A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network
Abstract The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to iden...
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| Format: | Article |
| Language: | English |
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Wiley
2024-10-01
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| Series: | IET Renewable Power Generation |
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| Online Access: | https://doi.org/10.1049/rpg2.13074 |
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| author | Mohd Bilal Saket Gupta Pitshou N. Bokoro Gulshan Sharma |
| author_facet | Mohd Bilal Saket Gupta Pitshou N. Bokoro Gulshan Sharma |
| author_sort | Mohd Bilal |
| collection | DOAJ |
| description | Abstract The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1. |
| format | Article |
| id | doaj-art-4622c737f2a449428a80130b73eb3fa4 |
| institution | Kabale University |
| issn | 1752-1416 1752-1424 |
| language | English |
| publishDate | 2024-10-01 |
| publisher | Wiley |
| record_format | Article |
| series | IET Renewable Power Generation |
| spelling | doaj-art-4622c737f2a449428a80130b73eb3fa42025-01-10T17:41:03ZengWileyIET Renewable Power Generation1752-14161752-14242024-10-0118142305232510.1049/rpg2.13074A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution networkMohd Bilal0Saket Gupta1Pitshou N. Bokoro2Gulshan Sharma3Department of Electrical Engineering TechnologyUniversity of JohannesburgJohannesburg South AfricaDepartment of Instrumentation and Control EngineeringBVCOENew DelhiIndiaDepartment of Electrical Engineering TechnologyUniversity of JohannesburgJohannesburg South AfricaDepartment of Electrical Engineering TechnologyUniversity of JohannesburgJohannesburg South AfricaAbstract The adoption of electric vehicles (EVs) is crucial for reducing pollution from traditional automobiles. Strategic placement of electric vehicle charging stations (EVCS) is needed to meet demand while minimizing impacts on the electrical grid. This article outlines a practical method to identify optimal EVCS locations within the IEEE 69 bus system. The transition to EVs affects the electrical distribution network, requiring consideration of voltage regulation, power loss, stability, reliability, and energy loss costs when deploying EVCS. To manage increased energy demands, the article recommends integrating solar distributed generation (SDG) units at strategic points in the network, creating a self‐sustaining system. The study explores the resilience of the distribution system with EVCS and SDGs through eight case studies (CS), examining EVCS deployment scenarios with and without SDG integration. The impact of slow and fast EV charging on system objectives is also analysed. The artificial hummingbird algorithm is used to solve the allocation problem, with results compared to other optimization methods. Notably, active power loss decreased from 224.67 kW (CS1) to 53.35 kW (CS8), and reactive power loss was reduced by 71.4% in CS8 compared to CS1.https://doi.org/10.1049/rpg2.13074electric vehicle chargingelectric vehiclesoptimisationpower distributionrenewable energy sources |
| spellingShingle | Mohd Bilal Saket Gupta Pitshou N. Bokoro Gulshan Sharma A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network IET Renewable Power Generation electric vehicle charging electric vehicles optimisation power distribution renewable energy sources |
| title | A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network |
| title_full | A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network |
| title_fullStr | A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network |
| title_full_unstemmed | A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network |
| title_short | A probabilistic approach for optimal integration of EVs and RES using artificial hummingbird algorithm in distribution network |
| title_sort | probabilistic approach for optimal integration of evs and res using artificial hummingbird algorithm in distribution network |
| topic | electric vehicle charging electric vehicles optimisation power distribution renewable energy sources |
| url | https://doi.org/10.1049/rpg2.13074 |
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