Hierarchical Multi-Area State Estimation via Sensitivity Function Exchanges

A new hierarchical multi-area power system state estimation method is proposed in this paper. Instead of exchanging boundary measurements or state estimates, the proposed technique is based on exchanging the sensitivity functions of local state estimators. The main benefit of the proposed scheme is the improved convergence speed, which also reduces the amount of information exchange required. Extensive numerical results involving IEEE standard systems and a utility scale system are presented.

[1]  Efstratios N. Pistikopoulos,et al.  Multi-Parametric Programming: Volume 1: Theory, Algorithms, and Applications , 2007 .

[2]  H. Poor,et al.  Fully Distributed State Estimation for Wide-Area Monitoring Systems , 2012, IEEE Transactions on Smart Grid.

[3]  Lieven Vandenberghe,et al.  Convex Optimization: Unconstrained minimization , 2004 .

[4]  Farrokh Albuyeh,et al.  Grid of the future , 2009, IEEE Power and Energy Magazine.

[5]  E. Pistikopoulos,et al.  Multi-parametric programming : theory, algorithms and applications , 2007 .

[6]  Frank L. Lewis,et al.  Distributed Consensus-Based Economic Dispatch With Transmission Losses , 2014, IEEE Transactions on Power Systems.

[7]  Ankush Sharma,et al.  A multi-agent-based power system hybrid dynamic state estimator , 2015, IEEE Intelligent Systems.

[8]  F. Zhao,et al.  A Marginal Equivalent Decomposition Method and Its Application to Multi-Area Optimal Power Flow Problems , 2014, IEEE Transactions on Power Systems.

[9]  S. Chakrabarti,et al.  Multi Area State Estimation using area slack bus angle adjustment with minimal data exchange , 2013, 2013 IEEE Power & Energy Society General Meeting.

[10]  George N. Korres,et al.  A distributed implementation of multi-area power system state estimation on a cluster of computers , 2013 .

[11]  Asuman E. Ozdaglar,et al.  A Distributed Newton Method for Network Utility Maximization—Part II: Convergence , 2010, IEEE Transactions on Automatic Control.

[12]  Antonio Gómez Expósito,et al.  A Multilevel State Estimation Paradigm for Smart Grids , 2011, Proceedings of the IEEE.

[13]  Tomaso Erseghe,et al.  Distributed Optimal Power Flow Using ADMM , 2014, IEEE Transactions on Power Systems.

[14]  A. Abur,et al.  Total transfer capability computation for multi-area power systems , 2006, IEEE Transactions on Power Systems.

[15]  G.T. Heydt,et al.  Diakoptic State Estimation Using Phasor Measurement Units , 2008, IEEE Transactions on Power Systems.

[16]  Georgios B. Giannakis,et al.  Distributed Robust Power System State Estimation , 2012, IEEE Transactions on Power Systems.

[17]  A. Abur,et al.  Multi area state estimation using synchronized phasor measurements , 2005, IEEE Transactions on Power Systems.

[18]  Thierry Van Cutsem,et al.  A taxonomy of multi-area state estimation methods , 2011 .

[19]  A. Sharma,et al.  An Iterative Multiarea State Estimation Approach Using Area Slack Bus Adjustment , 2016, IEEE Systems Journal.

[20]  M. Ribbens-Pavella,et al.  A Two-Level Static State Estimator for Electric Power Systems , 1981, IEEE Transactions on Power Apparatus and Systems.

[21]  R. Schaback Convergence analysis of the general Gauss-Newton algorithm , 1985 .

[22]  Yih-Fang Huang,et al.  Decentralized power system state estimation with reduced inter-area communication , 2015, 2015 IEEE International Conference on Digital Signal Processing (DSP).

[23]  Liam Murphy,et al.  Parallel and distributed state estimation , 1995 .

[24]  R. Baldick,et al.  State estimation distributed processing [for power systems] , 2000 .

[25]  I. O. Habiballah Modified two-level state estimation approach , 1996 .

[26]  A.J. Conejo,et al.  An Optimization Approach to Multiarea State Estimation , 2007, IEEE Transactions on Power Systems.

[27]  George N Korres,et al.  A Distributed Multiarea State Estimation , 2011, IEEE Transactions on Power Systems.