Network data: The key to success as electric vehicle targets approach

As EVs are increasingly adopted, utilities face more and more challenges, including immense pressure on local grids. To cope with this, strategies to manage charging, such as time-of-use tariffs and smart charging, will need to be implemented, writes Adrian McNulty, vice president of Utility Solutions at IQGeo.

Electric vehicle (EV) targets are accelerating globally, and utilities are racing to stay ahead. Globally, governments are prioritising the shift to EVs as part of their efforts to meet net zero carbon objectives. In the US, the previous Biden-Harris administration set a target for 56% of all new vehicles sold to be electric by 2032.

As a result, the US electric vehicle market is expected to show an annual growth rate of 10.49%, which would result in a projected market volume of $156.3bn by 2029. This surge in demand is driven by both government incentives and increasing environmental consciousness among consumers. Meanwhile, in the UK, the government has set a mandate that all new cars and vans must be zero-emission by 2035, accelerating the demand for electric vehicles.

These targets will create significant demand that utility operators must be ready to handle, whilst ensuring that the energy used to power electric vehicles does not undermine the environmental advantages of their adoption.

As utilities prepare for the increase in EVs, accurate data on grid infrastructure, capacity and network performance will be crucial for preventing outages, responding to unplanned disruption, and ensuring grid resilience. Quality grid data sits at the heart of every successful transition, playing a vital role in adaption. As a result, significant upgrades to the current infrastructure are needed to support the influx of new technology, and they are needed urgently.

As EVs are increasingly adopted, utilities face more and more challenges, including pressure on local grids from both home and public charging points, particularly during peak hours. To cope with this, strategies to manage charging, such as time-of-use tariffs and smart-charging, will need to be implemented. Without proper planning, substations risk becoming bottlenecks for energy distribution.

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To forecast and manage the load, accurate, real-time information about the state of physical network infrastructure on the grid is critical. For example, accurate load forecasting depends on having relevant data on energy consumption trends, grid capacity and the operational status of individual substations. Without this data, utilities risk overloading sections, causing costly repairs and service interruptions. This happened in Texas in 2021, when data issues caused grid mismanagement during peak demand, leading to widespread outages.

It’s essential that utilities enhance their ability to manage physical grid infrastructure, as this is the route to accurately forecasting electricity demand. With improved management, utilities can balance energy supply with the surging demand from millions of new EVs joining the grid.

As utilities accommodate EVs, they must prioritise the reliability and precision of their data to ensure that upgrades are implemented efficiently and effectively.

The increasing complexity of an already complex grid

The rise in the uptake of EVs means that demand on the grid is rising, and as a result, the volume of work for network operators is increasing. Teams need to deliver even faster, with either the same or reduced resources. Utilities operators therefore require automation across every aspect of their operations to maintain that tasks are handled with as much efficiency and accuracy as possible. This is heightening the complexity of an already complex network.

Automated demand-response systems can adjust charging schedules during peak hours, which maintains grid stability. Meanwhile, predictive maintenance algorithms, supported by real-time data, help utilities address issues before they interrupt the service. Utilities such as National Grid in the UK and PG&E in California are already adopting automation technologies, including automated demand-response systems, to actively manage charging schedules during peak periods, easing pressure on the grid and preventing overloads.

As reliance on automation continues to rise, so does the necessity for highly accurate, real-time data about the current state of the grid infrastructure. With the increase of fieldwork, data must be gathered from a variety of sources, each becoming more complex. This makes traditional methods, such as manually updated paper records and spreadsheets, increasingly insufficient.

Automation can’t be built on outdated network data. Digital work execution with integrated mobile solutions is essential for utility operators to ensure the synchronisation of physical network data between field operations and central systems. This can enhance the efficiency of field teams while ensuring the accuracy of physical network data, which is fundamental for effective grid management and resilience.

Meeting EV demands with efficient data management

To reap the rewards of automation, network operators must focus on digital work execution to ensure that it’s built on accurate network information. Any delay between data collection in the field and updates in back-end systems hinders efforts to standardise, streamline and accelerate processes, preventing efficient automation. Data flow and management must be prioritised, or larger utility initiatives will be adversely impacted. If staking, inspection or surveys are not recorded in real-time, automation and network data quality can be compromised.

Adopting an integrated network data management solution that enables real-time access and updates of network data from various touchpoints, for both field operators and office teams, can enhance the precision of digital twin models. This streamlines operations and supports regulatory compliance.

When systems are built on an accurate and comprehensive network model, automation becomes a powerful tool to better support the rising energy demand driven by emerging technological advancements and decarbonisation. Operations are also future-proofed by enabling data input from many decentralised points – ready for a time when more drones, robots and satellites are used.

As ambitious net-zero goals are shared across the world, the utility industry needs to ensure that its people, processes and systems are prepared for the change. For decades, the industry has been at the forefront of innovation, navigating complexity to provide critical national infrastructure. Now, it needs to take the next step and continue providing this service with energy efficiency at its core.

It’s vital that electricity grids remain functional while they are being updated, as they provide a key service to millions worldwide. Much like a pilot rebuilding an aeroplane mid-flight, utilities operators must update and enhance their network while remaining functional, which comes with its own challenges.

Many have already begun moving towards digitisation, exploring predictive analytics and machine learning to manage grid demands and enhance reliability. However, the integration of numerous sensors and automation tools can generate an overwhelming volume of data, which traditional systems may struggle to process effectively, or it can create challenges when trying to integrate with legacy systems.

By focusing on the management and accurate flow of physical network data, operators can enhance productivity and make more informed decisions about grid modernisation and performance. This improves day-to-day field operations and ensures that upgrades and long-term resilience objectives are achieved efficiently. Providing real-time, accurate grid management solutions that boost automation efficiency and accommodate the growing demand from EVs is crucial for utilities operators to quickly adapt to the changing industry landscape.