Resiliency Analysis of Large-Scale Renewable Enriched Power Grid: A Network Percolation-Based Approach

Resiliency Analysis of Large-Scale Renewable Enriched Power Grid: A Network Percolation-Based Approach

Abstract Recent trend of integrating renewable energy into the power grid poses new challenges like power quality, voltage stability, etc. Due to the intermittent behavior of the renewable energy sources power flow pattern changes continuously throughout the grid which adds more complexity to the grid monitoring and control task. Although the percentage of renewable energy consumption is low in Australia, the electricity generation sector shares a large proportion of the total renewable usage and the usage rate is increasing every year. The constant increase of generation in the existing transmission network creates a huge burden on the system and frequent large-scale contingencies are expected. The trouble encountered in analyzing systems like power grid is that individual behavior of its components is reasonably well understood. It is designed to behave collectively in an orderly fashion but sometimes it shows chaotic, confusing attitude, and sometimes behave destructively like when blackout occurs. Complex network theory provides an alternative but promising platform to analyze networked system like power grid where traditional approach fails to provide solution. In this chapter, a complex network framework-based network resiliency (percolation) analysis has been presented. A topological model of transmission level Australian National Electricity Market (NEM) with projected renewable integration has been simulated. The effects of random and targeted removal of transmission lines or substations on the network structure and functionality have been analyzed. A fast and simple algorithm to analyze percolation on large-scale power grid has also been addressed.

Keywords: Complex network · Cascading failure · Blackout · Percolation


Power systems play an indispensable role in modern society. However, there have been several large-scale blackouts in recent years in spite of technological progress and huge investments in system reliability and security. For instance, in August 1996, more than 4 million people in several western states of the USA were out of the power service [1].

In August 2003, a historic blackout was triggered in the power grid of the United States and Canada, which disconnected 61,800 MW of power to an area spanning most of the north-eastern states of the USA and two provinces of Canada, totally containing more than 50 million people [2]. Besides, in the summer and autumn of the year 2003, several large-scale blackouts happened, such as London blackout in the UK, Sweden–Denmark blackout and Italy blackout, etc. [2].

Prevention of large-scale outages is attributed to the security assessment and monitoring system. Recent series of blackouts occurring all over the world shows that the system designated for prevention of blackouts is not working well, which stimulates researchers to seek solutions from alternative means. Recently advances of research in complex network field have attracted the interest of researchers of the power grid to model and analyze the century old power grid under the complex network framework.

In case of a power system, the number of possibilities to be analyzed is huge.

Suppose we want to analyze the consequence of every line getting tripped with faults in several locations in the Australian power grid. It is just too complicated, time-consuming, and does not make any sense. So, first of all, from some topological characteristics of the network we have to find few cases which we should study in depth. The number of contingency is too large, somehow we have to decide which contingencies are important and which are not. Complex network framework can be used for this purpose.

If the network structure is known, several measures or matrices could be developed, which can identify particular features of the network. Social scientists have used several centrality measures [36] to explain a person’s influence within a network. Among these centralities most widely used measures are degree centrality, betweenness centrality, and closeness centrality. To analyze the vulnerability of the power grid or to measure which nodes are more important within a power network, these centrality approaches were used by researchers [710]. Some of this research considered the power grid as an abstract network and neglected concrete engineering features, whereas some literature studies considered various features like impedance or admittance of various lines.

Network percolation-based analysis has been carried out in case of the power system [5, 11]; Ref. [5] provided an excellent generalized framework for resiliency analysis for a networked system, but did not consider important vulnerability characteristics for the power system. The theoretical analysis of [11], although promising for planning operation of the power grid, but cannot be used for dynamic security assessment and monitoring systems, due to negligence of various operating parameters while modeling the power grid.

In this chapter, a percolation-based approach is devised to analyze the resiliency of the power grid. A fast algorithm is formulated to facilitate percolation analysis of large-scale power grids. Various standard IEEE test systems are simulated along with the Australian test system to simulate percolation in real power grids. An analysis of the effect of large-scale renewable energy penetration on the percolation threshold, which is an indicator of network robustness, is carried out at the end.

The rest of the chapter is organized as follows: Sect. 2 discusses the power system model for percolation-based analysis. Section 3 deals with basics of percolation process. Sections 46 concern about various centrality measures of the power system. Section 7 provides simulation results, while Sect. 8 concludes the chapter.

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