Quantifying impacts of automation on resilience of distribution systems

Abstract

Automating the process of restoring service to customers after a large-scale outage event have significant impacts on the agility and speed of recovery in distribution systems. This study develops a set of probabilistic metrics to assess the impact of automation in enhancing the resilience of power distribution systems. The proposed metrics capture the features and detailed process of automatically locating and isolating faults and restoring the service to customers in distribution systems. In addition, this study develops a model to evaluate the spatio-temporal impacts of hurricane on power distribution systems, which is used to generate hurricane-induced outage scenarios to calculate the resilience metrics given different automation schemes. The proposed model is utilised to evaluate the resilience of bus number four of Roy Billinton Test System in the face of a passing hurricane. The metrics are calculated to evaluate the impact of different levels of automation throughout the network, the intensity of the hurricane, and line hardening on the resilience of the system.

Resilience assessment methodologies and enhancement strategies of multi‐energy cyber‐physical systems of the distribution network

“…The impact of extreme natural disasters is generally analysed quantitatively by the extreme natural disaster mathematical models [6,73,76,[81][82][83][84][85][86][87] as shown in Table 2. Besides, Big Data technology is proved to be an efficient measure to deal with quantitative analysis, for example, Jamieson et al [88] drew the system fragility curve based on the existing fault data and then carried out the risk assessment on overhead lines.…”

“…Hours to days Two-three days Large [73] Wildfire disaster Days to months Three-six days Medium to large [76] Earthquake Minutes to days Seconds to minutes Small to large [81] Extreme heat waves and drought Days to months Three-six days Medium to large [82] Hurricane Hours to days Two-three days Large [83] Typhoon Hours to days Two-three days Large [6] Tornado Minutes to hours Zero-two hours Small [84] Flood Hours to days Two-five days Small to large [85] Lightning Minutes to hours Minutes to days Small to large [86] Heavy rainfall Hours to days Three-six days Small to large [87] theory, and traditional network analysis methods, the existing research constructs the dependency relationship in the form of a matrix and the cyber-physical dependency architecture of unified time sequence mapping from the perspectives of information physical mapping relationship [102], information state migration [106], network topology relationship [104], and business association relationship [107]. Tang et al [108] proposed the method of the association matrix to build the coupling relationship model of cyber physics and puts forward the fragility assessment indexes of power, communication, and composite system.…”

“…The impact of extreme natural disasters is generally analysed quantitatively by the extreme natural disaster mathematical models [6, 73, 76, 81–87] as shown in Table 2. Besides, Big Data technology is proved to be an efficient measure to deal with quantitative analysis, for example, Jamieson et al.…”

A risk‐based resilient distribution system planning model against extreme weather events

“…The study in [18] proposes a microgrid formation model, which considers power loss and voltage constraints. In [19], a mathematical model is developed to assess the spatio-temporal impact of hurricane on power distribution systems.…”

Wildfire Risk Mitigation: A Paradigm Shift in Power Systems Planning and Operation

“…The authors in [11] propose a method to quantify the resilience of an infrastructure system by measuring its ability to: 1) resist or prevent hazards, 2) absorb initial damage, and 3) recover to normal operation. This process has been expended in [12]- [14], and a series of resilience metrics have been developed based on measuring the power system's performance over time. The impacts of an extreme event to the power grid can be quantified by considering, for example, the number of customers without power, amount of load curtailed, or number of power system components made unavailable over the course of a catastrophic event.…”