A hierarchical robust scheduling framework for electric vehicle aggregators in coupled spot and ancillary service markets

Yunfan Meng, Yonghui Sun, Liang Zhao, Chenxu Yin and Fan Sheng

Energy, 2025, vol. 332, issue C

Abstract: This paper proposes a bi-level scheduling framework integrating probability-interval hybrid uncertainty modeling with robust optimization to address the co-existing challenges of electric vehicle (EV) user behavior and electricity market uncertainties. Targeting electric vehicle aggregators (EVAs) in the electricity spot market (ESM) and frequency regulation ancillary service market (FRASM), the framework hierarchically manages multi-market coordination. At the upper level, Analytic Hierarchy Process (AHP) and probability density functions (PDF) characterize stochastic EV behaviors (initial state-of-charge, arrival/departure times, multi-rate charging), with Monte Carlo (MC) sampling and Mini-Batch K-means clustering generating representative scenarios. The lower level develops a two-stage robust optimization model with battery degradation costs, where interval uncertainty sets (IPUs) bound market price/demand fluctuations, and compressed binary scenario matrices inform first stage bidding decisions. To solve this large-scale model efficiently, nonlinear constraints are linearized via Big M and McCormick envelopes, while the Column and Constraint Generation (C&CG) algorithm enables rapid worst-case optimization. Case studies validate the framework's effectiveness in enhancing economic returns and robustness for multi-market EVA operations.

Keywords: Electric vehicle; Electricity market; Robust optimization; Uncertainty sets; Battery degradation (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0360544225027550
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:332:y:2025:i:c:s0360544225027550

DOI: 10.1016/j.energy.2025.137113

Access Statistics for this article

Energy is currently edited by Henrik Lund and Mark J. Kaiser

More articles in Energy from Elsevier
Bibliographic data for series maintained by Catherine Liu ().