Risk for unmatched geographic infrastructure needs
As electric vehicles (EVs) become essential to our efforts towards going green, charging infrastructure must evolve to keep up. However, the current energy demand and supply patterns for EVs don’t match the locations of existing refueling stations.
Those involved in building infrastructure—such as charging point operators, fleet operators, grid operators, and real-estate developers—need new methods and tools to minimize costs and investment risks, making large-scale EV adoption easier and accelerating the green transition of the transport sector.
The reason is five complex and uncertain factors which makes the roll out of the well needed infrastructure a long term challenges
5 complex and uncertain factors
All these factors contribute to the uncertainty surrounding the planning and rollout of electric vehicle charging infrastructure.
1. New charging habits evolve with technology
Current charging habits at homes, depots, terminals, and destinations are shaped by today’s technology, but these habits may change suddenly as technology evolves. For example, new battery technologies could provide longer driving ranges and shorter charging times, while options like dynamic charging or battery swapping could further change how we charge our electric vehicles. All of these factors would influence when and where we need charging infrastructure.
2. Grid transmission capacity greatly varies.
On the energy supply side, grid transmission capacity’s availability, cost, and development time vary greatly by location and time. These factors aren’t known globally and can be quite high, which adds to the challenge of setting up new charging stations. Plus, the grid capacity situation is closely related to existing networks, which further complicates matters.
3. Geographic mismatch between existing tank stops and tomorrow’s emobility needs.
Today’s refueling stations were designed for the needs of fossil fuel-powered vehicles and don’t line up well with the requirements of electric cars. Adapting them for use with electric vehicles is often hindered by the limited availability, high cost, and long development times associated with suitable new real estate and services.
4. Better user experience is a priority to speed up EV adoption.
Apart from purchasing and operating costs, the adoption of electric vehicles is greatly influenced by how easy it is for drivers to find and use charging stations.
5. Influence of government policies, regulations, and competition
Government policies, regulations, and subsidies can guide the development of charging infrastructure up to a point, but the industry is mainly driven by competition among various companies who often have conflicting business goals.
All these factors contribute to the uncertainty surrounding the planning and rollout of electric vehicle charging infrastructure.
Optimizing for the future
Numerous studies have shown that our gut feelings and common sense often fall short when navigating the complexities. People usually overlook the unpredictable nature of these systems and the vital way that different transportation routes can affect how electric vehicles are used. These oversights can lead to development that’s not as efficient as it could be, wasted resources, hesitant investment, and a slower shift towards electric vehicles.
Gordian is developed to help overcome these challenges by having a adaptive planing approach.
The Adaptive Planning Approach: Dynamic and Data-Driven
A new approach, “dynamic adaptive planning,” along with a data-driven Spatial Decision Support System (SDSS), aims to address these infrastructure challenges.
Digital twin
This method uses a digital twin of an electrified road transport system to enable infrastructure developers to estimate essential performance measures, like charging demand and EV enablement, for potential charging locations or networks. Infrastructure planners and business developers can adjust and optimize network plans as more information about factors like grid capacity, access to real estate, and competition becomes available.
Data layers with up-to-date information
The adaptive planning tool incorporates a variety of data layers to make planning easier. These layers include up-to-date information on existing charging stations, truck stops with amenities, commercial logistics patterns such as trucks’ short and long stops, and where available power grid lines and regional transformers and cost estimates for connecting to the grid.
The tool also accounts for restrictions and priority areas for subsidies, as well as hundreds of millions of simulated yearly freight and long-distance private car trips across 26 European countries, based on the ETIS Plus project.
Collaborative & competitive planning
Expectations are that this approach could be applied in collaborative planning practices to efficiently allocate charging infrastructure subsidies, further expanding the reach of EVs.
It is also powerful decision support in competitive settings, gaining insights into competitors’ performance and how your plans can affect them. It can be used to calculate and maximize market share, etc.
Adaptability & responsiveness
The key to this system’s success is its adaptability and responsiveness to changing energy demands and technological advancements. It combines data, analytics, visualizations, models, and simulations to support decisions that fulfill multiple criteria while allowing users to adjust specific preferences.
Charging Network Demand Concepts
The adaptive planning tool is built around several essential concepts and methods related to road network demand for charging. This includes the route catchment of the station that allows the assessment of demand for individual charging stations, combined demand for an entire charging network, demand specifically from fully electric vehicles, and total network demand.
The system also considers losses due to competition between stations and the impact on CO2 emissions when charging at each station. There’s also an emphasis on how adding new charging stations to the network increases the number of fully electric routes and transport work, ton-km (Tkm) that can be covered by electric routes.
Evaluation Framework: Multi Criteria Evaluation (MCE)
The tool uses a popular approach, Multi Criteria Evaluation (MCE), used in Geographic Information Systems (GIS). It’s an intuitive and effective way to assess potential charging station locations from different angles. The MCE in the SDSS tool calculates scores based on factors for proximities to relevant geographical features/objects and various charging (network) demand metrics.
Balancing these scores based on the preferences of the person analyzing the data generates a single overall score for each location. These scores can then be used to compare and rank potential sites, helping decide the best rollout plan for new charging infrastructure.
Future Potential: Enhanced Functionality and Network Efficiency
In the future, this planning tool will be expanded to include network resilience and spatial sensitivity analysis functionality, helping to assess and, by design, ensure the robustness of operations on the designed charging networks and further reduce investment risks. Overall, this innovative approach to EV charging infrastructure planning has the potential to revolutionize the industry, making it more affordable, accessible, and sustainable for users.