A Fast Penalty-Based Gauss-Seidel Method for Stochastic Unit Commitment With Uncertain Load and Wind Generation
A Fast Penalty-Based Gauss-Seidel Method for Stochastic Unit Commitment With Uncertain Load and Wind Generation
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DOI：
10.1109/oajpe.2021.3079150
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发表时间：
2021
影响因子：
3.8
通讯作者：
Ananth M. Palani;Hongyu Wu;M. Morcos
中科院分区：
文献类型：
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作者：
Ananth M. Palani;Hongyu Wu;M. Morcos
随机网络约束机组组合（S-NCUC）可以有效地管理可再生能源渗透率不断提高的不确定性。然而，基于渐进对冲算法（PHA）的解决方案的缺点是，由于S-NCUC的非凸性，它们不能证明收敛。此外，获得的解决方案通常是分数值（非二进制），因此不容易实现。在本文中，我们应用一种新的基于惩罚的高斯-赛德尔（PBGS）算法求解S-NCUC使用一个精确的增广拉格朗日表示证明收敛。为了提高PBGS的计算效率，我们进一步提出了一个加速技术，严格证明跳过解决方案时，满足某些条件，在迭代过程中。我们数值验证了IEEE 118总线系统和一个实际规模的ERCOT样系统，无论是可变的风力发电所提出的算法。数值结果表明，所提出的算法在产生高质量的S-NCUC解决方案的有效性。与PHA和扩展形式（EF）的混合整数规划的解决方案相比，所提出的算法的优点也被揭示。
Stochastic network-constrained unit commitment (S-NCUC) can be used to manage the uncertainty of an increasing penetration level of renewable energy effectively. However, the drawbacks of the progressive hedging algorithm (PHA) based solutions are that they are not provably convergent due to the non-convexity of S-NCUC. Additionally, the solution obtained is usually fractional-valued (non-binary) and therefore not readily implementable. In this paper, we apply a novel Penalty-Based Gauss-Seidel (PBGS) algorithm in solving S-NCUC using an exact augmented Lagrangian representation with proven convergence. To improve the computational efficiency of the PBGS, we further propose an accelerating technique with rigorous proof to skip solving scenarios when certain conditions are met during iterations. We numerically validate the proposed algorithms on the IEEE 118-bus system and a practically-sized ERCOT-like system, both with variable wind generation. Numerical results demonstrate the efficacy of the proposed algorithms in yielding high-quality S-NCUC solutions. The merits of the proposed algorithms are also revealed in comparison with PHA and extensive-form (EF) based mixed-integer programming solutions.