Multi-Objective Electric Vehicles Scheduling Using Elitist Non-Dominated Sorting Genetic Algorithm
Source abstract
Abstract
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%).
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Electric Vehicles and Infrastructure · Smart Grid Energy Management · Advanced Battery Technologies Research
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
INESC-ID/IST, Universidade de Lisboa, Av. Rovisco Pais, 1049-001 Lisbon, Portugal
Department of Electrical Engineering, Technical University of Denmark, 2800 Kongens Lyngby, Denmark
Polytechnic of Porto, Rua Dr. António Bernardino de Almeida, 431, 4249-015 Porto, Portugal
Funding
Fundação para a Ciência e Tecnologia
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