JuliaCon 2023

Computational simulations are important in the design, operation and analysis of power systems in order to ensure a secure and stable operation of power grids. The current power system operating environment, however, shows several transformations in the grid structure as a result of increasing operation of large interconnected networks, an increase in electricity demand from e.g. electric vehicles and heat pumps, and the increasing integration of renewable energy sources in the energy transition context. These changes directly impose additional requirements to the stability analysis process, whereby the time-domain simulations widely used for dynamic stability studies are faced with an increase in computational burden due to the increasing complexity of the system under analysis. In order to address the complexity in the analysis of large networks, parallel and distributed computing techniques are frequently applied to improve the computational speed by taking advantage of multi-core processors and cluster computing.

This talk presents an extension in a Julia-based parallel simulation algorithm to address the need for improved computation methods in power system stability analysis. The algorithm achieves an improvement in computational speedup by reformulating an inherently sequential numerical solution to a parallel approach using a parallel-in-space decomposition scheme and the Julia computing environment. The talk will focus on the parallelization approach applied to restructure the numerical formulation in order to solve the resulting power system differential and algebraic equations in parallel. The basis of the parallelization is a parallel-in-space decomposition to partition the network into independent subnetworks and the equations of the subnetworks are assigned to different processors. The in-space decomposition uses the branch splitting Multi-Area Thevenin Equivalent (MATE) algorithm to divide the network coefficient matrix into submatrices that can be solved in parallel.

The talk will describe the multi-level graph partitioning technique which is used to achieve optimal balancing of tasks in the parallel solution process. The partitions are extended to the dynamic simulation problem to obtain balanced subnetworks that are solved in parallel and only linked via a link subsystem. Furthermore, simulation results will be presented to highlight the difference between the original parallel approach based on the node-splitting Block Bordered Diagonal Formulation (BBDF) and the improved extension of the algorithm based on the branch-splitting MATE algorithm.