High-performance computing methods in large-scale power system simulation

In the Renewables Directive of the European Union, in effect since 2009, the member states agreed that the share in renewable energy should be 20% of the total energy by 2020.The concomitantly growing number of renewable energy producers such as photovoltaic systems and wind power plants leads to a more decentralized power generation. This results in a more complex power grid management. To ensure a secure power grid operation even so, there is a transformation from conventional power grids to so-called smart grids where, for instance, not only status information of power producers but also of consumers (e.g. heat pumps and electrical vehicles) is included in the power grid management. The utilization of flexibility on generation and demand side and the use of energy storage systems for achieving a stable and economic power supply requires new solutions for the planning and operation of smart grids. Otherwise, manipulations of the systems in the public energy sector (i.e. power grid, ICT infrastructure, energy market, etc.) can lead to unexpected problems such as power failures. Computer simulations therefore can help to estimate the behavior of smart grids on any changes without the risk of negative consequences in case of immature solutions or incompatibilities. The main objective of this dissertation is the application and analysis of HPC and computer science methods for improving power system (co-)simulation software to allow simulating more detailed models in a, for the particular use case, appropriate time. Through more automation and control in smart grids, the higher demand on flexibility, and the need of stronger market integration of consumers, the power system models become more and more complex. This requires an ever greater performance of the utilized computer systems. The focus was on the improvement of different aspects of state-of-the-art and currently developed simulation solutions. The intention was not to develop new simulation concepts or applications that would make large-scale HPC on super-computers or large computer clusters necessary. The dissertation presents the integration of modern direct solvers for sparse linear systems in various power grid simulation back-ends and subsequent analyses with the aid of large-scale power grid models. Furthermore, a new method for an automatic coarse-grained parallelization of power grid system models at component level is shown. Besides such concrete applications of HPC methods on simulation environments, also a comparative analysis of various HPC approaches for performance improvement of Python based software with the aid of (just-in-time) compilers is presented, as Python - usually an interpreted programming language - becomes more popular in the area of power system related software. Moreover, the dissertation shows the integration of an HPC interconnect solution based on InfiniBand - an open standard - in a software framework for the coupling of different simulation environments to a co-simulation and for HiL setups. The support of a standardized data model for the processing of power system topologies by simulation environments, on which the aforementioned HPC methods were applied, is necessary. Therefore, the dissertation concerns the CIM as, i.a., standardized by IEC61970/61968, which can be used for the specification of data models representing power system topologies. At first, a holistic data model is introduced that was developed for co-simulations of the power grid with the associated communication network and the energy market by extending CIM. To achieve a sustainable development of CIM related software tools, an automated (de-)serializer generation from CIM specifications is presented. The deserialization from CIM is a step needed for the subsequently developed template-based translation from CIM to simulator-specific system models which is also covered in this dissertation. Many presented findings and approaches can be used for improving further software from the area of electrical engineering and beyond that. Moreover, all presented approaches were implemented in open-source software projects, accessible by the public.