# Day-ahead Operation of an Aggregator of Electric Vehicles via Optimization under Uncertainty

## TL;DR

This paper presents a bilevel optimization model for day-ahead operation of EV aggregators, providing robust charging schedules under uncertainty, and compares its performance with deterministic methods using real-world data.

## Contribution

It introduces a computationally efficient mixed-integer programming approach for EV aggregator scheduling that accounts for uncertainty and demonstrates its effectiveness with real data.

## Key findings

- Robust schedules outperform deterministic ones under uncertainty
- The model is computationally efficient due to the unimodular constraint structure
- Economic and technical analysis shows improved operation stability

## Abstract

We pose the aggregator's problem as a bilevel model, where the upper level minimizes the total operation costs of the fleet of EVs, while each lower level minimizes the energy available to each vehicle for transportation given a certain charging plan. Thanks to the totally unimodular character of the constraint matrix in the lower-level problems, the model can be mathematically recast as a computationally efficient mixed-integer program that delivers charging schedules that are robust against the uncertain availability of the EVs. Finally, we use synthetic data from the National Household Travel Survey 2017 to analyze the behavior of the EV aggregator from both economic and technical viewpoints and compare it with the results from a deterministic approach.