Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm

Hugo Morais, Tiago Sousa, Rui Castro*, Zita Vale

*Corresponding author for this work

    Research output: Contribution to journalJournal articleResearchpeer-review

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    The introduction of electric vehicles (EVs) will have an important impact on global power systems, in particular on distribution networks. Several approaches can be used to schedule the charge and discharge of EVs in coordination with the other distributed energy resources connected on the network operated by the distribution system operator (DSO). The aggregators, as virtual power plants (VPPs), can help the system operator in the management of these distributed resources taking into account the network characteristics. In the present work, an innovative hybrid methodology using deterministic and the elitist nondominated sorting genetic algorithm (NSGA-II) for the EV scheduling problem is proposed. The main goal is to test this method with two conflicting functions (cost and greenhouse gas (GHG) emissions minimization) and performing a comparison with a deterministic
    approach. The proposed method shows clear advantages in relation to the deterministic method, namely concerning the execution time (takes only 2% of the time) without impacting substantially the obtained results in both objectives (less than 5%).
    Original languageEnglish
    Article number7978
    JournalApplied Sciences
    Number of pages18
    Publication statusPublished - 2020


    • Electric vehicles
    • Elitist nondominated sorting genetic algorithm
    • Multi-objective optimization
    • Optimal resource scheduling
    • Virtual power plants


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