Natural hazards, like wildfires, present various challenges to the electric grid that can leave many communities without power. To identify vulnerabilities in the grid and the corresponding at-risk communities, this work considers the implementation of two Graph Theory assessment approaches, namely betweenness centrality and minimum cut, and combines the results from each with spatial fire probability data to produce a novel assessment of communities at-risk of losing service because of a wildfire. The results from a betweenness centrality analysis identified at-risk communities whose critical lines, necessary for routing power to the community from the numerous generators, were found to be at-risk if they were located within high probability burn zones. Communities at-risk of separation from the grid with one cut (or electrical shorting) of a transmission line due its proximity to a high burn probability (BP) area were also identified using the minimum cut Graph Theory algorithm. When the methodologies were applied to a demonstration transmission grid, the results found that about one third of the 585 substations had centrally located lines in high BP areas. About 46% of the substations require just one cut to be removed from the grid, and the average length of these one-cut segments was 37 km and the longest was 188 km.

Electric Grid Vulnerability Analysis to Identify Communities Prone to Wildfires

It is a challenge to search fault chains for facilities in power grid to provide rapid warning for the smart power grid. In this paper, a data fusion approach for searching fault chains (DF-FCS) is proposed. We take into account the factors for the association between facilities in the fault chains in power grid, such as the operation states, fault risks, spatial distance and fault time when the cascading faults occur. Based on the datasets from WAMS, SCADA and GIS systems, the metrics and the process for determining the initial and successive faults of facilities in fault chains are presented. Experiments over the real datasets from local network of China Southern Power Grid showed the effectiveness and accuracy of DF-FCS.

A Data Fusion Approach for Searching Fault Chains in Power Grid over Datasets from Multiple Systems

With the rapid development of urbanization and modernization, people’s requirements for electric power are also consequently increasing. It is thus essential to ensure the sustainable and stable supply of power and maintain the safety performance of the power grid. Constructing a modern safety management system (SMS) can effectively improve power enterprises’ ability to respond to emergencies. To effectively evaluate the ability of electric power enterprises to resist risk impact, and the recovery and optimization ability after impact, this paper makes a systematic investigation of the SMS of power enterprises. It evaluates the safety resilience capabilities of the SMS from four basic elements of stability, redundancy, efficiency, and adaptability. A multilevel evaluation system of electric power enterprises’ safety resilience capacity consists of four first-level and 25 second-level indices. Then, an analytic hierarchy process (AHP) and matter-element extension (MEE) joint model—the AHP-MEE evaluation model of the resilience capacity of power enterprises—is proposed. A case study of electric power enterprise safety resilience evaluation is conducted using the proposed resilience evaluation index system and assessment model. The results showed that the provided model agrees with the actual situation and verified the index system and model for electric power enterprise safety reliability and applicability. Improved enterprise safety resilience recommendations are proposed accordingly based on the evaluation results. This work proposes a robust safety resilience capacity evaluation method in the face of upcoming new risks, thus building a good working foundation to construct a new power system with new energy sources.

Resilience Capacity Evaluation for the Safety Management System of Power Grid Enterprise Based on AHP-MEE Model

In order to solve the problem of voltage sag, a method for preventing and controlling the voltage sag on the grid side is provided. Through the analysis of the correlation and conflict between the investment cost and economic loss in the prevention and control of voltage sag. An optimization model considering the voltage sag prevention scheme by reducing short-circuit faults on the grid side. Taking the IEEE 30 node system as an example, the genetic algorithm is used to simulate the model, and obtain the specific prevention scheme of the corresponding line.To achieve the purpose of reducing the investment cost of voltage sag prevention and control, and reducing the annual economic losses of voltage sags suffered by sensitive users of the entire network.

A Method for Preventing and Controlling Voltage Sag On Grid Side Considering Economic Benefits

Natural hazards, like wildfires, present various challenges to the electric grid that can leave many communities without power. To identify vulnerabilities in the grid and the corresponding at-risk communities, this work considers the implementation of two topological assessment approaches, namely betweenness centrality and minimum cut, and combines the results from each with spatial fire probability data. The results from a betweenness centrality analysis identified at-risk communities whose critical lines, necessary for routing power to the community from the numerous generators, were found to be at-risk if they were located within high probability burn zones. Communities at-risk of separation from the grid with one cut (or electrical shorting) of a transmission line due it’s proximity to a high burn probability (BP) area were also identified using the minimum cut Graph Theory algorithm. When the methodologies were applied to a demonstration transmission grid, the results found that about about one third of the 585 substations had centrally located lines in high BP areas. About 46% of the substations require just one cut to be removed from the grid, and the average length of these one-cut segments were 37 km with the longest at 188 km. 

The large-scale interconnection of the power grid has brought great benefits to social development, but simultaneously, the frequency of large-scale fault accidents caused by extreme weather is also rocketing. The power grid is regarded as a representative complex network in this paper to analyze its functional vulnerability. First, the actual power grid topology is modeled on the basis of the complex network theory, which is transformed into a directed-weighted topology model after introducing the node voltage together with line reactance. Then, the algorithm of weighted reactance betweenness is proposed by analyzing the characteristic parameters of the power grid topology model. The product of unit reliability and topology model’s characteristic parameters under extreme weather is used as the index to measure the functional vulnerability of the power grid, which considers the extreme weather of freezing and gale and quantifies the functional vulnerability of lines under wind load, ice load, and their synergistic effects. Finally, a simulation using the IEEE-30 node system is implemented. The result shows that the proposed method can effectively measure the short-term vulnerability of power grid units under extreme weather. Meanwhile, the example analysis verifies the different effects of normal and extreme weather on the power grid and identifies the nodes and lines with high vulnerability under extreme weather, which provides theoretical support for preventing and reducing the impact of extreme weather on the power grid.

Changfan Li

Papers

Topological Modeling Research on the Functional Vulnerability of Power Grid under Extreme Weather