• DOI:10.1109/NPSC49263.2020.9331921
• Corpus ID: 231735485

Multi-Objective Framework for Optimal Scheduling of Electric Vehicles

@article{Kapoor2020MultiObjectiveFF,
  title={Multi-Objective Framework for Optimal Scheduling of Electric Vehicles},
  author={Aastha Kapoor and Priyanka Gangwar and Ankush Sharma and Abheejeet Mohapatra},
  journal={2020 21st National Power Systems Conference (NPSC)},
  year={2020},
  pages={1-6},
  url={https://api.semanticscholar.org/CorpusID:231735485}
}

• Aastha Kapoor, P. Gangwar, +1 author A. Mohapatra
• Published in National Power Systems… 17 December 2020
• Engineering, Environmental Science

This paper presents a multi-objective framework for the optimal scheduling of Electric Vehicles (EVs) to satisfy the interests of multiple stakeholders, such as EV owner/aggregator and the Distribution System Operator (DSO). Optimal scheduling refers to smart charging and Vehicle-to-Grid (V2G) discharging operations of EV. The modelling of stochastic nature of arrival, departure, and the distance travelled by the EV is taken into account with appropriate Probability Distribution Funtions (PDFs… 

This paper presents a multi-objective framework for the optimal scheduling of Electric Vehicles (EVs) to satisfy the interests of multiple stakeholders, such as EV owner/aggregator and the Distribution System Operator (DSO). Optimal scheduling refers to smart charging and Vehicle-to-Grid (V2G) discharging operations of EV. The modelling of stochastic nature of arrival, departure, and the distance travelled by the EV is taken into account with appropriate Probability Distribution Funtions (PDFs). The proposed formulation considers the perspectives of the aggregator and the DSO, which are minimization of net cost of charging of EVs and the power loss in the system to improve the performance of the system, respectively. A multi-objective function is formulated using normalized linear weighted sum approach, to optimize both the objectives simultaneously. The Particle Swarm Optimization (PSO) is implemented to find out the optimal scheduling of charging and discharging of EVs. The competence of the proposed methodology is tested by implementing it on the IEEE 33-bus test feeder. The analysis is carried out for the residential and commercial loads with the consideration of different Time-of-Use (TOU) tariffs. The results show that the scheduling operation of EV, obtained by implementing proposed methodology, leads to significant reduction in the net cost of charging borne by the owner/aggregator and decrement in the network power loss.