Electric Public Bus Load Model Based on Improved Kernel Density Estimation and Latin Hypercube Sampling
Miao Pengbin1, Yu Juan1, Shi Lefeng2, Liu Guoping2, Liang Ming1, Li Wenyuan1, Ren Zhouyang1
1. State Key Laboratory of Power Transmission Equipment & System Security and New Technology Chongqing University Chongqing 400044 China; 2. Chongqing Electric Power Research Institute Chongqing 401123 China
Abstract To build a more precise charging load model of electric public buses, the probability distribution and sampling method of charging state and charging start time are studied. Firstly, an adaptive kernel density estimation with boundary Kernel algorithm is proposed to build probability distribution models. The proposed algorithm does not require any assumptions about the probability distribution, and can also solve the problems of boundary bias and lacking of local adaptability, which improves the precision and adaptability of probability distribution. Secondly, Latin hypercube sampling with cubic spline interpolation algorithm is proposed to figure out the inverse cumulative distribution function of probability distribution. The proposed algorithm has high precision and sampling efficiency. Finally, based on these two algorithms, electric public bus charging load model is set up. The simulation result demonstrates the effectiveness and adaptability of the proposed method.
Miao Pengbin,Yu Juan,Shi Lefeng等. Electric Public Bus Load Model Based on Improved Kernel Density Estimation and Latin Hypercube Sampling[J]. Transactions of China Electrotechnical Society, 2016, 31(4): 187-193.
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