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Creating the Business Case
Published in James Northcote-Green, Robert Wilson, Control and Automation of Electrical Power Distribution Systems, 2017
James Northcote-Green, Robert Wilson
where ALRNA = annual lost energy without automation and ALRDA = annual lost energy with distribution automation. The speed of restoration provided by the DA functions remote feeder switching, FLIR and CLPC (VCLC) is reflected in an improvement to the reliability index SAIDI. Calculation of the respective benefit contributions of each of the DA functions must be considered to avoid double-counting.
A review on resilience assessment of energy systems
Published in Sustainable and Resilient Infrastructure, 2021
Patrick Gasser, Peter Lustenberger, Marco Cinelli, Wansub Kim, Matteo Spada, Peter Burgherr, Stefan Hirschberg, Božidar Stojadinovic, Tian Yin Sun
The ‘resist’ function of resilience describes the ability of a system to withstand disturbances with no or only small fluctuations in its performance. In the case of electric power systems, suitable performance measures include the number of people without power (Sun et al., 2015), the loss of load (Ouyang & Dueñas-Osorio, 2014; Ouyang et al., 2012), the generation capacity available (Sun et al., 2015), and reliability indices such as the System Average Interruption Duration Index (SAIDI) (Layton, 2004). In the oil and gas sector, the deterministic or probabilistic flow through the distribution network is a well-established indicator (Carvalho et al., 2014; Lustenberger et al., 2017; Nadeau, 2007). More than half of the studies use complex networks theory to model the studied infrastructure in order to quantify the ‘resist’ function of resilience (Akhavein & Fotuhi Firuzabad, 2011; Bilal et al., 2016; Bompard et al., 2010; Carvalho et al., 2014; Cimellaro et al., 2012; Cong et al., 2018; Holmgren, 2007; Kim et al., 2017; Kyriakidis et al., 2018a; Layton, 2004; Li et al., 2017, 2016; Lustenberger et al., 2017; Martinez-Anido et al., 2012; Nadeau, 2007; Nezamoddini et al., 2017; Ouyang & Dueñas-Osorio, 2014; Ouyang et al., 2012; Rocchetta et al., 2018; Shinozuka et al., 2004; Su et al., 2017, 2018; Veeramany et al., 2017). Generally, energy system networks are considered ‘complex’ because of their non-trivial topological features, and because their elements (i.e., nodes and links) are neither purely random nor regular. For these reasons, the complex network approach is particularly suitable. Other approaches considered are fuzzy logic (A. Azadeh, et al., 2014a; Bilal et al., 2016; Guo et al., 2016; Makarov & Moharari, 1999), composite indexes (Fisher et al., 2010; Gnansounou, 2008; Molyneaux et al., 2012), agent-based modelling (Nan & Sansavini, 2017), hazard and operability studies (Karimi et al., 2016), multi-attribute utility theory (McCarthy et al., 2007), economic interdependence models (Bing Li et al., 2017) and statistical models (Amirat et al., 2006; Beheshtian et al., 2018a; Feofilovs & Romagnoli, 2017; S. Rose et al., 2012). It is important to note that the concept of reliability is widespread in studies assessing the ‘resist’ function of resilience.