Applying data and analytics to asset management enables utilities to move beyond reactive response to proactive maintenance and predictive management. Developing these capabilities allows utilities to identify and circumvent problems to capitalize on modernization efforts made in grid investments. Because reliability is a utility’s highest priority, insights that can help to maintain and sustain power without interruption are in the spotlight.
This transition from “What happened?” to, “What is happening” to, “What will happen?” is made possible by data and analytics and can be undertaken using a progressive approach that delivers value and insights at each milestone:
- From intelligence to analytics
- From reactive to proactive decision making
- From proactive maintenance to predictive management
As the questions utilities are able to answer about asset management evolves, data and analytics can help to determine where it pays off the most to move from a proactive stance to predictive asset management. Proactive maintenance fuels the advance to predictive management.
Examples of such questions may include:
- Does it make sense financially to replace an asset before the end of its projected, useful life?
- At what point does an aging asset impact service to customers—especially in terms of outage minimization and power quality?
- How much of an asset’s performance can be compromised before it merits replacing the asset—rather than repairing it—to keep the systems working optimally?
Proactive Asset Management
Utilities must prove out the idea that improved efficiency can be profitable. Conservation Voltage Reduction (CVR) is one method that’s carrying its weight in this area. CVR is focused on reducing real power demand on a system enough to avoid adding new peak capacity. As regulators require utilities to achieve higher reductions in power demand, deploying CVR on circuits can deliver reductions ranging from nearly none to 2% on some and as high as 7% on others, enabling them to achieve the mandated reductions.
Another area that data and analytics are helping to make proactive is field operational workflows. An example is consolidating equipment maintenance to cut bottom-line expenses, such as having a field crew take care of meters scheduled for replacement at the same time they’re in the area to work on a set of distribution transformers.
What’s holding back this type of operational efficiency is a lack data integration that allows meter maintenance data to be analyzed with distribution asset data in regards to spatial relationships and the timing of upcoming planned maintenance. Enabling this type of analysis creates much more situational awareness of all near-term maintenance required. This new ability to identify all the assets due for maintenance in a particular area and assign them to a field team’s daily work orders becomes a reality that improves efficiency and cuts costs.
Predictive Asset Management
According to the Utility Analytics Institute, predictive asset management is defined as:
“…the ability to know when to perform maintenance on an asset versus replacing the asset, and to do so in a way that has the best financial benefit while ensuring the maintenance or improvement of power quality, system reliability and customer service.”
To repair or replace is a weighty question in regards to a utility’s infrastructure. Predictive management provides the ability to quickly model what-if scenarios that can clarify the financial and operational impacts of the choice, making the decision more informed and improving the utility’s system planning objectives.
Making the Transitions from Reactive to Proactive to Preventive Pays Off
Once a utility has achieved the level of predictive management, quantifiable impacts to the bottom line will escalate. This includes the ability to avoid unplanned outages that now result when an asset fails unexpectedly; reduce impact to operations from outages; and extend the life for assets—which, in a multi-billion dollar infrastructure, can really add up fast.