A fixed-structure automaton for load management of electric vehicles

The uncontrolled charging of electric vehicles (EVs) imposes additional stresses on the grid. These stresses are set to increase due to the predicted increase in penetration of EVs. However, EV charging loads offer opportunities for controlling the actual demand to limit the peak demand or track variable generation to support the grid. This also can facilitate the integration of intermittent renewable energy generation in the near future. In this paper a learning automaton is proposed to manage EVs capable of on/off charging. We propose a new algorithm for distributed control of charging based on the broadcast of a congestion signal to regulate the aggregated demand. We show that the proposed algorithm converges to steady state operation, and analyse its implications on the distribution of power demand amongst the EVs. The potential of the proposed algorithm is illustrated by simulations for capping the aggregate demand of the EVs, and for tracking of slowly varying power generation signals.