A Distributionally Robust AC Network-Constrained Unit Commitment
A Distributionally Robust AC Network-Constrained Unit Commitment
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DOI：
10.1109/tpwrs.2021.3078801
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发表时间：
2021-11
影响因子：
6.6
通讯作者：
S. Dehghan;P. Aristidou;N. Amjady;A. Conejo
中科院分区：
文献类型：
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作者：
S. Dehghan;P. Aristidou;N. Amjady;A. Conejo
本文提出了一种分布式鲁棒网络约束机组组合模型（DR-NCUC），该模型考虑了交流网络建模以及需求和可再生能源产量的不确定性。该模型使用由训练样本构造的数据驱动的模糊度集来表征不确定参数。非凸交流潮流方程近似凸二次和麦考密克松弛。由于所提出的min-max-minDR-NCUC问题不能直接由现有的求解器，本文报道了一种新的分解算法的收敛性证明。该算法的主问题采用原割和对偶割求解，而最大最小子问题采用原-对偶混合梯度法求解，避免了使用对偶理论。同时，提出了一种有效集策略，通过忽略无效约束子集来提高分解算法的易处理性。将该模型应用于不同条件下的6节点测试系统和IEEE 118节点测试系统。这些案例研究说明了所提出的DR-NCUC模型的性能来表征不确定性和所提出的分解算法优于其他分解方法使用原始或双重削减的优越性。
This paper presents a distributionally robust network-constrained unit commitment (DR-NCUC) model considering AC network modeling and uncertainties of demands and renewable productions. The proposed model characterizes uncertain parameters using a data-driven ambiguity set constructed by training samples. The non-convex AC power flow equations are approximated by convex quadratic and McCormick relaxations. Since the proposed min-max-min DR-NCUC problem cannot be solved directly by available solvers, a new decomposition algorithm with proof of convergence is reported in this paper. The master problem of this algorithm is solved using both primal and dual cuts, while the max-min sub-problem is solved using the primal-dual hybrid gradient method, obviating the need for using duality theory. Also, an active set strategy is proposed to enhance the tractability of the decomposition algorithm by ignoring the subset of inactive constraints. The proposed model is applied to a 6-bus test system and the IEEE 118-bus test system under different conditions. These case studies illustrate the performance of the proposed DR-NCUC model to characterize uncertainties and the superiority of the proposed decomposition algorithm over other decomposition approaches using either primal or dual cuts.