With EV Targets Growing Closer, Operators Must Remember that Network Data Sits at the Core of Success
As electric vehicle targets accelerate globally, utilities are racing against time to adapt – and data sits at the heart of every successful transition. Governments worldwide are focusing on moving to electric vehicles (EVs) as soon as they can to contribute to broader net zero carbon goals, and in the US specifically, the Biden-Harris administration previously set a target for 56% of all new US vehicles sold to be electric by 2032.
Utilities operators therefore need to be prepared for this growing and emerging area of demand, while also ensuring that the source of energy does not negate the environmental benefits of EV adoption. As utilities prepare for the surge in EVs, accurate data on grid infrastructure, capacity and network performance will be essential for preventing outages, responding to unplanned outages and ensuring grid resilience. This necessitates significant upgrades to the aging infrastructure to support the influx of new technology.
The urgency of these upgrades cannot be overstated. As EV adoption grows, utilities face increasing challenges, such as local grid strain from home and public charging stations, especially during peak hours. Accurate, real-time information about the state of physical network infrastructure plays a critical role in forecasting and managing load across the grid. Without this data, utilities are at risk of overloading sections, leading to service interruptions and costly repairs.
As utilities strive to modernize the grid to support EVs, they must prioritize the integrity and accuracy of their network data to ensure that they can efficiently and effectively manage grid upgrades. Creating and maintaining an accurate geospatial digital twin of the rapidly evolving grid infrastructure is a fundamental building block to enabling the grids of the future.
The growing complexity of the grid
The steady move to EVs means that the demand on the grid is increasing, and the volume of work for network operators is growing as a result. Teams need to be able to deliver even faster, and with the same or reduced level of resources.
Operators are therefore relying more on automation across every aspect of their operations to ensure that tasks are handled as quickly and accurately as possible. As such, an already complex network is becoming even more complex. For instance, automated demand-response systems can adjust charging schedules during peak hours, ensuring grid stability. Predictive maintenance algorithms, fueled by real-time data, help utilities address issues before they cause service interruptions. Utilities like PG&E in California are already implementing automation tools such as automated demand-response systems to dynamically adjust charging schedules during peak hours, reducing strain and preventing overloads.
Critically, as reliance on automation increases, so does the need for highly accurate, real-time data about the state of the grid infrastructure. As more work takes place in the field, the data needs to be collated from an ever-increasing number of sources of growing complexity which means that processes of the past - such as relying on paper records that are manually updated – are no longer fit for purpose.
Automation can't rely on data that is perpetually outdated. By digitizing workflows and integrating mobile solutions, utilities operators can ensure the synchronization of physical network data between field operations and central systems. This can significantly improve field team efficiency while also ensuring the accuracy of physical network data, which is critical for successful grid management and resilience.
Making data management the bedrock of meeting EV demands
To unlock this productivity, network operators must focus on the process of digitization to ensure their automation is built on accurate network information.
A delay between data collection in the field - such as staking, inspection or surveys - and updates in back-end systems prevents timely automation, creating process disconnects which hinder efforts to standardize, streamline, and accelerate processes. If data flow and management aren't prioritized, these larger initiatives will be adversely affected.
Implementing integrated data management systems that allow both field operators and office teams to access and update network data in real time from multiple touchpoints, can vastly improve the accuracy of digital twin models. This not only streamlines operations and supports regulatory compliance, but when systems are built on a comprehensive network model and data foundation, processes are streamlined and connected, better supporting the rising energy demand driven by decarbonization and emerging technological advancements.
For decades the utility industry has been a shining example of a sector that has navigated unprecedented complexity to provide an essential service to many far and wide. With ambitious net-zero goals being shared globally, utility operators need to ensure that their people, processes, and systems are prepared for the required transformation.
Many have already started to move towards digitization, even looking to predictive analytics and machine learning to manage grid demands and improve reliability, but the integration of numerous sensors and automation tools can lead to an overwhelming volume of data that traditional systems may struggle to process effectively, or there can be difficulties when integrating with legacy systems. Furthermore, when it comes to the industry, systems need to remain functional while they are being updated – it’s like a pilot rebuilding an airplane while trying to fly it at the same time.
By placing a clear focus on capturing and maintaining an accurate network model with integrated task management, operators can enhance productivity and make more informed decisions about grid performance. This focus not only improves day-to-day field operations but also ensures that upgrades and long-term objectives are achieved efficiently.
