By Carol L. Stimmel

 

Pike Research’s forthcoming assessment of the smart grid data analytics market finds that well over a hundred vendors claim that their offerings can help solve the challenges of managing the grid, from smart meters to synchrophasors.  Start-ups and established players are staking their territory in what will prove to be a very active market ripe with solutions, all with multiple facets and approaches.  This array of approaches is not surprising in what is fundamentally an early market where significant success stories – and complications – have not yet emerged.  Yet, in an environment where safe and reliable energy delivery depends on the effective use of smart grid data for decision-making, the stakes are very high.

 

The problem is that the value of these applications is limited by how well they help human beings do their jobs effectively.  Analyzing smart grid data for grid operations involves the rapid processing of raw data from the power delivery system, and the presentation of that data in a manner that allows the smart grid engineer to draw conclusions for quick and appropriate decision-making.  Combining aspects of information technology, behavioral sciences, advanced analytics, and graphical display and interaction, these transformative systems, if well designed, will break down the barriers between how grid operators think about solving problems and the information they need to act.  (On Aug. 7 Pike Research, in partnership with Smart Grid News, will present a webinar on Smart Grid Data Analytics: Transforming Data into Actionable Intelligence for Utilities.)

Humans create mental models of the real world that help describe how things work; those models aid us in solving problems.  Grid engineers use their own models, with system feedback, to make choices about the best possible course of action to keep the grid functioning.  If the data presented does not align with that mental model, the engineer is left to continuously translate the information coming in, resulting in slower responses, fatigue, and a higher danger of making mistakes.  A classic example of this phenomenon is the Three Mile Island accident, where the post-event inquiry concluded that the design of the control panel – specifically, a poorly designed and misunderstood light – was partly responsible for the disaster.

 

Managing the electricity grid is a complex job, and that complexity will only intensify as utilities incorporate two-way communicating meters, sensors, intelligent electronic devices, and myriad other technologies that monitor and report on the health of the grid.  And while many utility stakeholders are worrying about establishing a return on investment from these modernization investments, engineers are working to make sense of a deluge of data that requires rapid response in changing situations.  Without intuitive systems that provide good situational awareness, ineffective response (or the failure to respond at all) becomes more probable, and can lead to accidents with catastrophic consequences.

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The vendors that offer utilities the best chance of success will understand grid operations and the type of errors that are most likely to occur, and strive to reduce those probabilities through their interface design.  They will test their applications and provide credible evidence as to the extent to which their solution goes to support specific users in the accomplishment of their tasks.  This not only saves money by heightening productivity and bringing us closer to the promises of the smart grid, but it can also prevent disaster and potentially save lives.

 

Carol Stimmel is a research director responsible for leading Pike Research’s Smart Grid practice.

 

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Poor systems design

Every time I read the post-mortem reports for one of the big blackouts, I am struck by how much the systems look like they were *designed* to fail. Incomplete information about system linkages and failure to transfer real-time information are just two of the most obvious problems.

Michael Cain - 07/26/2012 - 10:09

Grid Conditions Need to be Measured

What seems to be missing in many discussions on grid analytics is the actual measure of conditions present. Just as vegetation management progressed though the use of analytics to trend and predict growth that leads to future problems, so the condition of the grid can be measured and trended during routine flight patrols using automated sensors proven to be able to detect broken and contaminated equipment. If load current is removed as a causal condition creating heat at a damaged connector, then period trending is possible because other conditions (ambient for example) are easily known and correlated. Adding automated condition assessment analytics to other utility IT sourced outage and operational data creates a pathway to predictive management of the grid assets.

John Lauletta - 08/15/2012 - 13:28

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