable loads are those that can be moved in time. However, the amount of consumed energy does not change; it only gets shifted in the time domain. In addition, the identification and implementation of measures for DR are quite complex. The technical processes define many different boundary conditions and dependencies, such as a minimum or maximum period of interruption due to quality issues, which have to be considered [33]. In addition, DR can also counteract the main objectives of manufacturing, such as throughput or delivery reliability [34]. The high number of dependencies between individual flexibility measures and a large number of manufacturing parameters represents a major challenge in the manufacturing industry. This includes planning horizons, temporal resolutions, and maximum permissible runtimes [35]. At the same time, the complexity of the boundary conditions and the abstraction level increase with higher automation levels [36]. Moreover, data from a wide range of heterogeneous sources on the shop floor (e.g., machine control, manufacturing planning, etc.) is required for industrial DR. As manufacturing  
\begin{table}
\begin{tabular}{c c c c} \hline \hline
**Requirement Profile** & **Short Description** & **Request Duration** & **Remuneration** \\ \hline \multirow{2}{*}{\(1\). Short-term load adjustment} & Flexibility can compensate short-term fluctuations in generation or demand. & \multirow{2}{*}{\(5\) min–\(1\) h} & Balancing power markets, interruptible load market, intraday-market \\ \hline \multirow{2}{*}{\(2\). Load adjustment over several hours} & Mismatch of renewable generation and demand lead to significant fluctuations in electricity prices. & \multirow{2}{*}{\(3\) to \(12\) h} & Day-Ahead-Market, derivatives \\  & & & derivatives \\ \hline \multirow{2}{*}{\(3\). Reduction/increase of load over several days} & Relevance for flexibility over longer periods increases with regard to the security of supply. & \multirow{2}{*}{\(1\)–\(5\) days} & Day-ahead-market, derivatives \\ \hline \multirow{2}{*}{\(4\). Atypical grid usage} & By avoiding grid usage at congested times, the grid and procurement costs can be reduced. & Several hours per day & Reduction of network charges \\ \hline \hline \end{tabular}
\end{table}
Table 1: Requirement profiles and potential remuneration for DR.  
IT platforms have been proven to provide such features, the existing ecosystems in this field are examined below.