About Digital Asset Management: Answering Your Webinar Questions


I recently presented an Energy Central webinar on digital asset management, along with Bill Ernzen of Accenture. Unfortunately, the webinar took up all of the allotted time, leaving no time for Q&A with the audience.

Energy Central kindly shared with me the questions that participants submitted during the webinar. I’ll do my best to answer those questions here. I’ve adapted some of the questions to make them work as part of a blog post.


The focus of your talk is based on distribution utilities. How do concepts presented apply to the working environment of transmission utilities?

In the Asset Intelligence section of the webinar, our software demonstrated the capabilities in scoring the condition metric for large transformers and charting the dissolved gas trends over time on a Duval triangle, along with the DGA metrics. Similar capabilities are built into the software for high voltage circuit breakers, tap changers and generation step up transformers, among many other asset types.

Do you see any difference in adoption of digital asset management across gas, electricity or water companies?

All utilities that installed their asset in the 1960’s and 19070’s face a unique challenge, whether they are electric, gas or water. Though gas, water and sewer utilities operate differently than electric, the underlying pain points are very similar. Our software has accommodated their need in how it performs the analysis. The software is structured to perform analysis around business-centered needs, and method applies to electric, gas and water utilities. The method builds from asset health indices to probabilities of failure to risk scores, which are then used for maintenance prioritization, replace versus refurbish analysis and capital planning. The presentation and analysis capabilities also are very similar, except for specific regulations that change some calculations.

Is there any dollar value on what digital asset management can help avoid as ‘risk’ or prevent as ‘avoided cost’?

Putting a dollar value on avoided cost is fairly straightforward. For example, if you can successfully postpone spending money, then you can use your cost of borrowing (weighted average cost of capital) and the inflation rate to calculate the financing costs you didn’t pay because you didn’t do the project.

Putting a dollar value on avoided risk is a little more abstract. Let’s say that an aging power transformer represents an economic consequence of $10 million, should it fail. Assume that, before you start your digital asset management project, that transformer has a ten percent probability of failure. You could describe the transformer’s risk as ten percent of $10 million, or $1 million.

If digital asset management practices directed you to refurbish the transformer to reduce the probability of failure to one percent, then you lowered the risk by $9 million. If your refurbishment project cost you $4 million, then you realized a 225 percent ROI in terms of risk reduction.

How do you define criticality? Aren’t you mixing probability and consequences in your definition of criticality?

In our definition, criticality is the resulting consequence should a failure occur. Consequence can encompass lost revenue from power outage, cost to replace damaged equipment, crew wages to restore power and replace equipment, and other costs. A unique feature of our software is that it runs a connectivity analysis through a topology processor to identify upstream and downstream assets and impacts thereof.

Probability is the likelihood that failure will occur, regardless of the consequences of failure. Probability is based on the age of the asset and its condition, load factor, network relationships and other considerations.

Risk is the product of failure probability multiplied by criticality.

How precisely do you compute your risks?

Where the data for calculating the asset health index, probability of failure and criticality are quantified and precise, our calculated risk metrics are precise.

How do you take “expert feeling” into account?

Our software provides flexibility to customers to tune the asset health index, criticality and probability metrics to match their knowledge and experience by allowing them to modify the factors and weightings in algorithms.

How do you maintain temporal consistency when you have very fast data streams such as PMU and inspection reports which may be once a year?

Our software uses the most recent applicable data in calculations, regardless of its comparative frequency. Where you get into trouble is when you have outdated data, whether it’s monthly data that’s a year old or hourly data that’s a week old. This is why our software computes two additional metrics/indices called Completeness and Confidence. The Completeness index identifies if any data sets were unavailable for computation while Confidence measure if the data sample expected at a point in time was received before the indices were computed. This can be used as an indicator of data quality, data availability or a missed inspection cycle.

Is there a way you can estimate the sensitivity of the risks to your entire system?

Our criticality scores incorporate network connectivity information, and therefore reflect impact on the entire system. Let’s assume that, through an oversight in network design, you have a new, small transformer that sits at the nexus of your entire network and has no redundancy. That transformer could have a very low probability of failure, because it’s brand new, and a sky-high criticality score because it’s the linchpin of your network.


Ajay Madwesh is Vice President of the Utilities Business Unit at Space-Time Insight. He possess more than 20 years of experience in software development and technology management in Utility and Process automation environment, and has spent several years evangelizing the integration of real-time operational technologies with IT. He has previously held leadership roles at top companies such as GE, ABB and Infosys.


The Industrial Internet of Things


Except for smart electricity meters (if your house even has one), the Internet of Things is likely not invading the average home in the near future. Individual consumers are having a hard time justifying connecting their refrigerators and dishwashers to the Internet.

On the other hand, the Internet of Things is likely already up and running in your workplace and your city. For years now, cities and industries have been cutting costs and improving service by connecting their processes to the Internet. Consider these uses:

  • As this Venture Beat blog points out, a GE jet engine generates 500 GB of data per flight. Fly a four-engine plane, that’s 2 TB of data. Think about that on your next business trip.
  • In areas with toll road and toll bridges, planners and researchers use data from toll payment transponders to study traffic volumes and patterns.
  • Harley-Davidson has connected the machinery and systems in its York, PA manufacturing plant to automate workflows and optimize production systems without human intervention.
  • As mentioned in this Accenture report Apache Corporation, an oil and gas company, is using IoT approaches to predict and avoid pump failures that reduce production.

Some have taken to calling this the phenomenon the Industrial Internet of Things. There’s even an industry consortium.

It’s in this industrial arena, and not your house, that situational intelligence shines. Situational intelligence excels at analyzing and visualizing multiple, disparate silos of data. Companies and cities are where you’ll find those silos of data.

This isn’t to say that individual consumers won’t benefit from the Internet of Things. Fewer traffic jams, more reliable service, higher quality products, better environmental protection—all are possible using situational intelligence to analyze and visualize data from the Industrial Internet of Things.


Microgrids and Situational Intelligence


The increasing efficiency and affordability of distributed and renewable energy sources such as solar, wind, fuel cells, and batteries creates more opportunities for portions of the grid to function independently, in configurations known as microgrids.

As defined by the U.S. Department of Energy, microgrids are “localized [energy] grids that can disconnect from the traditional grid to operate autonomously and help mitigate grid disturbances to strengthen grid resilience.”

Microgrids can range in scope from an individual house to part or all of a distribution feeder, the area served by a substation, or an entire island.

Image courtesy of U.S. Dept. of Energy
Image courtesy of U.S. Dept. of Energy

Resilience is a core characteristic of microgrids, but resilience comes in many forms. It can mean providing secure and continuous power to critical sites such as hospitals, military bases, data centers and water treatment and pumping facilities. Resilience can mean helping incorporate distributed energy generation into the grid to balance supply and demand. It can mean stronger defense against and faster recovery from storm-related outages.

Microgrids are a great use case for situational intelligence. Why?

  • They are geospatial entities.
  • They are networks connected to other networks.
  • They rely on multiple, disparate data sources.
  • They depend on real-time operations.

As described in a previous blog post, situational intelligence applications excel at unifying the utility Internet of Things. Microgrids are a microcosm of the large utility Internet of Things. Information from smart metering, SCADA, outage management, workforce management, distributed energy source and other utility data sources can be analyzed and visualized to provide a whole and coherent real-time picture of the grid to aid in operations, maintenance and planning.

According to Navigant Research, more than 400 microgrid projects are under development worldwide. Notable examples of microgrids include:

  • New York University, where the microgrid successfully detached from the main grid during Hurricane Sandy and continued to power much of the campus
  • Sendai microgrid in Japan, which powered a nearby hospital for two days after the 2011 earthquake and tsunami
  • The 1,200-person ecological estate in Mannheim-Wallstadt, Germany
  • Bornholm Island, Denmark

As distribution automation permeates more and more of the grid, we’ll see more microgrids of varying scopes bolstering the reliability of power delivery.



Situational Intelligence Makes Electric Vehicles More Useful, Even When They’re Parked


When you buy an electric vehicle (EV), you get a big battery. The Nissan Leaf comes with a 24 kWh battery; the Tesla Model S sports one with 54 kWh capacity.

In a previous post, I talked about how situational intelligence helps EV owners stay within range of a charging station while they are out driving. But what about when your EV is parked at home—can situational intelligence help you get more value from your big, mobile battery?

First, it’s useful to understand how the capacity of a car battery compares to the power usage of a regular household.

An average US household uses approximately 900 kWh per month, according to the Energy Information Agency. Opower says that the bottom 90 percent of the economic strata use about 600 kWh per month. So, at 20-30 kWh per day for most households, and battery capacity of 24-54 kWh for most EVs, it’s conceivable for your fully-charged car to power your house throughout an entire day. (Of course, if you did that, you’re probably not driving anywhere that day.)

One way an EV battery is useful at home is demand response. In times of high grid demand, utilities could instruct houses to draw power from the EV battery instead of the grid, to reduce the need for additional, expensive peak generation. Demand response events range in time from a few minutes to a few hours, so demand response doesn’t need to put a crimp on EV range.

Another way an EV battery is useful at home is energy storage. At times of high grid supply, either at night when usage is low or on sunny days full of residential solar power, the EV battery can store excess energy and help keep the grid in balance.

Situational intelligence makes EV batteries more useful while parked by correlating, forecasting, analysing and visualizing multiple data sources such as

  • EV location and battery capacity
  • Grid energy supply and demand
  • Renewable energy supply and demand
  • EV owner’s demand response and car charging preferences
  • EV owner’s home energy usage patterns

Understanding all these variables in real time across a city’s grid enables a more affordable and reliable power supply without inconveniencing EV drivers. With the right power prices and incentives, this can be added incentive for making your next car an EV.



Situational Intelligence: Your Next Chief IoT Officer?


A recent Datamation blog post argues that gaining full value from the Internet of Things requires “a broadminded corporate vision, a radically new approach to product/service design, highly specialized technical skills, and fundamentally rethinking an organization’s go-to-market strategies.”

All this demands “a multidimensional perspective that spans the traditional corporate silos, and bridges the gap between business and technology.”

How are you supposed to achieve this multidimensional perspective that spans silos? According to Datamation, by appointing a Chief IoT Officer. This role would have three main functions:

  • Aligning technological components to corporate objectives
  • Capturing relevant data
  • Utilizing data to support operations and achieve corporate objectives

That sounds like a solid job description for a new C-suite role. But adding to the C-suite is a centralized approach, while the Internet of Things is a highly decentralized phenomena. Wouldn’t you want everyone in your organization working across silos, aligning technology to objectives, and capturing and applying relevant data to solve business problems?

If so, deriving value from the Internet of Things becomes a question of culture, not leadership.

Situational intelligence, by definition, correlates, analyzes and visualizes data from multiple data silos.

By making situational intelligence applications widely available across your organization, including in your boardroom, you can build a culture of spanning silos, applying data, and solving business problems. A new C-suite role doesn’t guarantee cultural change in your organization.