Two-Stage Stochastic Dynamic Unit Commitment and Its Analytical Solution With Large Scale Wind Power Integration

Abstract

Large-scale wind power integration brings great challenges to the secure and economic operation of power system. To cope with the uncertainty of wind power, this paper proposes a two-stage chance-constrained stochastic unit commitment model in which the wind power forecast error is described by a multi-dimensional Gaussian mixture model (GMM). The multi-dimensional GMM can accurately capture the characteristics of “multi-peak”, “asymmetric” and “multidimensional correlation In addition, a tractability transformation method is proposed and the chance constraints that cannot be directly handled are replaced by deterministic linear constraints approximatively. Therefore, the original problem can be solved efficiently with enough accuracy. The model proposed in this paper can effectively coordinates the master-problem of unit commitment and the sub-problem of generation-reserve dispatch. The optimal solution is an executable schedule, which avoids further modification and guarantees the optimality of generation cost. Compared with the two-stage robust unit commitment, the conservatism of its results is improved. Numerical tests on IEEE-118 system demonstrates the effectiveness of the proposed method.