Electronic Theses and Dissertations

Date

2025

Document Type

Thesis

Degree Name

Master of Science

Department

Electrical & Computer Engineering

Committee Chair

Hasan Ali

Committee Member

Davoodi Mohammadreza

Committee Member

Lee Yong

Abstract

The rapid adoption of Electric Vehicles (EVs) necessitates to optimize the grid through strategic placement of Electric Vehicle Charging Station (EVCS). Conventional algorithms such as Greywolf Optimization Algorithm and Particle Swarm Optimization have been used for the placement of EVCS, but they have difficulties in dealing with dynamic and complex environments. With this background, this thesis proposes a Modified Artificial Hummingbird Algorithm (MAHA) for optimal placement of EVCS in the IEEE 9-bus, 57-bus, and 118-bus systems. The first approach uses a single objective function that minimizes transmission losses due to EV charging, while the second approach considers a multi-objective function that includes dynamic user demand, battery energy storage system, and solar photovoltaic generation. MAHA, inspired by the foraging of hummingbirds, provides a more flexible approach and helps ongoing efforts for smart grid development and sustainability regarding EVCS deployment. Matlab/Simulink based simulation results demonstrate the effectiveness of the proposed MAHA method in optimal placement of EVCS.

Comments

Data is provided by the student.

Library Comment

Dissertation or thesis originally submitted to ProQuest.

Notes

Open access

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