By Angelo Fiumara, Head of Digital Asset Integration, Essential Energy
Essential Energy is an electricity distributor (sometimes referred to as a distribution grid service provider or DNSP) responsible for operating and maintenance of the electricity distribution network supplying 95 percent of New South Wales and parts of southern Queensland. The company is using a digital twin to improve its asset management and operational practices, freeing up ways of working that were not possible before. By building a digital model of the network and its surroundings, a lot of information that was not practical due to the manual and laborious calculations involved, is now easily accessible, as assets can be modeled accurately to replicate how they will function. in reality and will interact with the real world.
Essential Energy has more than 870,000 customers and manages more than 183,000 km of power lines. This very low customer-to-asset ratio means that it is essential that Essential Energy finds ways of working that are not only industry best practice, but are also agile enough to continuously improve and deliver value to customers. long term to its customers.
While there are many definitions of digital asset management, at Essential Energy digital asset management refers to the use of digital data sources to provide insight into asset management and operational efficiencies.
It encompasses a multitude of techniques that harness rich sources of intelligence that, at its most basic level, involve the use of digital images to determine what assets exist and what state they are in.
More advanced applications include building an accurate digital network twin that is supported by a technical grade mechanical and electrical analysis engine to provide information across the entire network.
Grid-scale risk and resilience assessments as well as grid compatibility for connecting renewable energy sources are some of the applications made possible by this modeling capability.
The development of the digital twin that can be applied at scale is seen as a core capability required to effectively manage one of the largest and most geographically dispersed distribution networks in the world.
Harnessing the power of big data for the development of digital twins
The journey of Essential Energy’s digital twin began in 2014, when Essential Energy began performing light sensing and telemetry (LiDAR) analysis of its service area for the purposes of]compliance reporting.
Acquiring this data laid the foundation for Essential Energy to improve its grid design function. LiDAR data, once ingested into a technological solution used for network design, enabled the automatic creation of Building Information Model (BIM) objects.
These BIM objects mimic how the network is actually built in the real world, creating a 3D digital twin of the âas builtâ network.
This precise âas-builtâ recording provided by the digital twin had never been available before. This automatic creation of digital twins for portions of the network dramatically improved the efficiency of the network design process, which began the transition from 2D construction plans to BIM-based 3D network designs.
The use of automatically generated 3D digital models of the network has also enabled rapid production of a compliant and consistent approach to network designs. It has also raised the skill levels of network designers and accredited service providers who design new connections based on digital twin objects on behalf of clients connecting to the network.
One of the key elements of designing with BIM objects is the implementation of a built-in compliance check. This enables consistent, accurate and timely design reviews for designs produced in-house and externally, creating high levels of compliance with Australian standards and Essential Energy guidelines, and reducing technical certification review times by up to 50%.
This digital twin network design application in turn provided the foundation for Essential Energy to extend its application to the entire network to support asset management and operations.
The Three Layers of Digital Asset Management Capability
Essential Energy considers three key capacity levels when considering digital asset management:
â¢ Digital data sources
â¢ A platform to visualize the digital twin and related asset data
â¢ Applications that use digital data, the digital twin to provide information on asset management
The visualization platform provides the means by which the digital twin and other digital data and content can be linked and digitally animated to show the network and its surroundings.
This allows all assets and contextual information to be associated with a digital twin asset and, for such a geographically dispersed network, enables ‘virtual site visits’ and reduced travel using a precise digital desk
The availability of rich digital datasets, the development of a configurable digital twin, and the visualization platform form the basis of what Essential Energy considers the third and most powerful layer of digital asset management capabilities. – digital asset management applications.
These applications are targeted at specific asset management needs. The results can then be used to activate other asset management applications and fed back to the visualization platform as a record of how the result was derived from digital imaging.
For example, digital asset imagery combined with the digital model can be used to determine information about the attributes and conditions of the assets.
Likewise, applications can use the digital twin and its configurable mechanical analysis engine to provide information such as ‘as-built’ conductor voltages and the mechanical load on utility poles that can be used to inform decisions about construction. reinforcement or replacement of posts.
The results provided by these applications can also be fed back to the visualization platform so that asset and operational managers are able to see the basis of what contributed to the modeled overview.
An active-by-asset approach to understand the health and performance of the network
Some powerful but fundamental use cases are already evident for the application of a digital twin at scale to improve asset management outcomes.
In the power distribution industry, many asset health assessment processes rely on the application of rule-based approaches rather than specific approaches for specific assets.
A tailor-made approach to specific assets is made possible through the modeling of digital twins. Using the actual measured mechanical load on assets in place of assumed values ââis a key potential area for a digital twin with a mechanical ‘scan engine’ at its core.
For Essential Energy’s assets, the decision to replace or reinforce wood poles is largely based on evaluating the mechanical load of the poles and the remaining strength required in the pole to safely withstand that load. .
Using the digital twin to determine the actual mechanical load on utility poles can better inform the asset decision-making process.
Likewise, mechanical loads can be derived from the digital twin to impact the shift from reactive to scheduled maintenance. This change – when applied to power line components such as poles and ties – is made possible by using the digital twin to determine the
mechanical loading on each asset.
The mechanical load on these pole and tie assets is determined primarily by evaluating the impact of wind forces on the conductors, which means that the combination of known conductor voltages, historical weather conditions and information on asset condition has significant potential to predict asset failures and assess overall network resilience.
Key visibility and flexibility for a sustainable energy future
The applications of digital twins in electrical distribution go beyond âstaticâ mechanical modeling. The growth of on-board generation customer connections and the growing role of the grid in managing complex energy flows mean that there is a growing need to understand how its performance and capacity evolves over the course of the day.
Adding real-world sensory data to a static digital twin helps determine the network’s true ability to handle highly variable energy flows. The introduction of this sensory data into the mechanical analysis engine of a digital twin would provide precise assessments of the electrical characteristics.
Adding an electrical model of the grid in the digital twin means that the overall impact of new connections – including intermittent renewable energy sources – can be fully modeled to make maximum use of grid capacity.
This future of new and intermittent renewable energies connected to the grid is a future that must be prepared so that customers continue to benefit from a grid that constitutes an energy exchange platform.
As technology improves and becomes more cost effective, the grid must be able to safely and reliably handle complex energy flows, which a mature digital twin can support.