A Numerical Study of the Fixed-Threshold Criterion for Expressing Transient Stability Constraints in Optimal Power Flow

2015 ◽  
Vol 781 ◽  
pp. 379-383
Author(s):  
Warut Suampun

A numerical study of the widely used fixed-threshold criterion for expressing transient stability constraints in optimal power flow (TSCOPF) is conducted. Based on a stability-region framework, a more accurate expression of transient stability constraint in TSCOPF is presented. A method for computing system exact threshold values is proposed and employed for the study of threshold values under different conditions. It is shown via numerical results on the WSCC9 and IEEE145 systems that the exact threshold value for each system and contingency is in fact not a constant, and can vary greatly depending on several factors such as types of contingency, loading conditions, and network topology.

2017 ◽  
Vol 11 (12) ◽  
pp. 3177-3185 ◽  
Author(s):  
Shrirang Abhyankar ◽  
Guangchao Geng ◽  
Mihai Anitescu ◽  
Xiaoyu Wang ◽  
Venkata Dinavahi

Author(s):  
Sourav Paul ◽  
Provas Kumar Roy

Optimal power flow with transient stability constraints (TSCOPF) becomes an effective tool of many problems in power systems since it simultaneously considers economy and dynamic stability of power system. TSC-OPF is a non-linear optimization problem which is not easy to deal directly because of its huge dimension. This paper presents a novel and efficient optimisation approach named the teaching learning based optimisation (TLBO) for solving the TSCOPF problem. The quality and usefulness of the proposed algorithm is demonstrated through its application to four standard test systems namely, IEEE 30-bus system, IEEE 118-bus system, WSCC 3-generator 9-bus system and New England 10-generator 39-bus system. To demonstrate the applicability and validity of the proposed method, the results obtained from the proposed algorithm are compared with those obtained from other algorithms available in the literature. The experimental results show that the proposed TLBO approach is comparatively capable of obtaining higher quality solution and faster computational time.


Author(s):  
A.L. Bettiol ◽  
D. Ruiz-Vega ◽  
D. Ernst ◽  
L. Wehenkel ◽  
M. Pavella

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