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In this paper, a model predictive control (MPC)-based coordinated scheduling framework for variable wind generation and battery energy storage systems (BESSs) is presented. On the basis of the short-term forecast of available wind generation and price information, a joint look-ahead optimization is performed by the wind farm and storage system to determine their net power injection to the electric power grid. In conjunction with moderate battery capacity, the excess unpredictable wind generation can be used to charge the battery storage and vice versa. The benefits of the proposed scheduling approach are that (1) the combined profit of wind generation and BESS is increased; (2) the net power injection from the wind farm into the power grid is smoothed out; and (3) the look-ahead optimization updates the price prediction in a moving horizon, which leads to more robust profit for wind farm and BESS against price uncertainties. By formulating the MPC-based coordinated scheduling as a quadratic programming problem, several numerically efficient algorithms to compute the optimal control strategy for wind generation and BESS are proposed. The effectiveness of the proposed algorithm in a modified IEEE 24-bus reliability test system with aggregated plug-in hybrid electric vehicles is demonstrated. It is shown that the proposed algorithm can increase the joint profit of wind farm and BESS while smoothing out the net power injection to the electricity grid. The proposed MPC-based scheduling problem can be solved in approximately 400 ms, which makes the framework implementable in realtime electricity market operations. [ABSTRACT FROM AUTHOR]