Charging Infrastructure for Electric Vehicles

Software solutions enable systematic planning and a demand-driven rollout of EV charging stations

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E-mobility is a key element of sustainable mobility. Political and industry leaders agree that the uptake of electric vehicles (EV) needs to speed up in the near future. EV can help to reduce air and noise pollution, making communities more livable. But where should all these new electric cars be charged? The rolling out of public charging infrastructure is still too slow.

An attractive network of charging stations will help accelerate the transition from gasoline- and diesel-powered cars, to the eco-friendlier EV. So how can the rollout of charging infrastructure be sped up? How can there be good return on investment in this infrastructure?

Cities, municipal utilities, as well as energy providers and private companies, face the major challenge of finding the optimal locations for charging stations and making the right investment decisions. The expected electricity demand also plays an important role. Software solutions help to plan charging infrastructure for EV in line with demand, while taking power grid capacities into account.

  • Systematic and cost-efficient planning of charging infrastructure
  • Demand-driven rollout of charging stations
  • Rapid evaluation of implementation costs
  • Quantitative forecast of electricity demand
  • Fine-scale planning of exact locations
  • Web-based & flexible 

How to find the best locations for charging stations?

Planning charging infrastructure for electric vehicles raises many questions: How many EV drivers are expected in a certain district or region? Are people going to charge their electric cars mostly at home, at work, or in the public domain? How many public chargers will be needed? What are the best locations? Which points of interest are nearby, to make the charging worthwhile?

To answer these questions, software and simulation models help to accurately forecast demand and make the right decisions for the best and most profitable locations.

What is the future demand?

The future demand for a charging station strongly depends on how people will live, work, and move in the future. So, a location analysis is based on data on mobility, demographics, and urban infrastructure. Software and transport models enable planners to examine various scenarios in a virtual environment, and forecast future developments and the need for EV charging infrastructure. This, for example, helps to evaluate how many fast chargers are needed compared to standard chargers, or how many charging points are generally required in every location. 

Does the power network meet the demand?

Another important factor for successful planning of an EV charging station, is the capacity of the power grid. Choosing the wrong location for a charger may result in high development costs due to necessary grid expansion. But small changes to the location can make a huge difference. Adding power grid data into a transport model enables integrated planning of charging infrastructure for electric vehicles. The spatially specific demand forecast can be matched with existing grid capacities in a granular manner. The resulting quantity structure helps to expand the charging infrastructure, in line with demand.

Interactive map shows best locations

The project “Charge” in the UK brings together transport and energy planning, in order to accelerate the investment and deployment of public EV charging infrastructure. The project includes an online tool, that was developed by overlaying a transport model in PTV Visum with electricity network capacity data. The interactive map provides planners, local authorities, and charging point installers with data to explore where public charging points will be needed, and where connections to the grid can be made.

Advantages of planning systems for charging infrastructure

  • Integrated planning of charging infrastructure by modeling mobility and the capacity of the power grid
  • Configurable ramp-up scenarios for electric mobility with variable assumptions on fleet size for different time horizons
  • Cost-efficient rollout by identifying the most suitable locations
  • Information on the dimensioning of charging locations: Number of charging points, fast/standard charging, etc.
  • Greatest benefit for customers, as planning is based on demand