Optimal Scheduling of Plug-in Hybrid Electric Vehicles Operation in Distribution Networks Using Gravitational Search Algorithm

2019 ◽  
Vol 8 (1.6) ◽  
pp. 219-224
Author(s):  
Ahmad Asrul Ibrahim
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicles ◽  
Search Algorithm ◽  
Gravitational Search Algorithm ◽  
Distribution Networks ◽  
Optimal Scheduling ◽  
Gravitational Search ◽  
Hybrid Electric

Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

2016 ◽  
pp. 471-504 ◽  
Author(s):  
Imran Rahman ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Particle Swarm ◽  
Transportation Industry ◽  
Hybrid Particle ◽  
Search Optimization ◽  
Gravitational Search ◽  
Hybrid Electric ◽  
Computational Intelligence Methods

Electrification of Transportation has undergone major modifications since the last decade. Success of combining smart grid technology and renewable energy exclusively depends upon the large-scale participation of Plug-in Hybrid Electric Vehicles (PHEVs) towards reach the desired pollution-free transportation industry. One of the key Performance pointers of hybrid electric vehicle is the State-of-Charge (SoC) which needs to be enhanced for the advancement of charging station using computational intelligence methods. In this Chapter, authors applied Hybrid Particle swarm and gravitational search Optimization (PSOGSA) technique for intelligently allocating energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time. Computational experiment results attained for maximizing the highly non-linear fitness function estimates the performance measure of both the techniques in terms of best fitness value and computation time.

Hybrid Particle Swarm and Gravitational Search Optimization Techniques for Charging Plug-In Hybrid Electric Vehicles

2020 ◽  
pp. 195-228
Author(s):  
Imran Rahman ◽  
Pandian Vasant ◽  
Balbir Singh Mahinder Singh ◽  
M. Abdullah-Al-Wadud
Keyword(s):  
Electric Vehicles ◽  
Hybrid Electric Vehicle ◽  
Hybrid Electric Vehicles ◽  
Particle Swarm ◽  
Transportation Industry ◽  
Hybrid Particle ◽  
Search Optimization ◽  
Gravitational Search ◽  
Hybrid Electric ◽  
Computational Intelligence Methods

Electrification of Transportation has undergone major modifications since the last decade. Success of combining smart grid technology and renewable energy exclusively depends upon the large-scale participation of Plug-in Hybrid Electric Vehicles (PHEVs) towards reach the desired pollution-free transportation industry. One of the key Performance pointers of hybrid electric vehicle is the State-of-Charge (SoC) which needs to be enhanced for the advancement of charging station using computational intelligence methods. In this Chapter, authors applied Hybrid Particle swarm and gravitational search Optimization (PSOGSA) technique for intelligently allocating energy to the PHEVs considering constraints such as energy price, remaining battery capacity, and remaining charging time. Computational experiment results attained for maximizing the highly non-linear fitness function estimates the performance measure of both the techniques in terms of best fitness value and computation time.

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