Factorisation Path Based Refactorisation for High-Performance LU Decomposition in Real-Time Power System Simulation
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TL;DR: In this article, a factorisation path algorithm for partial refactorization is proposed, which takes into account that only a subset of matrix entries change their values during the decomposition process.
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Abstract: The integration of renewable energy sources into modern power systems requires simulations with smaller step sizes, larger network models and the incorporation of complex nonlinear component models. These features make it more difficult to meet computation time requirements in real-time simulations and have motivated the development of high-performance LU decomposition methods. Since nonlinear component models cause numerical variations in the system matrix between simulation steps, this paper places a particular focus on the recomputation of LU decomposition, i.e., on the refactorisation step. The main contribution is the adoption of a factorisation path algorithm for partial refactorisation, which takes into account that only a subset of matrix entries change their values. The approach is integrated into the modern LU decomposition method NICSLU and benchmarked against the methods SuperLU and KLU. A performance analysis was carried out considering benchmark as well as real power systems. The results show the significant speedup of refactorisation computation times in use cases involving system matrices of different sizes, a variety of sparsity patterns and different ratios of numerically varying matrix entries. Consequently, the presented high-performance LU decomposition method can assist in meeting computation time requirements in real-time simulations of modern power systems.
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Citations
GPU-resident sparse direct linear solvers for alternating current optimal power flow analysis
Kasia Świrydowicz,Nicholson Koukpaizan,Tobias Ribizel,Fritz Göbel,Shrirang Abhyankar,Hartwig Anzt,Slaven Peleš +6 more
TL;DR: GPU-resident sparse direct linear solvers accelerate ACOPF analysis by leveraging hardware accelerators and treating the problem as sparse.
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Towards Efficient Alternating Current Optimal Power Flow Analysis on Graphical Processing Units
Kasia Świrydowicz,Nicholson Koukpaizan,Shrirang Abhyankar,Slaven Peleš +3 more
- 11 Jun 2023
TL;DR: Sparse ACOPF analysis on GPU accelerates large-scale power flow studies. Speed-up compared to CPU using state-of-the-art solver.
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Towards Efficient Alternating Current Optimal Power Flow Analysis on Graphical Processing Units
16 Feb 2023
TL;DR: In this article , a solution of sparse alternating current optimal power flow (ACOPF) analysis on graphical processing unit (GPU) is presented, in particular, the performance bottlenecks and efforts to accelerate the linear solver.
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