EV charging load scheduling following uncertain renewable energy supply by stochastic matching

Renewable energy, such as wind power and solar energy, is becoming a major energy source. It is desirable to coordinate the uncertain supply and demand in the grid to make best use of the renewable energy and to ensure the stability of the grid. Electric vehicle (EV) is promising for its clean emission and elasticity of charging. However, the charging load of EVs is random by nature. In this paper, we consider EV load scheduling problem to match EV charging load with the stochastic wind energy supply in order to increase the wind power penetration. We formulate the stochastic matching problem as a constrained MDP model. The matching index is defined to measure the gap between the wind energy supply and EV demand, and used as the objective function, while the upper bound of the wind power penetration can be restricted in our model. The constrained MDP model for this scheduling problem is converted to an unconstrained MDP within a Lagrangian relaxation framework and dynamic programming is applied to derive the charging policy for the optimal matching. The numerical testing results show the effectiveness of the charging strategy on reducing the wind energy fluctuation to the grid.