A New Julia-Based Parallel Time-Domain Simulation Algorithm for Analysis of Power System Dynamics
Michael Kyesswa, Philipp Schmurr, Hüseyin Kemal Çakmak, Uwe Kühnapfel, Veit Hagenmeyer
Abstract
Michael Kyesswa, Philipp Schmurr, Hüseyin Kemal Çakmak, Uwe Kühnapfel, Veit Hagenmeyer
Abstract
The present paper describes a new parallel time-domain simulation algorithm using a high performance computing environment - Julia - for the analysis of power system dynamics in large networks. The parallel algorithm adapts a parallel-in-space decomposition scheme to a previously sequential algorithm in order to develop a new parallelizable numerical solution of the power system equations. The parallel-in-space decomposition is based on the block bordered diagonal form, which reformulates the network admittance matrix into sub-blocks that can be solved in parallel. For the optimal spatial decomposition of the network, a new extended graph partitioning strategy is developed for load balancing and minimizing the communication between subnetworks. The new parallel simulation algorithm is tested using standard test networks of varying complexity. The simulation results are compared to those obtained from a sequential implementation in order to validate the solution accuracy and to determine the performance improvement in terms of computational speedup. Test simulations are conducted using the ForHLR II supercomputing cluster and show a huge potential in computational speedup with increasing network complexity.
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The present paper describes a new parallel time-domain simulation algorithm using a high performance computing environment - Julia - for the analysis of power system dynamics in large networks. The parallel algorithm adapts a parallel-in-space decomposition scheme to a previously sequential algorithm in order to develop a new parallelizable numerical solution of the power system equations. The parallel-in-space decomposition is based on the block bordered diagonal form, which reformulates the network admittance matrix into sub-blocks that can be solved in parallel. For the optimal spatial decomposition of the network, a new extended graph partitioning strategy is developed for load balancing and minimizing the communication between subnetworks. The new parallel simulation algorithm is tested using standard test networks of varying complexity. The simulation results are compared to those obtained from a sequential implementation in order to validate the solution accuracy and to determine the performance improvement in terms of computational speedup. Test simulations are conducted using the ForHLR II supercomputing cluster and show a huge potential in computational speedup with increasing network complexity.
Key concepts: Speedup, Computer science, Domain decomposition methods, Parallel algorithm, Parallelizable manifold, Algorithm, Parallel computing, Supercomputer