Rank Similarity of Critical Nodes

From the results of Sect. 18.4 it is clear that, the nodes found from bidirectional flow model has much more impact than nominal and backward unidirectional models. In order to analyze the effect of system change on ranks of critical nodes a rank similarity analysis is performed. A structural change like change in the direction of power flow is incorporated in the model and critical nodes are found out for the modified system. This change in network corresponds to a situation when there is a pushback of power from low voltage network via transmission system to meet energy needs in other area.

Table 18.4 compares the changes in top ten critical nodes in IEEE 30 bus test system. This analysis is carried out for bidirectional power flow model. Top row of Table 18.4 corresponds to the topological state of the system. The first column gives the top ten critical nodes from the bidirectional model. The rest of the columns list change in critical nodes for changed topology. As for example, the third column represents the top ten critical nodes when the nominal direction of flow is changed through line 29-27. It is clear that; changed topology does not affect much the node criticality.

On the other hand, slightly more change is observed in criticality for the uni­directional model as shown in Fig. 18.8. When power flow pattern through the grid is unidirectional, nominal unidirectional method is effective. But, in order to model

Rank Similarity of Critical Nodes

the situation in the future smart grid, bidirectional model gives better result in terms of rank similarity as given in Fig. 18.9.

This introduces a new concept of power flowing from customer end towards the grid. The bidirectional power flow changes the whole power flow pattern of the existing grid [18]. Analytical methods, technical strategies, control system and protecting devices need to be changed along with, to mention a few. Metering and protecting equipments will experience flows coming from the reverse side. Proper operation of the equipments used earlier can be ensured either by changing the instruments themselves or by incorporating new measurement techniques [27].

18.2 Conclusions

The prospect of complex network theory based research in analyzing the critical components in smart grid environment is analyzed here with Monte-Carlo simu­lation techniques on various standard test systems. A bidirectional flow graph is constructed from the superposition of forward and backward unidirectional flow graphs. The bidirectional flow graph captures the true power flow scenario of the future smart electricity grid. Electrical centrality measure, motivated by closeness centrality measure of power system, is used to find critical components. Four different measures of impacts are analyzed to quantify the effect of removing critical nodes from the grid. The results found from different measures show that, bidirectional power flow based model is more effective in smart grid environment than unidirectional ones. Rank similarity analysis shows that, critical nodes of bidirectional models do not change much with system topology change as a result of reverse power flow through transmission network in smart grid environment.

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Updated: October 23, 2015 — 12:41 pm