A Multi-Objective Optimization Framework for Electric Vehicle Charge Scheduling With Adaptable Charging Ports

The problem of charge scheduling of Electric Vehicles (EVs) at charging stations remains one of the significant challenges due to high charging time and insufficient charging infrastructure leading to unfulfilled demands. Moreover, most public charging stations (CSs) are equipped with charging ports...

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Bibliographic Details
Published in:IEEE transactions on vehicular technology 2023-05, Vol.72 (5), p.5702-5714
Main Authors: Mishra, Sanghamitra, Mondal, Arijit, Mondal, Samrat
Format: Article
Language:English
Subjects:
Adaptable ports
Approximation algorithms
Charging stations
Costs
Customer satisfaction
Electric charge
Electric vehicle charging
Electric vehicles
Evolutionary algorithms
Genetic algorithms
Heuristic methods
Incentives
Maximization
Multiple objective analysis
Pareto optimization
Pricing
Processor scheduling
Profit margins
Scheduling
Sorting algorithms
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Summary:The problem of charge scheduling of Electric Vehicles (EVs) at charging stations remains one of the significant challenges due to high charging time and insufficient charging infrastructure leading to unfulfilled demands. Moreover, most public charging stations (CSs) are equipped with charging ports that serve only a fixed charging rate. The installation of adaptable ports, that can vary their rate of charging with time, has been observed to alleviate these challenges. Hence, we propose an efficient EV charge scheduling plan, for a CS equipped with adaptable charging ports, to improve its performance. The CS aims at maximizing not only its profit but also its total customer satisfaction. Also, it is assumed that, upon being unable to fulfill their total energy demands, the CS pays an incentive to the EV owners. Such incentives reduce the profit margins of the CSs. Hence, we formulate a bi-objective optimization EV scheduling model that drives the CSs toward maximizing their profit and customer satisfaction. Satisfiability Modulo Theory (SMT) solver and Non-dominated Sorting Genetic Algorithm-II (NSGA-II) evolutionary algorithm are used to obtain the optimal and approximate Pareto fronts respectively. We further propose a charging action replacement-based heuristic approach to speed up the process of obtaining an approximate set of non-dominated solutions. We run several simulations and observe that the proposed algorithm results in a near-optimal set of solutions compared to the actual Pareto front with a much less computation time.
ISSN:0018-9545
1939-9359
DOI:10.1109/TVT.2022.3231901