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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.

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In order to support large scale integration of wind power, state-of-the-art wind speed forecasting methods should provide accurate and adequate information to enable efficient scheduling of wind power in electric energy systems. In this article, space-time wind forecasts are incorporated into power system economic dispatch models. First, we proposed a new space-time wind forecasting model, which generalizes and improves upon a so-called regime-switching space-time model by allowing the forecast regimes to vary with the dominant wind direction and with the seasons. Then, results from the new wind forecasting model are implemented into a power system economic dispatch model, which takes into account both spatial and temporal wind speed correlations. This, in turn, leads to an overall more cost-effective scheduling of system-wide wind generation portfolio. The potential economic benefits arise in the system-wide generation cost savings and in the ancillary service cost savings. This is illustrated in a test system in the northwest region of the U.S. Compared with persistent and autoregressive models, our proposed method could lead to annual integration cost savings on the scale of tens of millions of dollars in regions with high wind penetration, such as Texas and the Northwest.