基于交通出行矩阵的私家车充电负荷时空分布预测

发布时间：2018-08-15 18:09

【摘要】：针对电动汽车空间负荷预测中充电地点、充电方式不确定性的难题,提出了一种基于交通出行矩阵和云模型的充电负荷时空分布预测方法。首先,通过监测道路流量,反推小区的交通吸引量,动态预测不同地点的停车概率。其次,在选择充电方式时,根据快充、慢充特点,制定用户心理到快充概率之间的转换规则,并在规则中引入云模型,体现用户决策的随机性和模糊性。最后,利用蒙特卡洛法分析计算出不同充电地点的负荷时间曲线,并以某城市中心城区的数据为例,验证了该方法的有效性。计算结果表明,不同小区、不同工作日的交通量变化明显,且充电负荷曲线受交通量变化的影响显著;快充负荷将在一定范围内随机波动,提高慢充失效阈值将减小快充负荷峰值。
[Abstract]:In order to solve the problem of uncertainty of charging location and charging mode in space load forecasting of electric vehicles, a new method based on traffic travel matrix and cloud model is proposed to predict the temporal and spatial distribution of charging load. Firstly, the parking probability of different locations is predicted dynamically by monitoring the road flow and pushing back the traffic attraction of the residential area. Secondly, according to the characteristics of fast charging and slow charging, the conversion rules from user psychology to quick charge probability are formulated, and cloud model is introduced into the rules to reflect the randomness and fuzziness of user decision. Finally, the load time curves of different charging sites are calculated by Monte Carlo method, and the validity of this method is verified by taking the data of a city central city as an example. The results show that the traffic volume changes obviously in different residential areas, and the charge load curve is significantly affected by the traffic volume change, and the fast charge load will fluctuate randomly in a certain range. Increasing the failure threshold of slow charge will reduce the peak value of fast charge load.
【作者单位】：安徽省新能源利用与节能重点实验室(合肥工业大学);
【基金】：国家高技术研究发展计划(863计划)资助项目(2011AA05A107)
【分类号】：TM715

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