New MILP Framework for Shared EV Fleets Slashes Demand Charges by 30%+ — Real-World Data Shows It's Feasible
Shared EVs as mobile storage? New model proves profitability with real SF data.
Deep Dive
Researchers proposed a high-fidelity MILP framework for shared EV fleets to reduce commercial demand charges. Unlike idealized models, it accounts for driver wages, battery degradation, and transit overhead. Using San Francisco data, a modest number of EVs achieve savings that fully offset ownership and operational expenses. A marginal-value heuristic delivers near-optimal results at low computational cost.
Key Points
- MILP framework jointly optimizes demand charges, driver wages, and battery degradation under spatio-temporal constraints.
- Real San Francisco data shows a 20-EV fleet achieves demand charge savings that fully cover ownership costs.
- Marginal-value heuristic runs fast while keeping solutions within 1–2% of optimal MILP results.
Why It Matters
Shared EV fleets can transform demand charge costs into profit, accelerating commercial EV adoption and grid resilience.