Topological characterization and modeling of dynamic evolving power distribution networks

Abstract Existing complex network models are either with unvaried network size or based on simple growth mechanisms which cannot accurately describe the operational dynamics and characteristics of realistic networks. In this paper, we exploit the dynamic evolving phenomenon of power distribution networks covering its growth, reconnection and shrinking characteristics from the network topology perspective, and attempt to produce a novel dynamic evolving model through introducing the locating probability and shrinking mechanism. The proposed modeling approach is assessed and validated through extensive numerical simulation experiments for a range of standard IEEE power test systems. The statistical results reveal that the node degree distribution of the power network follows the power-law distribution and the node removal probability of the network dynamic has a significant impact on the network evolvement and robustness. Such macroscopic topological findings can greatly benefit the power distribution network operators (DNOs) from many aspects, including network planning, vulnerability analysis, fault prediction and cost-effective reinforcement.

[1]  Y.J. Cao,et al.  Stability of the spreading in small-world network with predictive controller , 2010, Physics Letters A.

[2]  Julian Lee,et al.  Disease spreading on fitness-rewired complex networks , 2011 .

[3]  Zhejing Bao,et al.  Comparison of cascading failures in small-world and scale-free networks subject to vertex and edge attacks , 2009 .

[4]  Alessandro Vespignani Modelling dynamical processes in complex socio-technical systems , 2011, Nature Physics.

[5]  Kousuke Yakubo,et al.  Scale-free property of local-world networks and their community structures , 2010 .

[6]  Hemanshu Roy Pota,et al.  Transient stability assessment of smart power system using complex networks framework , 2011, 2011 IEEE Power and Energy Society General Meeting.

[7]  Robert Lasseter,et al.  Smart Distribution: Coupled Microgrids , 2011, Proceedings of the IEEE.

[8]  Luo Yi Complex Network Characteristic Analysis and Model Improving of the Power System , 2008 .

[9]  Zhe Zhang,et al.  An electrical betweenness approach for vulnerability assessment of power grids considering the capacity of generators and load , 2011 .

[10]  D S Callaway,et al.  Network robustness and fragility: percolation on random graphs. , 2000, Physical review letters.

[11]  Ben Wang,et al.  Using modified Barabási and Albert model to study the complex logistic network in eco-industrial systems , 2012 .

[12]  Shi Ding-hua,et al.  Markov chain-based analysis of the degree distribution for a growing network , 2011 .

[13]  Hui Zhang,et al.  Modeling the effects of social impact on epidemic spreading in complex networks , 2011 .

[14]  Randy L. Ekl,et al.  Security Technology for Smart Grid Networks , 2010, IEEE Transactions on Smart Grid.

[15]  Cao Yijia,et al.  A novel local-world evolving network model for power grid , 2009 .

[16]  BERNARD M. WAXMAN,et al.  Routing of multipoint connections , 1988, IEEE J. Sel. Areas Commun..

[17]  Jian-Wei Wang,et al.  Robustness of the western United States power grid under edge attack strategies due to cascading failures , 2011 .

[18]  Optimizing Method for Synchronization of Small-World Networks , 2012, 2012 International Conference on Industrial Control and Electronics Engineering.

[19]  Hong-Jun Quan,et al.  Behaviors of imitated agents in an evolutionary minority game on NW small world networks , 2010 .

[20]  Athula D. Rajapakse,et al.  Microgrids research: A review of experimental microgrids and test systems , 2011 .

[21]  Anna Scaglione,et al.  Generating Statistically Correct Random Topologies for Testing Smart Grid Communication and Control Networks , 2010, IEEE Transactions on Smart Grid.

[22]  R H Lasseter,et al.  CERTS Microgrid Laboratory Test Bed , 2011, IEEE Transactions on Power Delivery.