Real-time, agile data is crucial to reaching government targets to phase out the sale of fossil fuel-powered vehicles by 2030, argues CK Delta's Geoff McGrath.
The mass market rollout of Electric Vehicle (EV) technologies has been a central challenge in the debate on how we can reach our net zero emissions target by 2050 or sooner. Despite continued innovations in battery manufacturing and favourable market conditions making EVs more accessible to consumers, reaching our ambitious target to phase out the sale of fossil fuel-powered vehicles by 2030 will depend a great deal on several variables – including the provision of alternative fuel and transport sources, and the capacity of distribution network operators (DNOs) to accommodate shifting patterns of electricity consumption.
In broad policy terms, the road to 2030 requires us to break down barriers between important stakeholders, enhancing charge point accessibility for both fleet and consumer vehicles and invest in deprived neighbourhoods to avoid charging blackspots. This means adopting meaningful, data-driven models that are responsive, agile, and capable of delivering on ever-shifting consumer priorities. With the adoption of EVs so dependent on external factors, predictive analytics models combined with mobility and network data can help us to achieve these aims by better helping us map out risks and opportunities that are crucial to determining the success of the mass rollout of associated EV infrastructure.
Keeping accessibility at the heart of the charge point rollout
Mobility data allows for the planning and understanding of future travel scenarios by providing an insight into aggregated patterns of behaviour. Combined with predictive analytics, it can indicate key commuter routes in towns and cities and help pinpoint which areas are likely to experience a quicker EV uptake before the network is embedded.
Leveraging this data in real-time can help local authorities to rollout charging infrastructure with a near-complete view of satisfying end-user demand. The insight provided by predictive analytics can also assist cash-strapped councils with deploying EV charge points in areas likely to drive a positive return on investment. This data has already been proven capable of identifying which demographics are likeliest to invest in EVs (18–24-year-old white collar workers) and will be essential if the industry is serious about improving accessibility and public confidence in charge point provision.
Deploying real-time data modelling enabled by predictive analytics means that developers, local authorities and planning authorities, and estate managers can better understand and leverage usage information. This means they can generate smarter, more flexible planning cycles that can account for evolving trends such as reduced demand during off-peak hours. Such trends become more insightful when integrated with additional system data meaning that those responsible for demand planning can adopt a more holistic approach to catering for EV demand.
Monitoring and acting on consumer demand
We have seen similar success in the utilisation of real-time data modelling in shaping the preparedness of DNOs in the UK and improving the resilience of their low voltage (LV) electricity networks. By leveraging several operational data sets through innovative machine learning capabilities, new models can help generate estimated maximum load profiles for LV substations on DNO networks without relying on consumer smart meter data. By embracing the art of the prediction through quality data and machine learning, we can ensure that they are ready to meet consumer demand for new charge point infrastructure.
Widespread application of predictive analytics
From planning through to grid optimisation, predictive analytics will be a key tool in the UK’s mission to phase out the sale of fossil fuel-powered vehicles. To meet the 2030 deadline, we need a coordinated approach from different stakeholders to develop and deliver the right interventions during the development and operation of key assets. Making use of meaningful data and predictive modelling can ensure that we reach our target in an equitable, fair and sustainable manner.
Geoff McGrath is the managing director of data science business, CKDelta.