The German high-pressure gas pipeline network has an overall length of approximately 50,000 km. Each month, 120 billion kW/h of energy are transport via this network. This roughly corresponds to 10 times the electricity generation of all German nuclear power plants. Natural gas contributes more than 20% to the entire energy supply in Germany and is therefore, besides fuels, the most important source of energy. Thus, the gas transport system is of high social and political relevance. Expansion measures have a long time horizon and are as cost-intensive as maintenance and operation. The optimal use of existing capacities therefore offers high savings potentials. Suppliers and consumers involved currently have to inform the transport network operator about one day in advance about the gas purchase quantities expected. This process is called nomination. The network operators then control their network correspondingly, with security of supply being the factor with highest priority. At the same time, part of the gas in the network is used to supply the energy for transport. It is the network operators’ task to process a maximum number of orders with a minimum of internal energy consumption. Control of the network is highly non-linearly complex and, due to the storage behavior of the network, very difficult to keep track of in terms of time. There is always the risk that a decision that has already been made will lead to a bottleneck at some point in the future. Thus, the most important aspect of the project is to identify potentially critical situations early and to initiate counter measures. MODAL aims at exploring the fundamentals for the development of a dynamic nomination validation. This would then calculate, on the basis of the current network condition and the existing nominations, an optimized control schedule for the network for the next 1 to 3 days. This would provide the foundation for an automated process for the control of gas networks. Such a process would entail various benefits: The security of supply would be increased considerably, since an early warning could be issued whenever a situation that cannot be operated threatens to arise in the network. Algorithms would be able to find solutions for control problems that are difficult or impossible to identify for human dispatchers. For the same reason, the capacity in the gas network would also be increased, so that it will be possible to process transport orders that at this point in time are out of reach. This would, in particular, be very welcome by the Federal Network Agency, since it would be possible to offer and use free capacities at very short notice. By calculating an optimized control schedule it will be possible to save costs of operation: Even a reduction of only 10% would correspond to the output of a nuclear power plant. Ultimately, such methods would also enable far better possibilities to react on geopolitical events such as reduced gas supplies from Russia. The online calculation of an optimized operation for a large gas transport network with a time horizon of 1 – 3 days is a task that can presently not be solved mathematically. The aim of GasLab is to develop a prototypical method that enables the automatic calculation of the control schedule with sufficient accuracy on the basis of real-world data. The challenge of this project lies equally in the design of suitable models, in the development of new optimization algorithms and in finding a way to make available the required but extremely voluminous data in a suitable manner.