Abstract:To solve the problem of the uncertainty of charging sites and charging modes in forecasting electric vehicle spatial loads, a temporal and spatial distribution forecasting method based on origin-destination (OD) matrix and cloud model is proposed. First, through monitoring road traffic, the traffic attraction volume of the residential areas can be inversely deduced and the parking probability at different locations can then be dynamically predicted. Next, according to the characteristics of fast charge and slow charge, the conversion rules between users' psychology and fast charging probability can be formulated. Furthermore, the cloud model will be introduced in the rules to reflect the randomness and fuzziness of users' decision. Last, the load time curves of different charging sites will be analyzed and calculated via applying the Monte Carlo method, whose validity has been verified by data from an urban center city as an example. The calculation results show that the traffic volume changes apparently in different residential areas and different working days, and the charging load curve is significantly affected by the traffic volume change. The results also show that the fast charging load will fluctuate in a certain range randomly. Moreover, increasing the slow charge failure threshold will reduce the fast charging load peaks.
张晨彧, 丁明, 张晶晶. 基于交通出行矩阵的私家车充电负荷时空分布预测[J]. 电工技术学报, 2017, 32(1): 78-87.
Zhang Chenyu,Ding Ming,Zhang Jingjing. A Temporal and Spatial Distribution Forecasting of Private Car Charging Load Based on Origin-Destination Matrix. Transactions of China Electrotechnical Society, 2017, 32(1): 78-87.
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