Please use this identifier to cite or link to this item: http://localhost/handle/Hannan/601469
Title: Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario
Authors: Qi Kang;JiaBao Wang;MengChu Zhou;Ahmed Chiheb Ammari
subject: electric vehicle|genetic algorithm|Battery swap|particle swarm optimization|centralized charging
Year: 2016
Publisher: IEEE
Abstract: Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.
URI: http://localhost/handle/Hannan/178856
http://localhost/handle/Hannan/601469
ISSN: 1524-9050
1558-0016
volume: 17
issue: 3
Appears in Collections:2016

Files in This Item:
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7330009.pdf4.88 MBAdobe PDFThumbnail
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Title: Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario
Authors: Qi Kang;JiaBao Wang;MengChu Zhou;Ahmed Chiheb Ammari
subject: electric vehicle|genetic algorithm|Battery swap|particle swarm optimization|centralized charging
Year: 2016
Publisher: IEEE
Abstract: Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.
URI: http://localhost/handle/Hannan/178856
http://localhost/handle/Hannan/601469
ISSN: 1524-9050
1558-0016
volume: 17
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7330009.pdf4.88 MBAdobe PDFThumbnail
Preview File
Title: Centralized Charging Strategy and Scheduling Algorithm for Electric Vehicles Under a Battery Swapping Scenario
Authors: Qi Kang;JiaBao Wang;MengChu Zhou;Ahmed Chiheb Ammari
subject: electric vehicle|genetic algorithm|Battery swap|particle swarm optimization|centralized charging
Year: 2016
Publisher: IEEE
Abstract: Centralized charging of electric vehicles (EVs) based on battery swapping is a promising strategy for their large-scale utilization in power systems. The most outstanding feature of this strategy is that EV batteries can be replaced within a short time and can be charged during off-peak periods or on low electric price and scheduled in any battery swap station. This paper proposes a novel centralized charging strategy of EVs under the battery swapping scenario by considering optimal charging priority and charging location (station or bus node in a power system) based on spot electric price. In this strategy, a population-based heuristic approach is designed to minimize total charging cost, as well as to reduce power loss and voltage deviation of power networks. We introduce a dynamic crossover and adaptive mutation strategy into a hybrid algorithm of particle swarm optimization and genetic algorithm. The resulting algorithm and several others are executed on an IEEE 30-bus test system, and the results suggest that the proposed one is effective and promising for optimal EV centralized charging.
URI: http://localhost/handle/Hannan/178856
http://localhost/handle/Hannan/601469
ISSN: 1524-9050
1558-0016
volume: 17
issue: 3
Appears in Collections:2016

Files in This Item:
File Description SizeFormat 
7330009.pdf4.88 MBAdobe PDFThumbnail
Preview File