Optimal power flow with security operation region

Abstract This paper presents an approach to include security constraints in the Optimal Power Flow (OPF) problem aiming at providing a power system operation in safe region. The proposed operating region is formed by elliptical wraps that enclose historical and safe system operative states, which include wind power generation and demand scenarios. The wraps are obtained through a proposed non-Linear Programming (NLP) problem that defines the ellipse’s shape by using nodal voltages at buses of interest. In addition, the proposed approach can determine the operational risk, that is, the risk of an operative state leaving the safety region, by applying a relaxation method. The proposed methodology is tested in the 39-bus New England system and 118-bus IEEE test system. The results show that the elliptical wraps are effective to enclose the secure operational states and maintain the system within a safe region.

[1]  J. Ramos,et al.  State-of-the-art, challenges, and future trends in security constrained optimal power flow , 2011 .

[2]  F. Wen,et al.  Risk-based security-constrained economic dispatch in power systems , 2013 .

[3]  R. Jabr,et al.  Statistical Representation of Distribution System Loads Using Gaussian Mixture Model , 2010 .

[4]  T. Zheng,et al.  Solving corrective risk-based security-constrained optimal power flow with Lagrangian relaxation and Benders decomposition , 2016 .

[5]  K. Shanti Swarup,et al.  Artificial neural network using pattern recognition for security assessment and analysis , 2008, Neurocomputing.

[6]  Farid Karbalaei,et al.  A new method for solving preventive security-constrained optimal power flow based on linear network compression , 2018 .

[7]  Yuri R. Rodrigues,et al.  Network partitioning in coherent areas of static voltage stability applied to security region enhancement , 2020 .

[8]  M. J. Stevens,et al.  The estimation of the parameters of the Weibull wind speed distribution for wind energy utilization purposes , 1979 .

[9]  João A. Passos Filho,et al.  Assessment of Load Modeling in Power System Security Analysis Based on Static Security Regions , 2013 .

[10]  Yixin Yu,et al.  Static Voltage Security Region-Based Coordinated Voltage Control in Smart Distribution Grids , 2018, IEEE Transactions on Smart Grid.

[11]  D. M. Vinod Kumar,et al.  Security-constrained optimal power flow with wind and thermal power generators using fuzzy adaptive artificial physics optimization algorithm , 2016, Neural Computing and Applications.

[12]  Naoto Yorino,et al.  Robust Power System Security Assessment Under Uncertainties Using Bi-Level Optimization , 2018, IEEE Transactions on Power Systems.

[13]  On finding the shortest distance of a point from a line: Which method do you prefer? , 2017 .

[14]  Maria Vrakopoulou,et al.  Risk-Based Optimal Power Flow with Probabilistic Guarantees , 2015 .

[15]  K. S. Swarup,et al.  Power system static security assessment using self-organizing neural network. , 2013 .

[16]  Robert Eriksson,et al.  Efficient Database Generation for Data-Driven Security Assessment of Power Systems , 2018, IEEE Transactions on Power Systems.

[17]  Meritxell Vinyals,et al.  Fully distributed security constrained optimal power flow with primary frequency control , 2019, International Journal of Electrical Power & Energy Systems.

[18]  Chen Zhang,et al.  A parallel method for solving the DC security constrained optimal power flow with demand uncertainties , 2018, International Journal of Electrical Power & Energy Systems.

[19]  Shrikant I Bangdiwala,et al.  Regression: simple linear , 2018, International journal of injury control and safety promotion.

[20]  Balho H. Kim,et al.  A method of inclusion of security constraints with distributed optimal power flow , 2001 .

[21]  E. Vaahedi,et al.  Dynamic security constrained optimal power flow/VAr planning , 2001 .