Many utility companies have realized the importance of building a ‘Digital Twin,’ or digital representation of their electrical grid with accurate utility data. It requires high-quality GIS connectivity and topology, and accuracy is vital for planning, forecasting, and monitoring. This blog will explore GIS connectivity and why data accuracy is essential for utilities.
First of all, what is GIS Connectivity?
Connectivity relates to the connections between elements in the grid. For example, distribution lines are connected to transformers, transformers to meters, lines between each other, substations to feeders, etc. Everything in the grid is connected, but the connections are often poorly reflected in the utility’s data, also known as the “as-built vs. as-designed” problem. This results from years of under-performed documentation and lack of consistency when making changes.
Low connectivity and GIS accuracy don’t precisely make it impossible for operators and planners to understand the correct energy flow in their grid, but the added complexity of having lower accuracy certainly does not make this difficult task any easier. This complicates forecasting and significantly restricts the effectiveness of DERMS (Distributed Energy Resource Management Systems) and ADMS (Advanced Distribution Management Systems). These advanced systems require a very accurate digital representation of the grid to function optimally. Furthermore, as the challenges to the grid surmount with ever-changing demands and needs, the regulatory approval process demands more dynamic forecasting for regulatory approval. An accurate connectivity model helps planning engineers run more permutations on models with higher confidence that these scenarios are viable for future grid states.
What are specific problems that utilities may face with inaccurate connectivity?
- Inaccurate Network Modeling: Topology and connectivity errors can lead to false representations of the utility’s network infrastructure. This can result in incorrect or unreliable network models, making network analysis, planning, and optimizing operations challenging.
- Service Disruptions: Errors can cause service disruptions and outages. This can prolong the duration of outages and increase customer dissatisfaction. If the network’s actual topology is not accurately reflected in the data, it becomes difficult to identify and locate faults or perform effective maintenance.
- Inefficient Network Cost Management: Utilities rely on accurate data to plan and expand their networks efficiently. Topology and connectivity errors can lead to incorrect assumptions about network capacity and load distribution. As a result, utilities may make suboptimal investment decisions, leading to inefficiencies and unnecessary costs.
- Safety Concerns: Utilities must adhere to safety regulations and standards to ensure the well-being of their employees and the public. Inaccurate topology and connectivity data can compromise safety measures. For example, incorrect network diagrams can mislead technicians during maintenance or repair activities, potentially leading to accidents or unsafe working conditions.
- Asset Management Challenges: Errors can undermine asset management processes, such as asset tracking, maintenance scheduling, and inventory management. This can result in inefficiencies, increased maintenance costs, and difficulties managing the equipment lifecycle.
- Data Integration and Interoperability Issues: Utilities often must integrate data from different systems and sources. Topology and connectivity errors can make data integration challenging, leading to inconsistencies and data quality problems across various applications. It can hinder interoperability and prevent utilities from effectively leveraging advanced analytics and automation.
So now the question is, how do utilities mitigate connectivity errors?
In the past, connectivity errors were verified with “truck rolls,” where a utility employee drives to an asset to verify its coordinates manually. As one can imagine, this process is time-consuming and expensive. This is also known as “walking the lines,” whereby the utility puts forth, often a multi-year effort, to verify its network by physically inspecting all elements and connections across its service territory. There is, however, another way to rapidly increase the accuracy and prioritize areas requiring physical inspection. The Awesense Energy Transition Platform boasts a powerful AI-driven Engine that can ingest any grid data sources to validate, edit and estimate the data, with the output being a structured and synchronized model that serves as a digital twin of the grid. The AI Data Engine can ingest GIS data and analyze the connectivity and topology for errors is a crucial functional set of algorithms that can process in hours vs. months or years of walking the lines. It can also process time-series data to provide trustworthy contextual insight into the grid, which is invaluable for utilities. It can be set up to run continuously for persistent accuracy.
Here is an example of the types of errors the AI Data Engine can cleanse for:
- Coordinates validation and transformation
- Connectivity validation, estimation, and correction
- Meter association validation
- Switch state validation and correction
- Time series data validation and synchronization
- Missing time series data estimation
As Christophe Guille & Stephan Zach, Bain & Co. state: “Cleaning up data is a major challenge, requiring painstaking, time-consuming work to rationalize what is frequently a haphazard collection of systems and restructuring them along common lines so [utilities] can share and effectively use the data at hand.”
The Energy Transition Platform empowers utilities with a tool to identify and solve their data quality issues quickly. By providing automated monitoring and updating of the system data, they can be confident that critical decisions are correct.
We are passionate about the energy transition and believe the path forward is digitalization. We have developed a platform to help utilities digitalize and unlock the power of data. Contact us directly for more information on our AI Data Engine.