# GPU-Accelerated Batch-ACPF Solution for N-1 Static Security Analysis

Title | GPU-Accelerated Batch-ACPF Solution for N-1 Static Security Analysis |

Publication Type | Journal Article |

Year of Publication | 2017 |

Authors | Zhou, G., Feng, Y., Bo, R., Chien, L., Zhang, X., Lang, Y., Jia, Y., Chen, Z. |

Journal | IEEE Transactions on Smart Grid |

Volume | 8 |

Pagination | 1406–1416 |

ISSN | 1949-3053 |

Keywords | Acceleration, ACPF problem, Algorithm design and analysis, alternating current power flow, batch-solving method, contingency analysis, contingency screening, float-pointing calculation, GPU-accelerated, GPU-accelerated batch-ACPF solution, GPU-accelerated batch-Jacobian-matrix, GPU-accelerated batch-QR solver, graphics processing unit, graphics processing units, High performance computing, high performance computing (HPC), Intel Xeon E5-2620, Jacobian matrices, KLU library, memory bandwidth, Metrics, Multicore Computing, multicore computing security, multicore CPU parallel computing solution, Multicore processing, N-1 static security analysis, parallel processing, parallelism, Power systems, program diagnostics, pubcrawl, QR factorization, resilience, Resiliency, Scalability, security, security of data, SSA, Static security analysis (SSA), UMFPACK-library-based single-CPU counterpart |

Abstract | Graphics processing unit (GPU) has been applied successfully in many scientific computing realms due to its superior performances on float-pointing calculation and memory bandwidth, and has great potential in power system applications. The N-1 static security analysis (SSA) appears to be a candidate application in which massive alternating current power flow (ACPF) problems need to be solved. However, when applying existing GPU-accelerated algorithms to solve N-1 SSA problem, the degree of parallelism is limited because existing researches have been devoted to accelerating the solution of a single ACPF. This paper therefore proposes a GPU-accelerated solution that creates an additional layer of parallelism among batch ACPFs and consequently achieves a much higher level of overall parallelism. First, this paper establishes two basic principles for determining well-designed GPU algorithms, through which the limitation of GPU-accelerated sequential-ACPF solution is demonstrated. Next, being the first of its kind, this paper proposes a novel GPU-accelerated batch-QR solver, which packages massive number of QR tasks to formulate a new larger-scale problem and then achieves higher level of parallelism and better coalesced memory accesses. To further improve the efficiency of solving SSA, a GPU-accelerated batch-Jacobian-Matrix generating and contingency screening is developed and carefully optimized. Lastly, the complete process of the proposed GPU-accelerated batch-ACPF solution for SSA is presented. Case studies on an 8503-bus system show dramatic computation time reduction is achieved compared with all reported existing GPU-accelerated methods. In comparison to UMFPACK-library-based single-CPU counterpart using Intel Xeon E5-2620, the proposed GPU-accelerated SSA framework using NVIDIA K20C achieves up to 57.6 times speedup. It can even achieve four times speedup when compared to one of the fastest multi-core CPU parallel computing solution using KLU library. The prop- sed batch-solving method is practically very promising and lays a critical foundation for many other power system applications that need to deal with massive subtasks, such as Monte-Carlo simulation and probabilistic power flow. |

URL | http://ieeexplore.ieee.org/document/7544647/ |

DOI | 10.1109/TSG.2016.2600587 |

Citation Key | zhou_gpu-accelerated_2017 |

- pubcrawl
- Multicore Computing
- multicore computing security
- multicore CPU parallel computing solution
- Multicore processing
- N-1 static security analysis
- parallel processing
- parallelism
- power systems
- program diagnostics
- Metrics
- QR factorization
- resilience
- Resiliency
- Scalability
- security
- security of data
- SSA
- Static security analysis (SSA)
- UMFPACK-library-based single-CPU counterpart
- GPU-accelerated batch-Jacobian-matrix
- ACPF problem
- Algorithm design and analysis
- alternating current power flow
- batch-solving method
- contingency analysis
- contingency screening
- float-pointing calculation
- GPU-accelerated
- GPU-accelerated batch-ACPF solution
- Acceleration
- GPU-accelerated batch-QR solver
- graphics processing unit
- graphics processing units
- High performance computing
- high performance computing (HPC)
- Intel Xeon E5-2620
- Jacobian matrices
- KLU library
- memory bandwidth