To risk or not to risk? that is the question – AI is the answer
- Dec 8, 2021 6:18 am GMT
This item is part of the Data Analytics & Intelligence - December 2021 SPECIAL ISSUE, click here for more
Written by: Dr. Andy Skumanich and Dr. Manny Ghiassi
With apologies to Shakespeare, the current state of the utility sector is in a quandary. Historically the energy domain was built to be a low-risk-taking domain. And understandably, they became good at not-taking-risks. However, as with Mr. Shakespeare, those days are gone.
Now the real dilemma – what about the risk that comes with being risk adverse. Is that an oxymoron? No, it is a legitimate consideration for present day utilities. Utilities are under increasing pressure to reduce costs while still maintaining reliability and all the while reducing risk. Electricity markets try to strike a balance between reliability and affordability, but the latter is in a short-term mode, and in need of forward-looking investments. Many utilities continue to try to maintain the old ways of proven solutions. For example, they have a set schedule for trimming vegetation which could impact transmission lines, and they address hardware issues when this equipment presents a problem or failure. They may ramp up personnel ahead of bad weather, but is there much pro-active planning for predicted storms and the possible impacts? We recently saw that the Texas grid had some weather-related issues during a deep freeze, due in part to the complexities of substantial wind power (at 22.8% in 2020) which relegated standard power plants to lower utilizations and perhaps lower investment in upgrades. Portfolio forecasting could help anticipate production trends and could benefit from AI/ML. In California, there are now Public Safety Power Shutoffs. A new PSPS may be brought on when it becomes necessary to turn off electricity during very dry and windy conditions, combined with a heightened fire risk. Although a step in the right direction, however, these show just how limited the approach is for identifying and developing pro-active capabilities, which can save money in the long run.
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