A Two-Fitness Resource Scheduling Strategy Based on Improved Particle Swarm Optimization

2020 ◽  
pp. 263-277
Author(s):  
Xueming Qiao ◽  
Meng Chen ◽  
Xiangkun Zhang ◽  
Weiyi Zhu ◽  
Yanhong Liu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Particle Swarm ◽  
Resource Scheduling ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Scheduling Strategy

Multi-Objective Comprehensive Charging/Discharging Scheduling Strategy for Electric Vehicles Based on the Improved Particle Swarm Optimization Algorithm

2021 ◽  
Vol 9 ◽  
Author(s):  
Baling Fang ◽  
Bo Li ◽  
Xingcheng Li ◽  
Yunzhen Jia ◽  
Wenzhe Xu ◽  
...  
Keyword(s):  
Particle Swarm Optimization ◽  
Electric Vehicles ◽  
Distribution Network ◽  
Particle Swarm ◽  
Objective Functions ◽  
Swarm Optimization ◽  
System Load ◽  
Improved Particle Swarm Optimization ◽  
Scheduling Strategy ◽  
Multi Objective

To solve the problems that a large number of random and uncontrolled electric vehicles (EVs) connecting to the distribution network, resulting in a decrease in the performance and stability of the grid and high user costs, in this study, a multi-objective comprehensive charging/discharging scheduling strategy for EVs based on improved particle swarm optimization (IPSO) is proposed. In the distribution network, the minimum root-mean-square error and the minimum peak valley difference of system load are first designed as objective functions; on the user side, the lowest charge and discharge cost of electric vehicle users and the lowest battery loss cost are used as objective functions, then a multi-objective optimization scheduling model for EVs is established, and finally, the optimization through IPSO is performed. The simulation results show that the proposed method is effective, which enhances the peak regulating capacity of the power grid, and it optimizes the system load and reduces the user cost compared with the conventional methods.

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