Industry
Energy, Resources & Utilities
Rebalance the energy equation
Highlights
- The utility industry is witnessing unprecedented demands while grappling with extreme climate changes, aging infrastructure, and workforce challenges.
- A modernization framework becomes necessary to derive actionable insights from the utility grids.
- Next-gen spatial information, in tandem with IoT, smart meters, cybersecurity solutions, and state-of-the-art communication infrastructure, can make utilities more resilient.
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The vision for utility companies
Traditional distribution networks transfer the centrally generated power in bulk from the transmission system to downstream consumers.
This conventional design is unable to support microgrid management and a coordinated ecosystem, leading to transmission and distribution (T&D) losses and challenging the grid's stability. Moreover, consumers demand energy from renewable resources, and regulatory authorities need utilities to upgrade technology, and meet decarbonization, green energy, and safety standard requirements.
Digital transformation is essential for a sustainable network, which is the vision for utility companies. It will help to operate the grid securely and enable companies to achieve business values, meet KPIs, ensure employee and IT security, and address business challenges.
Utilities benefit from implementing an active distribution system for analyzing the source of power from fossil fuels vs renewable energy resources. Utility companies encourage customers to run energy-intensive machines during off-peak period where power generated from renewable sources are available for consumption. A next-gen network which is an active distribution system will offer greater flexibility to switch the source by coordinating between the demand and supply across electricity network.
Getting on the smart grid bandwagon
In the last few decades, the utility industry has relied on geometric networks to model their real-world infrastructure.
These networks analyze resource flows for water, gas, and energy. As utilities jump onto the smart grid bandwagon, technologies such as IoT, smart meters, cybersecurity solutions, state-of-the-art communication infrastructure, and geospatial information play a crucial role in their modernization journey. With regulations becoming more stringent, utilities are left with little choice and are required to modernize their infrastructure. Companies look for new solutions that can be integrated with their existing legacy infrastructure, increasing the need for massive investments.
Similar is the case with the legacy geometric network model, which might soon be replaced by a highly scalable network model. Utility enterprises that run on the geometric network model must consider switching to a more scalable utility network model, which would enable spatial modeling of the various network elements, such as pipes, valves, circuits, devices, and the like, in utility-specific spatial systems like power, water, sewage, gas, and telecom. It also helps build digital twins of real-world behaviors of these components under normal and various extreme conditions while modeling the network grids.
The graphic elucidates the spatial utility network model, encompassing a connected network of assets from generation to meters. The next-generation network model supports the addition of distributed renewable energy resources to the network, defining micro-grids, decentralization of energy, and replacing analog with communicative and operatable meters, which can be added to any part of the connected network supporting advanced functions to improve resiliency, optimize power flows, and increase situational awareness with real-time sensing capabilities.
Figure 1
A conceptual view of a fully connected network model
Figure 1
The framework could be further scaled by extending the data model rather than appending it. Additionally, it can be designed to provide a single view of the generation, transmission, and distribution of assets with the status of the network for making better operational decisions in near real-time.
Use cases and scenarios
Implementing the utility network model on a GIS platform will be a transformation journey.
This requires a deep understanding of the utility domain data model and the proprietary applications accessing it.
Here are a few scenarios where the utility network model will bring a distinct advantage:
Visualizing substation data: In areas that face extreme weather conditions such as wildfires and hurricanes, utilities need to plan ahead and manage outages for public safety. In such cases, power restoration takes a long time and impacts the customer average interruption duration index (CAIDI).
Outages caused by the underground assets take longer to isolate and restore, as it is challenging to visualize and locate the damaged assets with the current GIS capabilities. An internal view of the substation with all the details of the connected units is required to proactively isolate circuits in the path of wildfire or hurricanes. It is also important to visualize the substation internals to trace, select, isolate, and repair the vulnerabilities in the network, which can further improve the system average interruption duration index (SAIDI) and system average interruption frequency index (SAIFI).
Visualizing underground ducts: To lay new underground cables effectively for quick power restoration during planned and unplanned outages, it is important to find the empty ducts in the duct bank. The model can help companies address new connection requests, prepare, and lay the cables, and energize them quickly.
Visualizing and managing the water grid in GIS: The utility network model can be integrated with IoT devices to visualize the actual status of a network. An entire water grid can be visualized in GIS with operations-critical readings and parameters for better decision-making. The latest status of the devices and pressure readings on the network can be seen in the model-enabled GIS system to locate the defective devices that need to be isolated from the grid and restore the grid remotely using supervisory control and data acquisition (SCADA).
Processes such as readiness assessment, subsequent data gap analysis, implementation recommendations, and the changes expected on the upstream or downstream system to attain the desired goals are key to modernization programs for making utilities future-ready.
Migrating to the new model
Implementing the utility network model would require significant effort from business, IT, and dependent system stakeholders.
Utilities will need to perform deep dive analysis of business workflows, applications, data, and tech architecture and the readiness to migrate to the utility network model through due diligence and thorough system analysis. They must realign business processes to take advantage of the current maturity of technology, which has replaced manual tasks, and identify workflow processes that are currently reactive but can be made proactive by leveraging next-gen spatial information in tandem with IoT data.
Paying close attention to the current data model and changes that must be implemented in the utility network model’s foundation will help customize it as per business needs. In addition, utilities can look for opportunities to automate tasks during migration, such as source-to-target mapping, data cleansing, and post-migration validation, to mention a few.
Taking these steps to modernize the core network model will help utilities unlock many advantages, such as:
- Next-generation network modeling: The model would provide a detailed and flexible framework for representing networks of all sizes and complexities, supporting various network features, devices, and subnetwork arrangements to suit the specific needs of different utilities.
- Robust network analysis: Accomplishing advanced tasks such as tracing, subnetwork analysis, and network switching analysis would be possible. This will enable the utility industry to better understand their networks and share up-to-date readings and status from IoT devices to make informed decisions about operations and maintenance.
- Data quality: The model can be designed to include features that can help the utility industry improve network data quality by making it more consistent and accurate.
