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Abstract
Planning and operation of electric vehicle charging station (EVCS) have problems such as data loss, unqualified data instantaneity. Mass heterogeneous, polymorphism, multi-property, and hard-sharing data are expected to be used reasonably for optimal planning and operation of the stations. Charging station data are typically big data. However, requirements of easy low-cost implementation and integration with other systems conflict each other in nature. This paper focuses on this integration problem. Firstly, big data required by EVCS optimal planning and operation are analyzed, including data origination, characteristics and application challenges. Secondly, four integration patterns suitable for EVCS big data application are presented. Correspondingly, concepts, connotations of the four patterns and involved key technologies are elaborated. Moreover, a general architecture for EVCS big data integration is proposed based on these four patterns. Finally, examples are given for application of the four patterns.
... Data on various types of energy, including electricity, coal, petroleum, natural gas, and heat are collected during the processes of energy generation, transmission, storage, consumption, and trade, and are integrated using various energy big data techniques. In this manner, governments can supervise energy industries, societies can share energy information resources, and energy industries can conduct in-depth market reform [3,4]. In addition, open data-sharing forms the core of energy big data and is an efficient approach to facilitate the smart transformation and enhancement of energy systems. ...
Based on the relationship between traditional gas stations and electric vehicle charging stations in the automotive fuel market, the static Cournot model with profit maximization as the objective function and a dynamic one based on difference method are established to get the Cournot equilibrium of every fuel station. Numerical simulation and sensitivity analysis show feasibility and robustness of the model. All results acquired can be used in the electric vehicle charging stations and the traditional gas stations in the automobile fuel market for output policy guidance.
The load forecast of electric vehicles (EVs) is the foundation of planning and scheduling of charging stations. Compared with the traditional method, the load forecast method under big data has the feature that the data to be forecast is quickly observable, real time, etc. Hence the need of the corresponding adjustments of the load forecast methods. This paper first analyzes the data demand for charging station planning and scheduling, and then the ways of main data acquisition. Based on volume, variety, velocity data, each EV's start time, duration and location for charging, it will be possible to build the load model of a single EV. Furthermore, the total charging power of a charging station can be estimated by origin-destination (OD) flow statistics or adding up all the EV loads that are connected with its related transport line and node. Finally, a case study is given around the load forecast of EV station, and the load forecasting results from different load forecasting methods are compared.
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