Geospatial Digital Twins
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- Jul 22, 2019 3:00 pm GMT
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There is much discussion in the literature about digital twins. Technology advancements make the development of digital twins much more possible. With robotics, big data analytics, machine learning, etc., it is possible from both a technology and a cost standpoint to develop a twin of a system. Building a virtual model of a physical asset allows scenario planning and testing against data collected by the ever-increasing sensors available today. With IoT, IoB, edge computing and new devices being developed and operationalized for all types of data collection, digital twins are within reach of most industries.
My interest lies with geospatial digital twins. This is the addition of geospatial information and connections to the models. Understand where an asset is critical but it is also important to know when the data was collected and what conditions existed at that time and place. To fully build a digital twin that is geospatially aware, it has to be refined through analyzing the volume of data being collected and that data has to be integrated via geospatial connectedness. Building and leveraging such digital twins will be transformational.
Almost every industry has data or assets that are influenced by location. For some industries, location is more critical. For electric utilities, modeling their assets based on the environment they are located is extremely critical. While the foundational data of the utility system in many cases is built via a GIS, in most cases it is out of as sync with the actual live system. However, by linking the core mapping data with sensors, IoT, LiDAR and satellite data, utilities can realize the potential of geospatial digital twins.
Electrical Utility Digital Twins
For many years, utilities have automated their utility networks from paper maps. The building, managing and storing assets in a geospatial format makes viewing and mapping utility networks easier. However, with new technology, the true digital twin can be created to model all the vast amount of data influencing a network. Weather, slope, vegetation, construction, and more can now be accurately included in and used for scenario planning.
Aging infrastructure, population growth, and climate change is putting a tremendous burden on existing electrical systems. Satellites and other geospatial technology, however, allows us to gather information about a location faster and with greater accuracy. Geospatial digital twins let us build an accurate model of the network that includes the environmental factors that affect operations. Building out and testing real-world scenarios can help discover hidden weakness before a problem occurs.
With budgets and time constraints, utilities need to prioritize replacement/upgrade plans taking into account affected customers, costs and communities. Understanding place/location and how different elements affect each other is critical. We are able to collect data from sources unheard of before, now we must use the data to build a stronger and more resilient network.
Geospatial digital twins will help us operate better today with smarter utility networks, emergency response plan, and hardening plans. They will also enable systems that are designed for the future, that embrace distributed generation and empower smart cities.
What is Next?
The good news is that core geospatial infrastructure data is being automated and managed by large organizations in GIS implementations. This often-hidden source of base data is a gem for IT departments looking for ways to build smarter models or digital twins of their systems. Locked away in GIS departments and in proprietary structured databases, these geospatial data need to be transformed into useful information. Integrating geospatial GIS data with big data from sensors and other devices will allow a powerful understanding of the system. From that understanding, intelligent action can be taken. When that happens, we will begin to see Smart Utilities, Smart Energy and unimagined other Smart (geospatial) systems.