Swarm intelligence‐based energy management of electric vehicle charging station integrated with renewable energy sources

Summary This research paper proposes a detailed design problem of electrical vehicle (EV) fast‐charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid...

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Veröffentlicht in:International journal of energy research Jg. 46; H. 15; S. 21598 - 21618
Hauptverfasser: Ray, Priyanka, Bhattacharjee, Chayan, Dhenuvakonda, Koteswara Raju
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Chichester, UK John Wiley & Sons, Inc 01.12.2022
Schlagworte:
Algorithms
Alternative energy sources
Cost benefit analysis
Design
Electric vehicle charging
electric vehicle charging station
Electric vehicles
Electronic equipment
Energy demand
Energy management
energy management and renewable energy sources
Energy resources
Energy storage
hybrid genetic pattern search algorithm (hGPS)
Intelligence
Operating costs
Particle swarm optimization
Pattern search
Performance indices
Profit
Renewable energy
Renewable energy sources
Renewable resources
Scientific papers
Search algorithms
Storage units
Swarm intelligence
ISSN:0363-907X, 1099-114X
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Zusammenfassung:Summary This research paper proposes a detailed design problem of electrical vehicle (EV) fast‐charging stations to maximize the net profit. The charging station is integrated with the renewable energy sources (RES) and battery energy storage system (BESS) to minimize the energy demand from the grid. The performance indices of the design problem, such as the number of chargers and their rating, installed RES power, energy and power of the storage units, and dynamic power contracted by the grid to the EV charging station, is estimated through the proposed algorithm. The detailed modelling of the charging process considers the EV behavioural characteristics like arrival time, state of charge, departure time, and battery capacity and is simulated through sequential Monte‐Carlo method. The hybrid Crow Search Algorithm (hCSA), along with the particle swarm optimization (hCSA‐PSO), is adopted for the first time to optimize the charging station's installation and operational cost. The effectiveness of the proposed method is compared with a hybrid genetic pattern search algorithm, CSA, and PSO. Several case studies are considered, and it is observed that hCSA‐PSO provides the best‐optimized results in profit maximization. The cost‐benefit analysis is also performed to estimate the financial feasibility of both RES and BESS.
Bibliographie:ObjectType-Article-1
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ISSN:0363-907X
1099-114X
DOI:10.1002/er.7601