Grasshopper optimization algorithm based two stage fuzzy multiobjective approach for optimum sizing and placement of distributed generations, shunt capacitors and electric vehicle charging stations

2020 ◽  
Vol 27 ◽  
pp. 101117 ◽  
Author(s):  
Srinivasa Rao Gampa ◽  
Kiran Jasthi ◽  
Preetham Goli ◽  
D. Das ◽  
R.C. Bansal
Keyword(s):  
Electric Vehicle ◽  
Optimization Algorithm ◽  
Two Stage ◽  
Distributed Generations ◽  
Electric Vehicle Charging ◽  
Grasshopper Optimization Algorithm ◽  
Multiobjective Approach ◽  
Grasshopper Optimization ◽  
Shunt Capacitors ◽  
Charging Stations

The Location of Electric Vehicle Charging Stations based on FRLM with Robust Optimization

2019 ◽  
Vol 33 (08) ◽  
pp. 1959027 ◽  
Author(s):  
Jing-min Wang ◽  
Yan Liu ◽  
Yi-fei Yang ◽  
Wei Cai ◽  
Dong-xuan Wang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Robust Optimization ◽  
Electric Vehicle ◽  
Optimization Algorithm ◽  
Site Selection ◽  
Queuing Theory ◽  
Location Model ◽  
Electric Vehicle Charging ◽  
Charging Stations ◽  
Charging Demand

It is very important for the application of artificial intelligence to accurately and quickly help the electric vehicles to find matching charging facilities. The site selection for electric vehicle charging station (EVCS) is a new field of artificial intelligence application, using artificial intelligence to analyze the current complex urban electric vehicle driving path, and then determining the location of charging stations. This paper proposes a novel hybrid model to decide the location of EVCS. First of all, this paper carries out the flow-refueling location model (FRLM) based on path requirement to determine the site selection of EVCS. Secondly, robust optimization algorithm is used to resolve the location model considering the uncertainty of charging demand. Then, queuing theory, which takes the charging load as a constraint in the location model, is integrated into the model. Last, but not the least, a case is conducted to verify the validity of the proposed model when dealing with location problem. As a result of the above analysis, it is effective to apply robust optimization algorithm and to determine the location of EVCSs effectively when charging demand generated on the path is uncertain. At the same time, queuing theory can help to determine the optimal number of EVCSs effectively, and reduce the cost of building EVCSs.

scholarly journals Joint planning of electric vehicle charging station and distributed generation on location and capacity

2021 ◽  
Vol 252 ◽  
pp. 01040
Author(s):  
Peiwen Wang ◽  
Jin Shen
Keyword(s):  
Power Generation ◽  
Electric Vehicle ◽  
Distributed Generation ◽  
Distribution System ◽  
Widespread Application ◽  
Electric Vehicle Charging ◽  
Grasshopper Optimization Algorithm ◽  
Network Losses ◽  
Vehicle Charging ◽  
Charging Stations

The widespread application of distributed generation and electric vehicles are two important ways to save energy and reduce emissions. Therefore, the location and capacity of distributed generation and electric vehicle charging stations are particularly important. From the perspective of environmental protection, distributed power generation has obvious advantages over traditional power generation methods. Based on this, this paper establishes a model of location and capacity of electric vehicle charging stations with distributed generation with the lowest sum of investment, operation and maintenance costs, network losses and environmental costs. And this paper uses the Grasshopper Optimization Algorithm to solve the model. Finally, the IEEE 33-node distribution system is used as an example to perform calculations to verify the effectiveness and feasibility of the proposed model and algorithm.

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