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

[2]  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).

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

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

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

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

[7]  Peter Deuflhard,et al.  Numerische Mathematik. I , 2002 .

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

[9]  Lang Tong,et al.  Coordinated multi-area economic dispatch via multi-parametric programming , 2015, 2015 IEEE Power & Energy Society General Meeting.

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

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

[12]  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.

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

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

[15]  Ross Baldick,et al.  State Estimation Distributed Processing , 2000 .

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

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

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

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

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

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

[22]  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.

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

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

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

[26]  M. Ribbens-Pavella,et al.  Critical Survey of Hierarchical Methods for State Estimation of Electric Power Systems , 1983, IEEE Transactions on Power Apparatus and Systems.