Unique Symbolic Factorization for Fast Contingency Analysis Using Full Newton–Raphson Method

Hakim Bennani (), Ahmed Chebak and Abderrazak El Ouafi
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Hakim Bennani: Mathematic Computer and Engineering Department, University of Quebec at Rimouski, Rimouski, QC G5L 3A1, Canada
Ahmed Chebak: Green Teck Institute, Mohammed VI Polytechnic University, Benguerir 43150, Morocco
Abderrazak El Ouafi: Mathematic Computer and Engineering Department, University of Quebec at Rimouski, Rimouski, QC G5L 3A1, Canada

Energies, 2023, vol. 16, issue 11, 1-17

Abstract: Contingency analysis plays an important role in assessing the static security of a network. Its purpose is to check whether a system can operate safely when some elements are out of service. In a real-time application, the computational time required to perform the calculation is paramount for operators to take immediate actions to prevent cascading outages. Therefore, the numerical performance of the contingency analysis is the main focus of this current research. In power flow calculation, when solving the network equations with a sparse matrix solver, most of the time is spent factorizing the Jacobian matrix. In terms of computation time, the symbolic factorization is the costliest operation in the LU (Lower-upper) factorization process. This paper proposes a novel method to perform the calculation with only one symbolic factorization using a full Newton–Raphson-based generic formulation and modular approach (GFMA). The symbolic factorization retained can be used during the iterations of any power flow contingency scenario. A computer study demonstrates that reusing the same symbolic factorization greatly reduces computation time and improves numerical performance. Power system security assessment under N-1 and N-2 contingency conditions is performed for the IEEE standard 54-bus and 108-bus to evaluate the numerical performance of the proposed method. A comparison with the conventional power flow method shows that the time required for the analysis is shortened considerably, with a minimum gain of 228%. The comparative analysis demonstrates that the proposed solution has better numerical performance for large-scale networks.

Keywords: contingency analysis; symbolic factorization; generic formulation; KLU solver (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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