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The acceptance rate of Electric Vehicles (EVs) in the transport industry has increased substantially due to the augmented interest towards sustainable transportation initiatives. However, their impact in terms of increased power demand on the electric power market can increase real power losses, decrease voltage profile, and consequently decrease voltage stability margins. It is necessary to install Electric Vehicle Charging Stations (EVCSs) and Distributed Generators (DGs) at optimal locations to decrease the EV load effect in the Radial Distribution System (RDS). This paper addresses a multi-objective optimization technique to obtain simultaneous EVCS & DG placement and sizing. The problem is formulated to optimize real power losses, Average Voltage Deviation Index (AVDI), and Voltage Stability Index (VSI) of the electrical distribution system. Simulation studies were performed on the standard IEEE 33-bus and 69-bus test systems. Harries Hawk Optimization (HHO) and Teaching-Learning Based Optimization (TLBO) algorithms were selected to minimize the system objectives. The simulation outcomes reveal that the proposed approach improved system performance in all aspects. Among HHO and TLBO, HHO is reasonably successful in accomplishing the desired goals.
Voltage Stability Index (VSI), Average Voltage Deviation Index (AVDI), Electrical Vehicles (EVs), Electric Vehicle Charging Stations (EVCSs), Teaching-Learning Based Optimization (TLBO), Distributed Generators (DGs), renewable energy sources, Harris Hawks Optimization (HHO)
Voltage Stability Index (VSI), Average Voltage Deviation Index (AVDI), Electrical Vehicles (EVs), Electric Vehicle Charging Stations (EVCSs), Teaching-Learning Based Optimization (TLBO), Distributed Generators (DGs), renewable energy sources, Harris Hawks Optimization (HHO)
| selected citations These citations are derived from selected sources. This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | 69 | |
| popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network. | Top 1% | |
| influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically). | Top 10% | |
| impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network. | Top 10% |
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