Open Grid Europe
Company: Open Grid Europe (OGE) operates Germany’s largest gas transmission pipeline system with a gas network spanning more than 12,000 kilometres.
Project: The aim of this project is to analyse time-evolving hierarchical networks, such as the German gas-network, in order to understand their inner structure. Based on this structural understanding, processes based on these networks will be modelled, simulated and compared to real world phenomena.
The GasLab aims to develop new methods for gas-grid planning and operation, combining the most modern mathematic algorithms and up-to-date information technology. The tools developed in the GasLab will assist gas dispatchers and planners in their work and put them in a position to make better decisions based on foresighted and comprehensive information. The GasLab brings together the main areas of expertise of the scientific partners; that is, modeling, simulation, and optimization to advance the state of the art in gas-grid management and facilitate innovations.
Open Grid Europe (OGE) operates Germany’s largest gas transmission pipeline system with a gas network spanning more than 12,000 kilometers. All over the country, more than 1,450 staff ensure safe, environmentally-friendly and customer-oriented gas transmission. OGE supports the European gas market and works together with the European distribution network operators to create the prerequisites for transnational gas transportation.
Real-world networks from various domains have been shown to be small-world (large local clustering coefficient and small diameter) and scale-free (node degrees follow a power law). Additionally, they are often showing a hierarchical organization, since they reflect the modularity of the underlying system. An important step in understanding these complex systems is to identify sub-networks and their hierarchical structure. Having this knowledge allows for example to derive strategies for optimal transportation through these types of networks. However, most existing methods are designed to find non-overlapping subnetwork and don’t allow nodes being shared by different modules. It is easy to see that this limitation needs to be overcome to analyze complex networks such as the German gas network. This is because a main purpose of the network is to distribute gas to actual regional sub-structures such as cities while many cities share large pipelines coming e.g. from storage systems. To make things even more complicated, most real-world networks such as the gas network change over time. A simple example is the down time of parts of the network for maintenance, e.g. shutting down a pipe connecting two sub-networks.
The aim of this project is to analyze time-evolving hierarchical networks, such as the German gas-network, in order to understand their inner structure. With this structural understanding, processes based on these networks will be modeled, simulated and compared to real world phenomena. An example for this kind of process is gas-flow within such a system, including its physical properties. Once structural understanding and process understanding is achieved, the ultimate goal will be to use this knowledge to understand the inner logic of such a complex system with respect to flow prediction between the sub-systems over time. A typical question would be: given the demand of a particular sub-system (e.g. a large consumer) over time – what will be the demand tomorrow? You will learn in this project that answering the question is fairly easy if particular smoothness conditions can be assumed (e.g. about the demand, as often done in modeling courses at university) but painfully fails using standard approaches if real world scenarios are targeted. Also, you will learn what can be done in these cases.
The prospective participant should:
have programming skills in some higher level programming language such as C/C++, Java, or Python
is familiar with algorithms in the area of network science,
be prepared to work with real-world datasets from industry partners (which involves cleaning and preprocessing to overcome inconsistencies and incompleteness).
Ideally he or she
has working knowledge in at least of the following areas: combinatorial optimization, linear and integer programming, machine learning
has experience in working in a Linux/Unix environment and
collaborative work on source code (e.g. working with revision control systems).