What is modern asset management?
Asset Management for the modern electrical grid-primarily consists of employing Asset Performance Management (APM) tools, which are specialized in taking in asset property data and historical asset information, which is then used to predict and model a range of calculations on the system.
This is often centered around their shelf life as aging assets can bring a host of problems if not dealt with in a timely manner. Modern-day asset management is often performed using a series of software tools to assess maintenance needs, predict remaining asset lifespan, as well as regular checks on existing infrastructure.
Climate change has brought with it a series of challenges surrounding proper asset management, many of which have a direct impact on assets on the power grid and strategies needed for effective asset management. Awesense employs a different approach to asset management, whereby real-time data is utilized to assess system performance, asset risk and understand where priorities should be focused.
Awesense provides advanced geospatial and grid-segmentation capabilities to visualize and analyze data from a geospatial perspective. Awesense’s capabilities complement that of an APM, where specific high-risk assets and portions of the grid can be monitored closely using a range of techniques. By utilizing real-time information as opposed to relying on predictive models, which can have a significant margin of error; this results in a further extension of general asset lifespan.
What problems and challenges arise from modern asset management?
A range of distribution network operators in the utility and public infrastructure sectors renew assets according to service life. Though often comprehensive enough in some cases, this approach can have its issues as some older equipment can outperform newer equipment.
Because of this, an older asset can often end up as a more reliable option, and its shelf life can extend beyond what is predicted. This is very important for distribution network operators- extending the lifespan of these assets is a direct way to cost saving for both utilities and customers in their service area.
In the energy industry, this facet of asset management is often rated on a Health Index. For an example of a more comprehensive publication surrounding the Health Index, the Office of Gas and Electricity Markets (OFGEM) in Great Britain has a guide used by distribution network operators in the region. Created by 6 distribution network operators, this document shows how each piece of equipment has a calculable value which allows for a series of interpretations.
These calculated values identify both the probability of failure of an aging asset, as well as the consequences of a failure if it were to occur. Together, these two values form a Risk Index. This Risk Index is used inversely as a measure of current asset health as well as a measure of forecasting for future needs.
Photo Source: Ofgem
Photo Source: Ofgem
How is data involved in asset management?
In the age of technology, data houses the key to addressing issues in many areas of the grid and is especially important in asset management. The more information a utility has about assets in the field, the better informed they can be when making decisions that account for assessing asset conditions. This includes both time series (measurement) and geospatial data, which should be stitched together and synchronized.
The best view of the grid is one that allows for granular insight into any individual component. By separating the grid into smaller pieces, a deeper understanding of asset performance on a localized level can be achieved. Though this is the standard, oftentimes a lack of data can result in incomprehensive analysis, meaning less accurate insights and assessments.
An example of this grid segmentation may look as such: segmenting the grid by High Voltage (HV), Medium Voltage (MV), and Low Voltage (LV) cables, or HV or MV substations. Then we can divide the segments by element type – poles, transformers, switchgear, etc. and approach each element type individually. Segmentation of the grid should be performed according to the level of detail which makes sense according to the type of asset management being performed. When the grid has been segmented and circuits have been created, each element type within those circuits can be color-coded or categorized and then analyzed. This allows for a per-element-type analysis, with a geographical emphasis, and each geographic region, or segment, can be ranked and tracked in order of priority according to its score. See figure 3 below for an example of grid segmentation.
Interested in how your utility can become more data-driven? Take a look at our article on how-to here.
What solutions address these asset management problems?
A good solution for asset management is one that is comprehensive and inclusive enough to coordinate and integrate all points of data into the decision-making process. The Awesense Energy Transition Platform can ingest all data sources, whether that is from DERs, EVSE, SCADA, AMI, or anything else, and from any cloud or historian host. This makes it easy to work with data, no matter what kind and where it comes from.
After ingesting, correcting, and connecting all of your data sources in the Awesense Energy Transition Platform, it allows utilities to create a customized calculation for each type of asset depending on the methodology of choice. One example is the OFGEM method mentioned above. This can be performed in the Awesense Platform itself, or in external tools which can be easily connected to the Awesense Energy Data Model.
TGI also provides flexible charting capabilities that allow users to visualize data according to their preferences. This includes maps, tables with filters, and calculated variables. This also helps utility operators understand grid topology because it is a native feature on the. This means that distribution network operators can easily identify failing assets, high-risk circuits in the grid, and overall gain a better understanding of real-time grid and asset performance.
FIGURE 3: A look at asset visualization on a map in TGI.
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- Cagle, R. F. (2003). “Infrastructure Asset Management: An Emerging Direction”. AACE International Transactions.
- “Piryonesi, S. M. (2019). The Application of Data Analytics to Asset Management: Deterioration and Climate Change Adaptation in Ontario Roads (Doctoral dissertation)”
- “The Institute of Asset Management (IAM): The IAM Competences Framework”