Distribution Automation: Readiness, Levels, and Intensity

Written by Alireza Fereidunian, Sahand Ghaseminejad Liasi, Mohsen Kojury-Naftchali, and Ali Alizadeh

Recent developments in information and communication technologies have led to growing penetration of the automation technologies in electrical power distribution systems. Nowadays, perform automatic processing and action using advanced detection, processing and control units is a trending practice in power distribution systems. In this letter, distribution systems automation or simply “distribution automation (DA)” is elaborated, regarding a systems approach.

Automation can be defined as “application of machines to tasks once performed by human beings or, increasingly, to tasks that would otherwise be impossible” [1], thus, DA can generally be regarded as the application of human-made systems to tasks once manually performed or to tasks otherwise be impossible, in distribution systems management and operation. Distribution automation is a process that enables an electric utility to remotely monitor, coordinate and operate distribution components in a real-time mode from remote locations.


Automation of the FLISR Process

In many utilities, distribution automation has another interpretation: applying automation to FLISR process (Fault Location, Isolation, and Service Restoration), to enhance system reliability. Higher reliability means less financial loss for the system operator, as well as less interruption cost for the consumers, and higher efficiency for the whole enterprise [2]. A simple definition of FLISR process is brought below [3,4]:

At each fault occurrence, the very first important step is identification of the fault location. While the fault cannot be cleared in a short time, the fault location must be isolated from the remaining healthy part of the grid. Then, to reduce the interruption time of costumers, utility companies have to provide the fastest possible approach to recover the disconnected load points outside the isolated section. Service restoration can be performed through different strategies: supplying load points through auxiliary power paths, exploiting distributed energy resources are amongst the most common approaches.

Although rapid service restoration results in enhancing the reliability of the system, and reduction in costumers’ interruption and system operators, it requires remarkable investment. There is a trade-off between the reliability of the system and initial investment. Proper placement of manual and remote-controlled switches can enhance the reliability.


Automation Intensity Level

In general, Automation Intensity Level (AIL) may be introduced as the penetration of automation along the feeder system, while a very simplistic approach to AIL defines it as the percentage of the automatic (remote-controlled) switches among all switches in distribution system, concentrating on action automation only [5].


Distribution Automation Readiness

Distribution Automation Readiness (DAR) represents the potential (readiness) of a distribution system to gain the most by an automation investment development. Major indicators of DAR include, but not limited to [6]:

  • SAIDI: System Average Interruption Duration Index
  • SAIFI: System Average Interruption Frequency Index
  • MAIFI: Momentary Average Interruption Frequency Index
  • ENS: Energy Not Supplied/Served
  • CIC: Customer Interruption Cost
  • Number of customers
  • Total Demand
  • Ease of Access for Repair/Restoration Crew
  • Bad (Hot/Cold/Windy) Weather

Why DAR is important?

Since the budget is limited for distribution companies, they have to prioritize their areas based on their DAR. For example, if an area suffers from bad reliability condition, but it has a remarkable number of switches or communication infrastructure, this area can be placed at the top of the list. Because it enjoys good infrastructure which can facilitate the deployment of automation.


Levels of Automation (LOA)

Level of automation (LOA) indicates the autonomy of the computer/machine/automation system. LOA increases by increasing the automation systems role and decreasing the human role [7].

LOA Description
10 The computer decides everything, acts autonomously, ignoring the human
9 informs the human only if it, the computer, decides to
8 informs the human only if asked, or
7 exectutes automatically, then necessarily informs the human, and
6 allows the human a restricted time to veto before automatic execution, or
5 executes that suggestion if the human approves, or
4 suggests one alternative
3 narrows the selection down to a few
2 The computer offers a complete set of decision / action alternatives, or
1 The computer offers no assistance: human must take all decisions and actions



  1. M.P. Groover, Automation, Encyclopædia Britannica (2020). https://www.britannica.com/technology/automation.
  2. A. Shahbazian, A. Fereidunian, S.D. Manshadi, “Optimal Switch Placement in Distribution Systems: A High-Accuracy MILP Formulation”, in IEEE TSG, 11(6), Nov. 2020, pp. 5009-5018.
  3. F. Dongming, et al "Restoration of smart grids: Current status, challenges, and opportunities." Renewable and Sustainable Energy Reviews, 143 (2021): 110909.
  4. A. Fereidunian, M., Abbasi, “Service Restoration Enhancement by FIs Deployment in Distribution System Considering Available AMI System”, IET GTD, 14(18), Sep. 2020, pp. 3665-3672.
  5. J. Northcote-Green and R. G. Wilson, “Control and Automation of Electrical Power Distribution Systems”, CRC Press, 2006.
  6. A. Fereidunian et al “Automation Readiness Evaluation: Ranking Distribution Company Areas for Automation to Improve Reliability”, in Proc. of the EPDC2021, Alborz EDC, Karaj, Iran.
  7. 7. A. Fereidunian, C. Lucas, H. Lesani, M. Lehtonen, "Challenges in Implementation of the Human-Automation Interaction Models", in Proc. of the 15th MED'07, Athens, Greece, June 27-29, 2007


This article edited by Hossam Gabber

For a downloadable copy of the July 2021 eNewsletter which includes this article, please visit the IEEE Smart Grid Resource Center.

Alireza Fereidunian profile picture
Alireza Fereidunian (M'09) received the M.Sc. and Ph.D. and M.Sc. degrees all in electrical engineering from the University of Tehran, Tehran, Iran, in 2009 and 1997, respectively. He is currently an Assistant Professor at the K. N. Toosi University of Technology, Tehran, and Postdoctoral Research Associate at the University of Tehran. His research interests include smart grid, high reliability distribution systems, and application of IT and AI in power systems. Moreover, he works in complex systems, systems reliability, and human-automation interactions areas, where he has invented the Adaptive Autonomy Expert System.
Sahand Ghaseminejad Liasi profile picture
Sahand Ghaseminejad Liasi received his B.Sc. and M.Sc. degrees in electrical engineering from K. N. Toosi University of Technology, Tehran, Iran, in 2016 and 2019, respectively. His research interests include smart grids, demand response, power quality, and power electronics. Sahand has published more than 10 articles and registered 1 patent. Furthermore, he has cooperated as a volunteer member in IEEE dot series standards. In addition, he has served as a volunteer member in IEEE electric vehicles white paper working group. Sahand Liasi has been a reviewer of various prestigious journals and conferences, including IEEE Transactions on Power Electronics and IEEE Transportation Electrification Conference and Expo 2020 (Chicago, Illinois, USA).
Mohsen Kojury-Naftchali profile picture
Mohsen Kojury-Naftchali is graduated in electrical power systems engineering from University of Tehran, Tehran, Iran in 2016. His research interests covers data analysis, data mining, time series analysis, Big Data issues.
Ali Alizadeh profile picture
Ali Alizadeh received B.Sc. in electrical engineering from Shahid Madanani University of Azerbaijan, Tabriz, Iran, in 2017. He also received the M.Sc. in power system engineering from the University of Tehran, Tehran, Iran, in 2020. Currently, He is a Ph.D. student at Laval University, QC, Canada. His research interest includes reliability and resiliency of distribution systems as well as optimization and artificial intelligence applications in smart grids.

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