Controlling the Charging of Electric Vehicles with Neural Networks
This work addresses grid stability issues for energy providers by incrementally applying neural networks to electric vehicle charging coordination.
The paper tackled the problem of coordinating electric vehicle charging to manage grid load by proposing decentralized neural network controllers, which successfully charged vehicles while maintaining peak consumption levels and reducing the difference between maximum and minimum grid consumption.
We propose and evaluate controllers for the coordination of the charging of electric vehicles. The controllers are based on neural networks and are completely de-centralized, in the sense that the charging current is completely decided by the controller itself. One of the versions of the controllers does not require any outside communication at all. We test controllers based on two different architectures of neural networks - the feed-forward networks and the echo state networks. The networks are optimized by either an evolutionary algorithm (CMA-ES) or by a gradient-based method. The results of the different architectures and the different optimization algorithms are compared in a realistic scenario. We show that the controllers are able to charge the cars while keeping the peak consumptions almost the same as when no charging is performed. Moreover, the controllers fill the valleys of the consumption thus reducing the difference between the maximum and minimum consumption in the grid.