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Preventive/corrective security constrained optimal power flow using a multiobjective genetic algorithm

S. Galvani, V. Talavat, S. Rezaeian-Marjani

Electric Power Components and Systems Vol. 46, nº. 13, pp. 1462 - 1477

Resumen:

Intensive researches have been investigated the security aspect of optimal power flow (OPF) problem. The primary objective of them is to ensure the reliable and continuous supply to the consumers as economically as possible. Although all of these researches offer valuable solution to enhancing security constrained OPF (SCOPF), they avoided a discussion on the operation cost of power system versus the security constrains. The understanding of relationship between the operation cost and system security leads to make best decisions on tradeoffs between operating cost and system reliability. The main objective of this article is to solve preventive/corrective SCOPF (P/C-SCOPF) problem considering the expected power not served (EPNS) index as security constraint. Also, the relationship between operation cost and EPNS index is discussed in detail. In this article, well-known nondominated sorting genetic algorithm (NSGA) is used to determine the precise relationship between operating cost and the security constraint (EPNS), for the first time. This would result in illuminating information that can be utilized to make a reasonable decision on the best tradeoffs between operating cost and reliability of system. The proposed approach has been evaluated on IEEE 14 and 57 bus test systems and results have been discussed.


Palabras Clave: security constrained optimal power flow; reliability; expected power not served; operating cost; nondominated sorting genetic algorithm


Índice de impacto JCR y cuartil WoS: 0,888 - Q4 (2018); 1,700 - Q3 (2023)

Referencia DOI: DOI icon https://doi.org/10.1080/15325008.2018.1489432

Publicado en papel: Agosto 2018.

Publicado on-line: Diciembre 2018.



Cita:
S. Galvani, V. Talavat, S. Rezaeian-Marjani, Preventive/corrective security constrained optimal power flow using a multiobjective genetic algorithm. Electric Power Components and Systems. Vol. 46, nº. 13, pp. 1462 - 1477, Agosto 2018. [Online: Diciembre 2018]


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