Predicting On-Street Vehicle Recharging Infrastructure Requirements: The OPOSRI Tool

Sales of EVs (full-electric and plug-in hybrids) are growing rapidly. In the UK there are now more than 80,000 plug-in electric cars and vans on the UK’s roads, with an increasing number of people prepared to make the change. Policy agendas to fully decarbonise transport by 2050, together with ongoing focus on climate change and air quality, continue to drive forward research, innovation and investment in the development and uptake of these new vehicle technologies.

National initiatives such as the Department for Transport’s Go Ultra Low City scheme and Transport Scotland’s ChargePlace Scotland roadmap and grants to support individuals and businesses to purchase EVs and install chargers, have served to encourage this growth.

As the number of EVs increases, there is growing discussion around the number and location of charging points. In response, there is increasing interest in predicting the demand for on-street recharging infrastructure. Effective EV charge point planning is essential to ensure that future demand is met cost effectively, steering a path between resource-wasting over-provision in some areas and EV ownership/use-suppressing under-provision in others. This need for demand forecasting has encouraged SYSTRA-JMP and MVA Hong Kong to develop a tiered planning methodology for use in different geographic regions. The approach combines strategic planning, economic advisory services, local charge point planning, and site design

The Optimising the Provision of On-Street Recharging Infrastructure (OPOSRI1) tool allows the user to determine the optimum number and type of EV chargers by location and journey type. The tool automatically simulates how well different infrastructure scenarios meet the predicted future-year demand for on-street recharging. Travel demand is derived from existing transport models and the tool takes account of the likely future demand for on-street recharging; location of charging points; number of chargers at each point; number of parking bays each charger serves and the duration of both the re-charge and the trip itself. It also takes into account a number of uncertainties by allowing decision makers to evaluate the impact of variations and sensitivity of different parameters, such as battery capacity.

The main benefit of the OPOSRI tool is to support the decision-making process around the optimum EV charging infrastructure strategy for a given level of budget, by allowing decision makers to optimise their investment in EV recharging infrastructure. From the end user’s perspective, the tool supports the provision of cost-effective accessible, convenient and reliable re-charging, helping to encourage EV adoption.

More specifically, the outputs of the tool provide an understanding of:

• The location and charging speed which minimises the level of unmet demand for a given level of capital investment; and/or
• The level of investment required to achieve a given level of service in different future years.
• This in turn provides a basis to provide recommendations on the number, type and location of additional charging points and the associated budget requirements, in order to:
• Support decision makers when planning where to install recharging stations;
• Identify the most cost-effective number and type of rechargers at each location;
• Allow decision makers to visualise which overall investment strategies are efficient and which are not; and identify the optimal recharging infrastructure for a given level of investment.

In conclusion, as the demand for electric vehicles continues to increase, we need to assess the optimal infrastructure requirements to make such growth more sustainable. The main benefit of the OPOSRI tool is to support the decision-making process around the optimum EV charging infrastructure strategy for a given level of budget, in order to help provide a cost-effective network of accessible recharging infrastructure for the end user. This can in turn be expected to help encourage EV adoption with potential impact on reducing greenhouse gas emissions and local air quality pollutants.