Future-Proofing Berlin's EV Infrastructure
The path to resilient urban planning requires iterative refinement. We started by mapping the Equity-Utility trade-off in Berlin (Paper 1), but quickly realized the solution was fragile. Our second phase (Paper 2) introduces a Fuzzy Optimization Framework to build resilience against real-world uncertainty.
Resilience Improvement
+20%
Higher Guaranteed Satisfaction over the Fragile Plan
The Fuzzy approach explicitly models fluctuating EV adoption rates and flexible budgets to guarantee high utility and equity performance across all possible futures.
Chapter I: The Foundational Model (Paper 1)
View PresentationThis chapter is based on the initial work: Enabling Equitable EV Charger Deployment in Berlin with Multi-Objective Geospatial Optimization. It established the core challenge: balancing maximum charging coverage (Utility) with fair distribution (Equity).
Defining Utility and Equity in Berlin
Objective 1: Maximize Utility
Objective 2: Maximize Equity
This trade-off yielded the Crisp Pareto Front, but its plans were only optimal if demand predictions were 100% accurate.
The Crucial Flaw: The Fragile Solution
The deterministic model in Paper 1 failed to account for two critical real-world factors: the uncertainty in future EV adoption rates and the flexibility in planning budgets. This created brittle plans that could suffer massive performance drops if assumptions proved wrong.
EV adoption is a wide range, not a single number.
Budgets often have an acceptable range of investment.
A plan optimized for the 'best guess' fails spectacularly in 'worst-case' scenarios.
Addressing Brittleness: The Move to Robustness (Paper 2)
The weakness of the initial plan necessitated a new, higher-level framework. Paper 2 introduces the Fuzzy Multi-Objective Optimization approach—explicitly modeling the uncertainty of demand and budget—to guarantee a resilient and reliable EV network, regardless of future volatility.
Continue to Chapter II (The Robust Solution) ↓Chapter II: The Robust Solution (Paper 2)
This chapter details the methodology and superior results of applying the Fuzzy Robust Optimization Framework. The focus shifts from simply optimizing for a single best case to optimizing for the highest guaranteed minimum performance.
Steps of the Robust-Fuzzy Solution Procedure
Demand & budget are modeled as ranges (TFNs)
Use Chance-Constrained Programming for objective satisfaction
Apply the E-NSGAII Algorithm to find the Robust Pareto Front
Finding A: Superior Resilience Under Pressure
This chart quantifies the value of the robust approach. Compared to the deterministic solution (Paper 1), the Fuzzy Robust plan (Paper 2) ensures 15-20% higher minimum satisfaction levels for both Utility and Equity in the absolute worst-case scenarios.
Finding B: Navigating the Robustness Trade-Off
The Fuzzy Pareto Front (below) allows planners to visualize four dimensions: expected Utility (Y-axis), expected Equity (X-axis), Robustness (color/transparency), and Risk/Uncertainty Span (bubble size).
The Optimal Outcome: Berlin's Robust Deployment Plan
The model recommends the "Most Robust Solution," which allocates approximately 108 new chargers. This plan maximizes resilience by proactively utilizing the flexible budget to hedge against the maximum potential future EV demand.
Chapter III: The Next Frontier
Moving beyond the policy-level strategic plan, Chapter III focuses on the immediate next steps required to transition this robust framework into an actionable, street-level engineering solution for Berlin's power and transportation grids.
Grid Integration & Resilience
The current model is geographically optimal but power-agnostic. Future work must couple the charger locations with power flow simulations to identify potential distribution grid bottlenecks, especially in areas with high density of new chargers.
- Minimize voltage sags and maximize feeder capacity utilization.
- Develop V2G (Vehicle-to-Grid) strategies informed by robust locations.
Finer-Grained Equity & Justice
While the Gini Index provided district-level fairness, true urban equity requires granularity. We must incorporate socio-economic indicators (income, car ownership, housing type) at the census-block level to ensure we address charging deserts for renters and low-income residents.
- Identify areas dependent on street parking (no home charging).
- Optimize placement for maximum social accessibility.
Dynamic Behavioral Modeling
Current demand is static. We must integrate dynamic user behaviors, such as adaptive routing (drivers choosing a non-closest but available charger) and price elasticity to refine demand predictions under operational conditions.
- Simulate queueing effects and waiting times.
- Inform dynamic pricing policies for load balancing.