How Utilities Can Prepare for Disruptive Events

Disruptive Events

by Jasjeet Singh, iGATE Global Solutions

Unplanned disruptive events cost the utility industry billions of dollars annually.

Utilities, however, can optimize their emergency plans, contingency plans and restoration activities by using a combination of next-generation technologies.

Consolidated mobility with geospatial analytics helps utilities assess situational awareness, prepare by leveraging situational intelligence, and act with consolidated mobility. Using these, utilities can be ready for disruptive events, minimize socioeconomic impacts and win the confidence of customers and public utility commissions.

Consolidated mobility has significant potential to help utilities during nationwide emergencies by optimizing field resources with meticulous built-in configurable algorithms by leveraging big data and geospatial descriptive analysis to pinpoint and assess damages.

Emergency crews and the field force can be scheduled with required equipment, skill set and location details to restore power swiftly. Consolidated mobility also helps utilities plan for widespread outages to minimize damages and improve the key reliability indices: System Average Interruption Duration Index (SAIDI), System Average Interruption Frequency Index (SAIFI), Customer Average Interruption Duration Index (CAIDI) and Customer Average Interruption Frequency Index (CAIFI).

Business Impact

Utilities have been struggling with more frequent superstorms since 2011. Superstorm Sandy left 7.9 million people across the East Coast without power, and a recent snowstorm affected many National Grid customers.

The storms cause retailers and vertically integrated utilities to lose revenue, and there is tremendous cost in repairing, replacing or refurbishing critical assets to bring the flow of electrons back on grid. The possibility of losing entire business operations exists if a utility fails to act adequately during disruptive events.

For example, Long Island Power Authority (LIPA) recently was asked to hand over its utility operations to New Jersey utility PSEG after intense criticism centered on LIPA’s response to Superstorm Sandy. Ninety percent of LIPA customers were left without power, and some for weeks. The impacts can go from worse to worst if a utility doesn’t have an effective restoration strategy, which can be put in place by leveraging advanced applications and having plans to deploy key crews at strategic locations to minimize estimated restoration times.

Key Issues

Most important are data silos. Utilities have data, but it’s in data marts and cannot be correlated to other data sets to gain actionable insights from collective intelligence.

For example, the distribution management system (DMS) knows the fuse is out on circuit one; the customer care center knows certain customers are out of supply; and the utility’s social media representative knows people from a certain area are tweeting about #outages and #breakdowns. But hidden insight for the dispatcher can be mined by correlating the events, and a field crew can go to the damaged location with the right equipment at the right place, knowing that one fuse will up X number of customers with two hours of effort. Similarly, one can have descriptive intelligence by collating the relevant data sets to optimize the restoration activities while keeping SAIDI and CAIDI controlled.

Consolidated Mobility, Internet of Things, Big Data and Spatial Analytics can Tackle Disruptive Events

Consolidated mobility offers more than handling disruptive events. A utility can plan to have intelligent common dispatch platform (iCDP), which leverages the spatial intelligence embedded with outage management system (OMS)/advanced metering infrastructure (AMI) events or last-gasps. ICDP shall have built in configurable intelligence, which can pick the most suitable field force personnel close to a location, to repair damaged transformers or deal with downed conductors. Having the right person with a relevant skill at the right time significantly improves estimated times of restoration (ETRs) and minimizes socioeconomic impacts.

Predicting the damage assessment by closely monitoring the weather and performing trend analyses helps plan for tropical cyclones. GeoBI or spatial analysis can help in damage assessment and getting the closest available mobile crew route to a specific location.

Conversely, big data analysis can process volumes of unstructured data primarily from social media and optimize the dispatching activities with defined priorities. If the line (conductor) is broken and down, it can be given highest priority, and critical facilities restoration can be prioritized such as for public health and safety, including hospitals, communication towers, water and sewer facilities, and police and fire stations. Big data also can help verify outages.

Smart sensors with embedded intelligence can communicate with one another about the health status of critical equipment and report to the operations center any exceptions by using the Internet of Things. A prescriptive algorithm can run on the machine or sensor data to advise the potential failure of a transformer, breaker, isolator, etc., which can be fixed and prevent cascading outages or potential blackouts. India witnessed a massive blackout in 2012 as a result of load encroachment. There, substation relays perceived load encroachment as a fault, which led to cascading outages.

Functional architecture can help utilities combat disruptive events (see figure).

disruptive events

Key Benefits

Immediate benefits of strategically managing inevitable disasters include:

  • Improved SAIDI and CAIDI;
  • Improved customer satisfaction;
  • Better estimation of ETRs;
  • Optimized crew operations;
  • Brand building; and
  • Reduced loss of revenue by quickly restoring power.

S. Jasjeet Singh is a utilities industry expert with knowledge of advanced DMS, EMS, DRMS, big data, energy storage, demand forecasting, enterprise asset management and mobility. Reach him at or follow him on Twitter @JasjeetKhalsa.

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