Bilevel Optimization for Improved Flexibility Aggregation Models of Electric Vehicle Fleets

TL;DR

This paper introduces a bilevel optimization approach to improve the accuracy of flexibility aggregation models for electric vehicle fleets, significantly reducing errors compared to traditional methods.

Contribution

It presents a novel bilevel optimization framework that enhances EV fleet flexibility modeling by minimizing scheduling deviations and maximizing profits.

Findings

Reduces RMS error of charging power by 78%

Provides more accurate flexibility representations

Establishes a foundation for future extensions

Abstract

Electric vehicle (EV) fleets are expected to become an increasingly important source of flexibility for power system operations. However, accurately capturing the flexibility potential of numerous and heterogeneous EVs remains a significant challenge. We propose a bilevel optimization formulation to enhance flexibility aggregations of electric vehicle fleets. The outer level minimizes scheduling deviations between the aggregated and reference EV units, while the inner level maximizes the aggregated unit's profits. Our approach introduces hourly to daily scaling factor mappings to parameterize the aggregated EV units. Compared to simple aggregation methods, the proposed framework reduces the root-mean-square error of charging power by 78~per cent, providing more accurate flexibility representations. The proposed framework also provides a foundation for several potential extensions in future work.