EV charging in North Rhine-Westphalia

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Loading map layers… Select a district to inspect the evidence, then test a proposed station. District scores support shortlisting; they do not identify approved sites. Methods & limitations · Data sources.

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District comparison

Investment Priority combines 60% charging deficit with 40% infrastructure opportunity to shortlist districts for new stations.

Total NRW stations -
Avg EV readiness -
Best EV readiness -
Most underserved - Largest charging gap: 100 − EV Readiness.
Highest priority - 60% charging gap + 40% infrastructure opportunity.
Fast charging share — Maximum point power ≥50 kW; statewide share of classified stations.

Where does NRW need more EV charging?

From 53 districts to a shortlist for your next charging project.

Identify gaps in public charging, compare infrastructure context and test how a proposed station changes the district’s charging supply.

We compare charging supply with population and accessibility, then consider traffic, mapped electricity infrastructure and renewable energy. Select a planning question, explore the map and ranking, and test a proposed station.

What we compareEV Readiness combines density (40%), accessibility (30%) and population-adjusted coverage (30%). Investment Priority combines charging deficit (60%) with infrastructure opportunity (40%).

The evidence behind itBundesnetzagentur charging stations; Eurostat population and boundaries; Straßen.NRW traffic; Energieatlas NRW energy data; OpenStreetMap grid and road data. See indicator formulas.

Before choosing a siteThese are comparative screening scores, not forecasts or approved construction sites. Exact locations need checks for demand, land access and grid connection. Accessibility is a distance proxy, not a driving-time measurement.

How the indicators are calculated

All data sources

0–100 is a comparison scale. It is not a percentage of demand met. The formulas below describe the canonical SQL analytical model used by both live data and the local fallback snapshot. Check the data notice above the map before interpreting a score.

EV Readiness

How well is a district served today?

40% Charging-point Density + 30% Charger Accessibility + 30% Population-adjusted Coverage.

Density measures charging points per km². Accessibility measures straight-line distance from the district’s geographic centre to the nearest station: closer scores higher. Population-adjusted coverage measures charging points per 100,000 residents. A station may contain several charging points.

Charger Deficit

Where is the charging gap?

Charger Deficit = 100 − EV Readiness.

Answers “Where is the charging gap greatest?” Most underserved is the district with the highest deficit, taking charging supply, population and accessibility into account. It does not estimate an exact number of missing stations.

Infrastructure Opportunity

What infrastructure is already nearby?

40% Transport Load + 35% Grid Readiness Proxy + 25% Renewable Context.

Combines traffic, mapped electricity infrastructure and existing renewable energy. Higher scores indicate stronger infrastructure context for further investigation, not confirmed spare grid capacity. See the detailed breakdown below.

Investment Priority

Where should investigation start?

60% Charger Deficit + 40% Infrastructure Opportunity.

Answers “Which district should we investigate first for new stations?” Highest priority balances need with infrastructure context. It can differ from Most underserved because 40% of this score comes from infrastructure opportunity.

Grid Absorption Risk Proxy

Where does grid uncertainty need attention?

45% Local Energy Balance + 30% Renewable Growth + 25% inverse Grid Readiness Proxy.

This is a screening proxy, not measured DSO hosting capacity. Feeder loading, transformer headroom and connection queues are not available.

Why “Most underserved” and “Highest priority” can differ

Most underserved means the largest charging gap. Highest priority combines that gap with infrastructure opportunity to shortlist districts for closer review.

Illustrative example: District A has a deficit of 90 and infrastructure opportunity of 20, giving a priority of 62 (0.60 × 90 + 0.40 × 20). District B has a smaller deficit of 70 but opportunity of 80, giving a priority of 74. A is more underserved; B has higher investment priority. These are example values, not NRW observations.

Infrastructure Opportunity: inputs, weights and limitations

40% Transport Load + 35% Grid Readiness Proxy + 25% Renewable Context. The percentages below are weights within each component, not shares of the overall score.

Transport Load — 40% of the total

  • 60% traffic intensity: average daily vehicle traffic, weighted by the length of measured road sections.
  • 25% traffic-weighted road density: road length multiplied by traffic, divided by district area.
  • 15% road proximity: straight-line distance from the district’s geographic centre to the nearest mapped road; closer scores higher.

Source: Straßen.NRW. Traffic covers federal, state and district roads. Autobahn traffic is excluded; the Autobahn map layer supplies geometry only.

Grid Readiness Proxy — 35% of the total

  • 45% substation proximity: distance from the district’s geographic centre to the nearest mapped substation or transformer; closer scores higher.
  • 35% voltage-weighted line density: power-line length multiplied by mapped voltage, divided by district area.
  • 20% substation density: mapped substations and transformers per km².

Source: OpenStreetMap. This describes mapped infrastructure, not available connection capacity. It does not measure grid congestion, transformer headroom or connection approval.

Renewable Context — 25% of the total

  • 70% installed capacity density: capacity of operating renewable installations in MW per km².
  • 30% technology diversity: the number of distinct renewable technologies with positive installed capacity.

Source: Energieatlas NRW. This measures existing installations, not future generation potential or electricity reserved for EV charging.

Map and calculation differ: the map displays solar farms and wind installations. Rooftop solar is hidden on the map but remains included in the analytical totals.

How different units become comparable

Each input is converted to a 0–100 score using the 5th and 95th percentiles across the district comparison data. For inputs where more means a higher score, values at or below the lower bound score 0 and values at or above the upper bound score 100; values between them are scaled proportionally. Distance scores run in reverse. If both bounds are equal, the component scores 50.

The model then applies the weights above. For example, transport 80, grid 60 and renewables 40 give an infrastructure opportunity score of 63: 0.40 × 80 + 0.35 × 60 + 0.25 × 40.

If a required component is unavailable, the overall infrastructure score is unavailable rather than calculated with replacement weights. A score can still use partial mapped or measured coverage; coverage information matters when comparing districts. A high score does not establish that a specific site is suitable for construction.