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首页> 外文期刊>Smart Grid, IEEE Transactions on >Data-Driven Approach for Analyzing Spatiotemporal Price Elasticities of EV Public Charging Demands Based on Conditional Random Fields
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Data-Driven Approach for Analyzing Spatiotemporal Price Elasticities of EV Public Charging Demands Based on Conditional Random Fields

机译：基于条件随机字段分析EV公共收费需求的时空价格弹性的数据驱动方法

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摘要

With the increase of electric vehicle (EV) sales, the pricing strategies of public charging stations have significant impacts on their revenues and the spatiotemporal distribution of charging loads. In this paper, we quantify three kinds of price elasticity of charging demands based on the historical charging data of multiple public charging stations with different pricing schemes. The relationship between the volume-weighted average price (VWAP) and the corresponding total charging demand within a zone is studied, which does not require changing the charging prices to estimate elasticity. To evaluate the shifting of charging demands in different periods and zones, a conditional random field (CRF) model is built, which depicts the spatiotemporal correlations of charging demands. In this model, the VWAPs and the total charging demands are taken as observed variables and hidden variables, respectively. The loopy belief propagation algorithm is used to infer the loopy graph approximately, and the learning algorithm with forgetting factors is used to estimate the unknown parameters of the CRF model. The price elasticities are derived from CRF, and the elasticity matrices of charging demands are obtained. Computational results based on historical charging data verify the validity of the proposed model and method.
机译：随着电动汽车（EV）销售的增加，公共收费站的定价策略对他们的收入和起飞率分布产生了重大影响。在本文中，我们根据具有不同定价方案的多个公共充电站的历史充电数据量化充电需求的三种价格弹性。研究了体积加权平均价格（VWAP）与区域内相应的总收费需求之间的关系，这不需要改变收费价格来估算弹性。为了评估不同时期和区域的充电需求的转移，构建了条件随机场（CRF）模型，描绘了充电需求的时空相关性。在该模型中，v酥子和总充电需求分别被视为观察到的变量和隐藏变量。用于缩进遗迹的遗传图谱算法，遗忘因子的学习算法用于估计CRF模型的未知参数。价格弹性来自CRF，获得充电需求的弹性矩阵。基于历史计费数据的计算结果验证了所提出的模型和方法的有效性。

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