数学优化
跳跃
网格
粒子群优化
电动汽车
电荷(物理)
理论(学习稳定性)
职位(财务)
混乱的
控制(管理)
电网
控制理论(社会学)
计算机科学
数学
物理
人工智能
功率(物理)
经济
几何学
机器学习
量子力学
财务
作者
Wanjun Yin,Liang Wenbin,Jianbo Ji
出处
期刊:Energy
[Elsevier]
日期:2024-06-11
卷期号:304: 132061-132061
被引量:1
标识
DOI:10.1016/j.energy.2024.132061
摘要
In view of the negative influence of electric vehicle (EV) random charging on power grid load stability and charging cost of users, on the premise of guaranteeing users' travel demand, in this paper, an improved PSO model is proposed with the objective function of optimizing the daily load variance of the power grid and minimizing the charge cost of the vehicle owner. In this model, chaotic mapping is used to initialize the position of particles so that the particles are uniformly distributed in space and the diversity of particle solutions is increased, based on the global search ability and local search ability of the improved algorithm, combined with the analysis of a numerical example, the results show that the improved multi-objective PSO algorithm converges quickly and can jump out of the local optimum, better multi-objective optimization to reduce the peak-valley load difference and charging costs.
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