What if I told you that advanced analytics and technology can save 10-15% of vegetation management costs annually?
Utilities spend around $6-8b dollars annually on clearing vegetation from overhead lines. Vegetation management is the largest single operations and maintenance (O&M) expense for most of the utilities in North America and is almost entirely spent on third party suppliers who perform various aspects of the program. Reducing this spend by 10-15% would put almost a billion dollars to the bottom line.
Trees are the leading cause of electric service interruptions, especially during major weather events (e.g., ice storms, hurricanes) and can create public and employee safety hazards, so reducing the spend requires a thoughtful and planful approach to program optimization.
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While it is difficult to replace labor that performs line clearance, their work can be optimized using emerging technology, tools and advanced analytic approaches. A careful investment and deployment of these new tools can help utilities save 10-15% of their annual vegetation management spend without reducing the reliability, safety and customer satisfaction as a result.
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So, how can advanced analytics and technology improve vegetation management? Here, we focus on three specific aspects of the program.
#1 Optimize the allocation of the annual preventive vegetation management spend
Some utilities trim their circuits on a fixed cycle (usually 4 or 5 years) or use outage data from the previous year to determine what the next year’s circuit trim list should be. However, neither one of these approaches takes into account the longer-term experience and view of the reliability associated with the different circuits. Using advanced analytics and tools, leading utilities have developed predictive models to create predictive failure and trimming cost curves to optimize the trim cycle at the individual circuit level.
Tree Trimming Model (TTM) is one example of such a tool that can be used to model 10 years of reliability related to vegetation management based on different cycles, budgets and targets. It has been used to support utilities discussions and agreements with regulators to secure sufficient funding for the vegetation management preventive maintenance program.
In an actual scenario, where a utility was on a fixed 5-year cycle (i.e., trimmed each circuit on a 5 year cycle), TTM was able to take the same spend over a ten-year period, optimize it and improve reliability by 10% over that period of time
#2 Reimagine work planning through the use of advanced analytics
In order to increase the productivity of the VM crews, utilities typically send planners to “walk the circuits” that are identified for trimming and determine what trees have to be trimmed, which ones need to be removed and which customers need to be notified. During COVID-19, these in-person discussions with property owners, to get their approval for removal, have become much less common. As a result, there is an opportunity to rethink the entire process and figure out how to leverage image analytics (e.g., satellite, drone) to identify the work; submitting approval requests to property owners electronically (e.g., e-mail, text).
While there are challenges in using this type of advanced analytics in the areas where there is overhang and satellite imaging cannot certify the distances, it can still be used to identify 80% of the work that needs to be done by applying machine learning (ML) and artificial intelligence (AI) algorithms to image analytics.
These technologies are already available and there is an opportunity to deploy them and leverage the experts (i.e., Foresters) to train these algorithms and build confidence that they are working properly over the next couple of years. At that point, the results from this analysis can be used to further refine the priorities, optimize the crew assignments and minimize the cost of acquiring property owner approval.
The experienced resources and experts should be used to deal with any escalations from the field and manage more complex issues, while the technology is used to build 80% of the plan.
#3 Automate the quality assurance/quality control process
As we mentioned previously, most of the field work related to the VM program is conducted by third party suppliers. In order to ensure that the circuits which were trimmed have adequate clearances, utility foresters conduct either 100% or statistical sample QA/QC reviews of the work.
Sometimes these field inspections are done soon after the work is completed and other times a few weeks or months later. When the inspectors find an issue, they refer it back to the contractor who then has to roll another bucket truck to make the correction, which one way or another adds costs.
With the proliferation of the technology and image analytics, the QA/QC process can be almost fully automated.
Imagine the future, where the trimmer can take a picture of the span that has have just completed using an app that automatically submits that image for analysis against a database using ML and AI algorithms. The trimmer then gets immediate feedback that identifies where there may be a lack of clearance, so that the crew can move the bucket truck 15 yards and fix the issue right away. This way, they would eliminate the need for rolling another truck (each roll costing about $200-300 all in) and minimizing the cost of conducting field reviews of the contractor work. Finally, it provides a clear record of the work (from the images) and the audit path for the invoices.
Cost benefits realized
Vegetation remains one of the main causes of outages for utilities and the single largest O&M expense. With a capital investment in new technologies and advanced analytics over the next couple of years, utilities can sustainably reduce their VM O&M spend by 10-15% a year over the longer term. The cost benefit realized from this investment makes it an easy business case for both internal approval and regulatory return on investment. Contact me to learn more.
 Accenture analysis of Federal Energy Regulatory Commission (FERC) Form 1 filings and client experience