I. Diagnosis: The State of Berlin (Q2 2025)
Explore the current infrastructure gap. Central districts are well-supplied, while outer districts face severe congestion.
Click on a district to view detailed metrics.
Select a District
Click the grid on the left to analyze specific infrastructure data.
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Total Chargers
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Registered BEVs
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Congestion (BEVs / Charger)
II. The Optimization: Choosing a Strategy
We modeled the deployment of 100 New Chargers. Compare how different optimization priorities shift resources across the city.
Max Equity Strategy
Prioritizes "charging deserts." It aggressively allocates resources to underserved districts like Treptow-Köpenick to lower the Gini coefficient.
Key Outcome (K=100)
- Gini Coefficient: 0.258 (-13%)
- Focus Area: Peripheral Districts
Charger Allocation by District (K=100)
Optimization Gain
vs. Deterministic Planning
III. The Trade-off (Pareto Front)
There is no single "perfect" solution. The chart below shows the optimal trade-offs found by the NSGA-II algorithm. Solutions to the top-left are better (Higher Utility, Higher Equity).
X: Equity (1 - Gini) | Y: Utility (Coverage)
IV. Why "Fuzzy" Matters
Standard plans are fragile. Our Robust (Fuzzy) model guarantees higher performance even when EV adoption fluctuates unexpectedly (Worst-Case Scenarios).
Comparison of Minimum Satisfaction levels in worst-case scenarios
V. Future Work — Grid Integration
Planned extensions to improve robustness, scalability and operational intelligence for large-scale EV integration. Focus areas include spatially-aware power flow, dynamic loading scenarios and smart charging control.
Geospatial Power Flow
Couple geospatial locations with grid topology to detect localized voltage constraints and prioritize reinforcement.
Dynamic Loading & Reinforcement
Simulate peak EV penetration events and time-varying dispatch to size reinforcements and control strategies.
Smart Charging & V2G
Design demand-response and V2G control to flatten peaks and support distribution-level services.