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Raising the Bar for Grid Reliability

Feb. 21, 2022
It may be time for utilities to put greater emphasis on MAIFI and criteria related to power quality to gain greater insight regarding overall service quality.

The 2021 State of Commercial and Industrial Power Reliability Report issued by S&C Electric Co. shows that monthly outages among five major industry categories including manufacturing, healthcare, education, small franchises, and retailers doubled in 2020. More troubling may be the data reveal outages on the increase include the short duration variety not captured by the closely monitored SAIDI and SAIFI utility reliability indices. As we wade deeper and deeper into a pervasively digital world, is it time to reassess how we define and maintain system reliability?

The IEEE Guide for Electric Power Distribution Reliability Indices (Standard 1366) is used universally to evaluate distribution service reliability. Possibly the most frequently monitored and reported indices include System Average Interruption Frequency Index (SAIFI), which is how often the average customer experiences an interruption; and System Average Interruption Duration Index (SAIDI) defined as the total number of minutes of interruption experienced by the average customer. These indices relate to sustained interruptions defined by IEEE as an interruption of greater than five minutes.

S&C’s survey indicates that a growing number of commercial and industrial customers are significantly impacted by momentary outages and power quality issues. Residential customers also report dissatisfaction about momentary outages that disrupt the sensitive electronic components now common in household appliances. IEEE uses the Momentary Average Interruption Frequency Index (MAIFI) to characterize short interruptions. It may be time for utilities to put greater emphasis on MAIFI and criteria related to power quality to gain greater insight regarding overall service quality.

S&C found that C&I companies are increasingly taking measures to track outage frequency, duration, and costs to pursue compensation or compel utility corrective action. In defense of electric service providers, the growing complexity of electric networks is not necessarily within their control. With predictions of a 10-fold increase in DERs between 2020 and 2030, supply volatility is increasing. We are not seeing counterbalances via increased demand load flexibility and storage options are not of system balancing magnitude. The solution according to some experts is the same movement that got us to where we are today: digitalization.

Siemens believes the digitalization of our electric infrastructure, including analog processes and data, is a major step in our transition to the future power grid. Aided by digital sensors, and IOT connectivity: data analytics, AI and automation will transform the industry, improving reliability and resiliency. Speaking during a Siemens-sponsored T&D World webinar titled “Adaptable, Secure and Resilient Grids: What Do AI and Automation Offer?”, Exelon reps shared their vision for using analytics to work with data inputs from GIS; operating systems; asset and work management; outage management and reporting; network modelling; energy management; AMI; weather monitoring and customer systems. The potential results from such deep data dives cover a broad spectrum, including optimized network modeling tools; assessments of asset maintenance effectiveness and life predictions; identification of high-risk assets and circuits; outage, storm damage and time of restoration predictions; and much, much more.

Exelon’s efforts are demonstrating transferable program opportunities for improving public and worker safety, lowering O&M costs, and increasing customer satisfaction as well as improving reliability and resiliency. A notable example is the use of data analytics and AI in the areas of vegetation management (VM) and outage prediction. Most utilities trace about 20% of their outages back to vegetation-related causes even though vegetation management routinely tops the list of ongoing maintenance costs. Exelon is using VM analytics to control spending and for outage risk prediction and recovery planning.

Capitalizing on the growing interest in using data analytics to modernize utility VM, AIDASH has combined satellite imagery and AI to facilitate remote monitoring and inspection of ROW conditions and perform a host of sophisticated planning, prioritizing, and review activities. Innovative, case-specific, pretrained algorithms, satellite surveillance imagery and other source data are combined with real-time location, weather, soil, and tree species data to determine clearance and growth rates of vegetation along power lines; identify danger/hazard tree and high-risk areas; plan trim cycle /line clearance; and plan use of herbicide and tree growth regulators. In addition, AIDASH platforms can be used   to improve emergency avoidance, preparedness, and response. Disaster-prone areas can be identified, and avoidance/disaster recovery can be planned in advance. Further, near real-time post-event satellite imagery can be obtained to facilitate damage response and restoration.

Customer expectations regarding reliability and resiliency provide a convincing rationale for utilities to use MAIFI in addition to SAIDI and SAIFI to demonstrate their on-going service improvements. Proactive companies are increasingly using cutting edge digital technologies to plan for, analyze, diagnose, control and maintain their grid systems.  AI and automation are rapidly expanding the areas of our business where we can make meaningful reliability and performance improvements, which in turn will continue to raise the bar.

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