Grid Analytics Offer Opportunities for More Efficient Spending

by Bradley Williams, Oracle Utilities

The advent of grid analytics is proving to be a boon to transmission and distribution (T&D) asset management, offering distinct opportunities for better and more efficient operations and maintenance (O&M) spending, particularly in predictive and proactive maintenance rather than the more historic, run-to-failure operation of assets.

Asset maintenance always has been a balancing act between efficiency—the cost of providing the equipment—and effectiveness—the availability and efficiency of the equipment.

Enterprise asset management, aided by operational analytics, can provide the fulcrum that creates the balance between a utility’s efficiency and effectiveness.

Why is this balance essential?

If availability is not important, it is easy, without technology, to provide a low-cost service: Simply run the asset to failure and then buy what is needed at the lowest possible cost during normal workday hours.

Availability will suffer, but the maintenance department might minimize its costs.

The opposite extreme is to over-maintain the asset by doing every conceivable inspection and replacement before need and regardless of cost.

The asset would be available, but the costs of maintaining it in such a fashion would be onerous.

The essence of most utility maintenance strategies is to balance the availability of assets against the costs of providing that availability.

Because utilities have many assets and components to worry about, software is the best solution with which to do this; however, within the massive scope of assets and components a utility has to maintain, there is no single best-practice maintenance strategy.

Each particular asset strategy will depend on the characteristics of the asset, the value of the asset to production, the risk and impact of failure, the maturity of the organization and the maturity of the technology available.

Using Meter Data for Operational Efficiencies

There is a clear business case to be made for using automated metering infrastructure (AMI) data, for instance, for far more than customer-facing applications (i.e., providing usage information to customers and using it to provide proactive customer alerts, personalized communication and segmentation-driven marketing offers).

Analytics is fundamental to sustaining and improving utility business performance right across the enterprise, including improved reliability, operational efficiency and safety.

Through better planning and execution, from revenue assurance and employee utilization and prioritized fieldwork to the reduction of infrastructure and asset replacement costs, operational efficiencies can be improved by leveraging data from multiple sources across organizational departments for new insights into utility operational performance.

The utilities surveyed in Oracle Utilities’ “Accelerating the Drive to Value” big data study in 2013 overwhelmingly indicated they expect to achieve the greatest value from new data in mainly operational areas, including revenue protection, reduced asset maintenance and asset replacement costs, reduced infrastructure costs and the ability to analyze distributed generation.

Analytics also can be used to improve safety by better understanding and proactively mitigating risks.

Asset management is an obvious example, but vegetation management also can be managed in this way to eliminate unnecessary outages and unneeded line clearing.

Public safety can be improved through analytics, as well: Utilities can analyze usage spikes for the benefit of the individual customer (a potential water or gas leak or a malfunctioning appliance), and usage spikes also can indicate a potential public safety hazard that a utility can act upon quickly as soon as it is identified.

For example, through daily monitored tests, data analytics can identify unfettered gas leakage in vacant homes caused by copper pipe theft.

Electricity theft also has been flagged quickly through data analytics.

In one utility’s case, analytics performed on data from 130,000 AMI meters deployed quickly turned up 228 meters with jumpers or tampering and 71 stolen meters used in place of disconnected meters, according to Commonwealth Edison Co.

According to the utility, the leads provided by data analysis provided valuable insight into pattern theft and tampering and has “transformed a very manual process of data mining from multiple systems into an automated process” with a hit rate through September 2013 of 89 percent.

Transformer Load Management Revisited

Smart meter and AMI data, once analyzed, also can be a boon for distribution planning and operations.

Operational analytics can aid a utility by providing an historic and a real-time view of the utility’s operations.

Add predictive analytics to the mix, and the utility then can begin to compare historical data to identify trends in usage and asset health, overlay weather maps and forecasts, and forecast demand to more accurately predict energy usage, the impact to the grid of renewable and distributed generation and more.

In the case of distribution transformer management, for example, the utility can identify overloaded units and those that have exceeded their design life by aggregating meter-by-meter AMI data at each distribution transformer.

This type of early identification and proactive replacement of at-risk transformers can save a utility millions of dollars per year in transformer failure costs.

To take this example a step further, let’s say that a utility first wants to identify at-risk transformers with high load growth so it can better size failure replacements.

By aggregating and analyzing historic customer load to prevent overload failures, it can identify transformers with year-to-year load issues of more than 25 percent and prioritize them for early replacement.

These types of analyses also offer potential uses for load balancing, system planning, new services and design support.

In addition, in areas such as San Diego and other parts of California where electric vehicle (EV) use is rising, predictive transformer load analysis based on EV purchases reported to the utility can determine which transformers will need to be upgraded to meet the increased load.

Similarly, meter analytics also can identify new EV load at a premise that could further trigger an automated transformer loading evaluation to assure ongoing safe and reliable service.

What’s Next?

Beyond customer-facing operations such as billing and call center support, analytics offers utilities abundant opportunities—including meter operations, revenue protection, demand-side management and distribution operations and planning—to better manage their businesses, just as they have begun to manage their customer relationships differently.

And new analytics processes eventually will migrate into standard operating procedure to be replaced by newer and even more potentially complex analytics issues.

The art of the possible with analytics constantly will be in a state of flux.

But being able to better analyze and predict asset health and manage potential risk of outages or leaks in the meantime is a quick and certain positive that can turn what has been a reactive, run-to-failure utility approach to asset and outage management into a much more proactive, cost-reductive process that can improve safety and reliability.

Bradley Williams is vice president of industry strategy at Oracle Utilities and has more than 26 years of utility technology innovation experience. He has a Bachelor of Science in Electrical Engineering and an MBA from California Polytechnic State University, holds four U.S. patents on smart grid-related technologies, is a registered professional engineer in California, and is an active member of IEEE PES, UCA International Users Group and CIM Users Group.

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