论文标题
随机依赖对网络约束筛查单位承诺的影响
Influence of Stochastic Dependence on Network Constraints Screening for Unit Commitment
论文作者
论文摘要
可再生能源的加深渗透在挑战电源系统运营商如何应对单位承诺问题的相关变异性和不确定性。鉴于其计算复杂性，已经提出了几种基于优化的方法来通过删除冗余线路限制来减轻整个单元承诺公式。这些方法通常忽略了多边可再生生成和需求的空间耦合。为了解决这一陷阱，我们通过添加约束来有效地模拟剩余需求变化的相关性，排除对原始单位承诺可行性区域的紧密线性编程放松的冗余约束。我们提出了一个新颖的，可进行的，可靠的多面体不确定性包络，该信封是由给定的一组场景引起的，以表征拧紧的约束。我们提出了一个数据驱动的雨伞约束发现问题制定，该问题大大增加了单位承诺中的网络约束过滤。在标准IEEE测试网络上进行数值测试以证实该方法的有效性。
The deepening penetration of renewable energy is challenging how power system operators cope with the associated variability and uncertainty in the unit commitment problem. Given its computational complexity, several optimization-based methods have been proposed to lighten the full unit commitment formulation by removing redundant line flow constraints. These approaches often ignore the spatial couplings of multi-side renewable generation and demand. To address this pitfall, we rule out redundant constraints over a tightened linear programming relaxation of the original unit commitment feasibility region by adding a constraint that efficiently models the correlation of residual demand variations. We set forth a novel, tractable and robust polyhedral uncertainty envelope induced by a given set of scenarios to characterize the tightening constraint. We propose a data-driven umbrella constraint discovery problem formulation that substantially increase the network constraints filtration in unit commitment. Numerical tests are performed on standard IEEE test networks to substantiate the effectiveness of the approach.
