Estimating EV uptake forecasts by understanding consumer behaviours and charging patterns will be an essential concept for DNSPs to grasp to better anticipate electricity demand.

From our discussions in Blog 1: Top Five Challenges Faced by DNSPs, it is clear that the rising demand from EVs will place pressure on the network and present new challenges for DNSPs.

DNSPs will need to understand and accurately forecast the EV loads and load profiles to ensure we are properly equipped for the future. Key factors to consider are peak demand periods, current methods, and the consequences to poor planning.

EVs are mobile resources that can offer demand flexibility services by either absorbing excess solar generation during the minimum demand periods or by helping networks manage the peak demands. Accurately identifying peak demand periods for EV charging will be crucial, especially given the potential risks of overloading the network. By collecting both qualitative and quantitative data, DNSPs will be equipped to devise strategies or incentives to potentially shift or reduce these periods, ensuring a smoother integration of EVs into our energy landscape.

Spotlight: Home vs Public Charging Behaviours

One of the key factors for DNSPs to account for is the distinction between home and public charging, due to the varying features they hold. Home charging, due to its linkage with daily household routines, tends to be more predictable. While there is more data on home charging patterns, making forecasts more reliable, they often exhibit longer charging durations with capacity limitations. Another layer to consider is the integration of solar energy, which can offset some of the charging loads directly from the network. In contrast, public charging, like at shopping centres, experiences more fluctuations. Such locations might only be active for half the day and can experience pronounced peak periods. To paint an accurate picture of EV load, DNSPs must account for these variances, understanding the nuances between different charging locations and station types.

Why accurate forecasting is essential: Diversity Factor

Accurate forecasting is essential, especially when considering the diversity factor in EV charging infrastructure. The diversity factor represents the actual utilisation rate against the maximum capacity, acknowledging that not all EVs will require charging at the same time. Think of a parking lot with multiple chargers: even if each has the potential to operate at maximum, in reality, only a portion might be used at any given moment. Similarly, not all EVs will be charged at home at the same time.  The amount of charge required will also depend on the EV adoption rate and travel patterns. Without a precise understanding of this factor, planning can either lead to underused resources or strained systems. Properly accounting for the diversity factor ensures optimal infrastructure use and grid reliability.

Conclusion

The consequences of inaccurate forecasts could lead to inefficient infrastructure usage if overestimated or a panic situation where the future demand cannot be kept up with. Current methods have found it difficult to achieve high accuracy forecasts due to both the lack of historical data available to develop more complex models and the number of random factors that contribute to charging behaviour.

EA Technology has developed VisNet | Connect, a cutting-edge software tool that integrates network capacity and demand to guide the installation of charge points. Keen to learn more? Check out our range of self-serve connection tools here.