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The robust optimization method optimizes the performance while at the same time maximizing its belief value.
Sensitivity analyses explore the trade-off between optimization and robustness by varying the robust optimization parameter values.
In order to directly solve the robust optimization model in Eq. (1), two robustness indices are introduced to evaluate the objective and feasibility robustness of a design vector.
We then present a reformulation of the robust optimization problem using S-Procedure which enables us to obtain the globally optimal robust power control with fixed transmit beamforming.
Therefore, the robust optimization method is needed in this content.
It guarantees the feasibility for the robust optimization problem.
Then the robust optimization problem is simplified to a bilinear programming problem based on duality theory.
The MIMO STAP model is introduced, and the robust optimization problem is formulated in Section 2.
Then, the performances of the "robust optimization" approach and the "p-robust optimization" approach are evaluated.
The robust optimization approach reduces the effect of the fluctuations of uncertain parameters under certain scenarios.
Section 3 presents the robust optimization model for determining the forward dispatch schedule of wind farms.
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