Confidence interval estimates for loading margin sensitivity for voltage stability monitoring in the presence of renewable energy

In modern power systems, the integration of variable and random renewable energy brings more challenges to real-time voltage stability monitoring and control. Loading margin sensitivity (LMS) is an index that is widely-used to evaluate voltage stability. The accuracy of LMS relies on the credibility of the data collected by local measurement units, which are used to estimate the system model parameters. Considering the noise signals and the variability of renewable generation, this paper proposes a probabilistic LMS calculation method based on bootstrap technique. Through the bootstrap, the proposed method provides a confidence interval estimate for LMS, which is effective to implement in the presence of renewable generation. Finally, the effectiveness of the proposed method is demonstrated by a TSAT simulation on the 48-machine 140-bus NPCC system.

[1]  J.W. Pierre,et al.  Bootstrap-based confidence interval estimates for electromechanical modes from multiple output analysis of measured ambient data , 2005, IEEE Transactions on Power Systems.

[2]  Tao Jiang,et al.  Real-time wide-area loading margin sensitivity (WALMS) in power systems , 2015, 2015 IEEE Power & Energy Society General Meeting.

[3]  Weixuan Lin,et al.  The impact of trading wind power in both energy and regulation reserve market on system operation , 2012, 2012 North American Power Symposium (NAPS).

[4]  B. Efron.  Bootstrap Methods: Another Look at the Jackknife , 1979 .

[5]  A. Phadke,et al.  Control of voltage stability using sensitivity analysis , 1992 .

[6]  T. Van Cutsem,et al.  Unified sensitivity analysis of unstable or low voltages caused by load increases or contingencies , 2005, IEEE Transactions on Power Systems.

[7]  Fernando L. Alvarado,et al.  Sensitivity of the loading margin to voltage collapse with respect to arbitrary parameters , 1997 .

[8]  Tao Jiang,et al.  Identification of voltage stability critical injection region in bulk power systems based on the relative gain of voltage coupling , 2016 .

[9]  Wilsun Xu,et al.  Voltage stability monitoring based on the concept of coupled single-port circuit , 2012, 2012 IEEE Power and Energy Society General Meeting.

[10]  N. Amjady,et al.  Application of a new sensitivity analysis framework for voltage contingency ranking , 2005, IEEE Transactions on Power Systems.

[11]  Fangxing Li,et al.  Probabilistic Model of Payment Cost Minimization Considering Wind Power and Its Uncertainty , 2013, IEEE Transactions on Sustainable Energy.

[12]  Ye Zhang,et al.  Design and Implementation of a Real-Time Off-Grid Operation Detection Tool from a Wide-Area Measurements Perspective , 2015, IEEE Transactions on Smart Grid.

[13]  Fangxing Li,et al.  Coupon-Based Demand Response Considering Wind Power Uncertainty: A Strategic Bidding Model for Load Serving Entities , 2016, IEEE Transactions on Power Systems.

[14]  Walmir Freitas,et al.  A Monte Carlo simulation platform for studying low voltage residential networks , 2015, 2015 IEEE Power & Energy Society General Meeting.

[15]  Kai Sun,et al.  Measurement-based real-time voltage stability monitoring for load areas , 2016, 2016 IEEE Power and Energy Society General Meeting (PESGM).

[16]  Chia-Chi Chu,et al.  Wide-Area Measurement-Based Voltage Stability Indicators by Modified Coupled Single-Port Models , 2014, IEEE Transactions on Power Systems.