2020/02/17 by Álvaro Porras, Porras, Álvaro, Ricardo Fernández‐Blanco +5
Energy · Engineering · #90C90 #Electric Vehicles and Infrastructure #Energy, Environment, and Transportation Policies #FOS: Mathematics #Optimization and Control (math.OC) #Transportation and Mobility Innovations
paper · pdf · doi:10.48550/arxiv.2002.07021
openalex publication_date 2020/02/17 · openalex created_date 2022/07/26 · openalex updated_date 2026/07/28
The growing use of electric vehicles (EVs) may hinder their integration into\nthe electricity system as well as their efficient operation due to the\nintrinsic stochasticity associated with their driving patterns. In this work,\nwe assume a profit-maximizer EV-aggregator who participates in the day-ahead\nelectricity market. The aggregator accounts for the technical aspects of each\nindividual EV and the uncertainty in its driving patterns. We propose a\nhierarchical optimization approach to represent the decision-making of this\naggregator. The upper level models the profit-maximizer aggregator's decisions\non the EV-fleet operation, while a series of lower-level problems computes the\nworst-case EV availability profiles in terms of battery draining and energy\nexchange with the market. Then, this problem can be equivalently transformed\ninto a mixed-integer linear single-level equivalent given the totally\nunimodular character of the constraint matrices of the lower-level problems and\ntheir convexity. Finally, we thoroughly analyze the benefits of the\nhierarchical model compared to the results from stochastic and deterministic\nmodels.\n