Currently geospatial data and technology are used tactically by electric power utilities for many purposes including outage management, vegetation management, disaster management, renewable energy facility siting, universal electrification planning, asset management, and energy density mapping, to name just a few. But the smart grid is fundamentally transforming the role of geospatial data and technology in electric power utilities. According to a report from Navigant Research “The smart grid is all about situation awareness and effective anticipation of and response to events that might disrupt the performance of the power grid. Since spatial data underlies everything an electric utility does, GIS is the only foundational view that can potentially link every operational activity of an electric utility including design and construction, asset management, workforce management, and outage management as well as supervisory control and data acquisition (SCADA), distribution management systems (DMSs), renewables, and strategy planning.” In other words the smart grid makes geospatial strategic. Location is the basis on which data is organized and spatial analytics is how information is extracted from the data. As Bradley Williams has so succinctly put it, "spatial analytics is becoming a key technology for electric utilities because everything a utility does - customers, assets, and operations - involves location."
At the Future of Canada's Utilities Summit in Toronto I had the opportunity to chat with Alexander Bakulev, VP of Strategy and Assets at METSCO, in Mississisaug, Ontario about asset management, location and GIS, and the future of utility IT. Alex has deep experience with asset management at utilities in Ontario and as VP of Strategy he brings a forward-looking perspective to the changing role of location and GIS in asset management in electric power utilities.
Alex, what type of asset management service does METSCO provide to your customers ?
We provide a framework for asset management based on a methodology, risk-based asset management. When a decision needs to be made such as whether to replace an asset or maintain it and run it to failure, those decisions are made by engineers to mitigate the risk of failing as well as for safety and environmental reasons. We enable our customers to translate those risks into dollars so they can compare dollars to dollars and make an economic decision whether to replace or maintain an asset. Of course, this has to be done within a regulatory environment. In Ontario this means PSAB 3150 and ISO 55000.
Can you give a perspective on how advanced your customers are with respect to asset management ?
We have a maturity hierarchy that helps us understand where a utility company is in managing its assets. When we begin working with a customer, the first thing we look at is their asset data, what do they have, in what media, and where it resides. Companies have different maturity in term of the asset records they have and how they manage them. Usually what we see is that companies may have good inspection data, but the information is stored in a decentralized mode - paper documents, Excel spreadsheets, special lab-testing software files, and so on - but not in a centralized system. In many cases we have to compile paper documents and files to order to combine it all into a centralized asset management system. But we see that is changing. Companies are starting to invest in document management systems and capturing data digitally. They are also collecting data with mobile devices in a way that goes directly to the online ERP system. In addition newer equipment is smarter and able to report inline so it's a paperless process.
Risk of failure
Once you have the basic information about the condition of each asset, the next step is to try to predict each asset's failure probability. We want to understand what's the chance that they will fail next year, after 5 years, or 10 years. If the company has historical data on failures including the type of asset, when it failed and why it failed, we can use that information to statistically predict failures. To do this we first find data about which assets have been replaced. Then we try to determine the reasons why the assets were replaced. Often this information is even less available than condition information. With this information we can estimate the expected failure rate for existing assets.
Consequence of failure
Once you know the risk for every asset to fail, the next step is to look at the consequence of failure. We not only look at what is being fed downstream from an asset such as a transformer, but also at the protection schema on the feeder. For example, when a radial feeder fails, the result is a minimum of 4 hours outage. If it the feeder is a loop design, the outage may only be 30 minutes. If the feeder is automated, the outage could be seconds. And it may not be just power that customers lose, there could also be environmental and safety issues. For example, if a corner pole fails, the consequences are more serious than a pole in a street line. There are also financial factors. How fast do you need to replace the asset ? Do you keep it in stock or order it ? For expensive assets, it is very expensive to keep spares in a warehouse.
Once we know the condition of each asset, the probability that it will fail, and the consequence of failure, we can monetize the risk. With monetized risk, when we have to make a decision, we make an economic decision. Maybe it's better to run to failure, or increase the frequency of maintenance to watch the asset closer and accept the risk of failure for the next 5 years and after that replace it, or if it is high risk, replace the asset immediately.
It sounds like you can classify utility companies based on the data they have and how they use it to make decisions ?
We have a maturity model that helps us determine where a customer is so we can determine how best to help them.
Run to failure - Assets are only replaced when they fail. From the perspective of financial costs, this alternative looks good, but if you look at customer value, the cost is very high. There are many unplanned outages of relatively long duration.
Age-based - In this case assets are replaced based on the age of each asset. In this case investment requirements can be three times higher than run to failure, but the customer is benefiting by improved service. There are much fewer unplanned outages.
Condition-based - With this approach you only replace assets that are in poor condition. Investment requirements are less than age-based, but still quite high compared to run to failure. Customer value is similar to age-based.
Risk-based - For example, you may decide to run 70 % of assets to failure in those areas where there is low risk, but for the 30% of assets that are high risk, you try to avoid any failures. This requires focussed investments. Investment requirements are significantly lower than condition-based, but still higher than run to failure. But with this alternative, customer value is optimal within the budgeted investment level.
Where are most of your customers also in the maturity model when you first start working with them ?
When we first begin to work with them, most are somewhere between age-based and condition-based. Many are making age-based decisions, but are trying to move to condition-based asset management for many asset classes. Surprisingly there are some large companies that still follow a run to failure approach. We have customers that have gotten to the highest level, risk-based asset management, and we have helped them get there.
What size utility finds it easiest to do this ?
In our experience, mid-sized utilities - with half a million to a million meters - find this transition easiest. They are more flexible. For large utilities the transition is challenging because it they have to change what they have been doing for the past ten years, mostly age and condition-based asset management, and they have implemented large expensive systems to help them do that.
How does location and GIS fit onto this ?
Location and GIS are fundamental in several areas of asset management. First of all geospatial technology provides a communication tool. Most critically geospatial visualization is used to justify investments. It makes it possible to show stakeholders high risk areas. It makes it possible to show people recommended investments and where to invest.
Secondly, it helps to plan for the future. By visualizing changing demographics it helps identify what parts of a city require investment. For example, if in the next 10 years most of the work is going to be done in a particular rapidly growing area of the city, it can be planned for by creating a yard in the area, for example.
We also use GIS for core planning. When engineers are designing the electric power network for a new subdivision, they require very detailed information about existing electric power assets in and near the area. This information is collected from many different sources and visualized on a map. The engineers can look at the risk profile of every existing asset on the map and start planning which assets need to be replaced immediately, which in five or ten years, and where they need to install new equipment.
Another critical area for GIS is connectivity, Connectivity is about which customers are connected to which service point, which service points are downstream from each transformer, and which transformers are on each feeder. Connectivity information is essential because it enables utilities to know who is affected by a planned or unplanned outage. It also provides a link between customers and infrastructure for financial planning purposes. Many utilities don't have reliable connectivity information or they think they do but really don't. GIS is the primary tool that we use to establish or correct connectivity.
Another critical area that is growing in importance for GIS is spatial analytics. GIS can help estimate the condition of assets and identify high risk assets. For a simple example, in a tabular ERP system, all poles are the same, but in the GIS we can see some poles are corner poles which are higher risk. As another example, if an asset is near a highway which is salted in winter, this can affect the condition of the asset. Here we are getting into predictive analytics where we can make estimates based on proximity to things that may affect the expected lifetime of assets. In this case assets near roads with salt in winter are at higher risk.
What's your perspective on the future for utility asset management ?
Many companies are beginning to see the advantage of the mapping environment for asset management. In the past asset management was pretty much done with Excel or similar table-based tools. What we are seeing now is that increasingly decisions are being made based on maps, increasingly interactive maps. There are several drivers for this.
The first driver is simplification. Asset management is simpler when you put everything on a map. And for the new generation of engineers, maps facilitate knowledge transfer - it 's easier for them to design, manage and maintain the grid with assets visulaized on a map.
The other driver is grid complexity. With the smart grid and distributed energy generation, the grid is getting much more complicated. There is a lot more data that needs to be analyzed. Thefe are also increasingly complex reporting requirements from the regulator. There should be one environment that can handle all of this - data management, analytics, and regulatory reporting. Maps help alleviate complexity and achieve simplification.
Do you foresee in the future that utility applications such as outage management, customer information system, crew management and dispatch systems, and others will be best of breed or an integrated solution from a single vendor ?
The future as I see it is all about map-based decisions - where are the outages, where are our marketing opportunities, where are assets likely to fail, where do our employees live, where are the customers that have lost power, where should we position crews in preparation for a storm ? To enable this to happen, I believe the future for utility IT is a map-based platform which applications like asset management, design, outage management, and disaster management from different vendors can plug into and share information in a shared geospatial environment.