Abstract
From the customer's perspective, the appeal of electric vehicles depends on the simplicity and ease of their use, such as flexible access to electric power from the grid to recharge the batteries of their vehicles. Therefore, the expansion of charging infrastructure will be an important part of electric mobility. The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs. This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques. The module will be integrated into a real charge management concept with optimization procedures for controlling the dynamic load point. The value of load forecasting through practical load data of a car park were taken to illustrate the proposed methods. The prediction performance of different ML methods under the same data condition (e.g., holiday data) are compared and evaluated.
Highlights
The uncontrolled charging of xEVs might increase the system’s peak demand and overload transformers
The related charging infrastructure is a big challenge for the load capacity of the grid connection without additional intelligent charge management: if the control of the charging process is not implemented, it is necessary to ensure the total of the maximum output of all xEVs at the grid connection point, which requires huge costs
This paper proposes to build a prediction module for forecasting dynamic charging load using machine learning (ML) techniques
Summary
The uncontrolled charging of xEVs might increase the system’s peak demand and overload transformers. It would be conceivable to provide a user interface at the charging pile, through which the desired departure time, energy quantity and other information can be exchanged Based on this and the underlying load predictions, a charging prioritisation of the individual xEVs in the event of an increased load volume can be controlled according to the situation. Costs for network expansion in conjunction with the expansion of upstream and downstream charging infrastructure through connection services, charges and transformers could be kept to a minimum. This approach directly benefits distribution network operators, property operators and even end consumers by harmonising their mobility needs. For super-short-term forecasting with deep learning, the long-short-term memory (LSTM) already showed very realistic results as described in [5]
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