标题： 基于顶点的机组组合冗余约束识别 显示英文标题

标题： Vertex-Guided Redundant Constraints Identification for Unit Commitment

摘要： 电力系统机组组合（UC）问题确定发电机的启停计划和调度决策，以实现电力网络的可靠和经济运行。 可再生能源和需求行为的日益渗透使得及时解决UC问题变得必要。 可以通过约束筛选来推导出轻量级、求解更快的UC模型，以消除冗余约束。 然而，由于需要求解大量线性规划（LP）问题，筛选过程仍然计算繁琐。 为了减少需要求解的LP数量，我们引入了一种对这种经典LP为基础的筛选的新观点。 我们的关键见解在于，冗余约束将被筛选后的可行区域的所有顶点满足。 利用通过求解更少LP得到的UC决策变量的边界，我们构建了一个外逼近作为筛选后的区域。 然后设计并应用一种矩阵运算到外逼近的顶点上，以实时识别所有冗余约束。 进一步探讨了对外逼近的调整，通过考虑负荷运行范围以及从UC成本和离散单元状态预测中得出的切割平面来提高筛选效率。 在最多2,383个母线的一组测试平台上进行了广泛的仿真，以验证所提出方案的有效性。 与经典的LP为基础的筛选相比，我们的方案可以在找到相同冗余约束的同时达到高达8.8倍的加速。 显示英文摘要

摘要： Power systems Unit Commitment (UC) problem determines the generator commitment schedule and dispatch decisions to realize the reliable and economic operation of power networks. The growing penetration of stochastic renewables and demand behaviors makes it necessary to solve the UC problem timely. It is possible to derive lightweight, faster-to-solve UC models via constraint screening to eliminate redundant constraints. However, the screening process remains computationally cumbersome due to the need of solving numerous linear programming (LP) problems. To reduce the number of LPs to solve, we introduce a novel perspective on such classic LP-based screening. Our key insights lie in the principle that redundant constraints will be satisfied by all vertices of the screened feasible region. Using the UC decision variables' bounds tightened by solving much fewer LPs, we build an outer approximation for the UC feasible region as the screened region. A matrix operation is then designed and applied to the outer approximation's vertices to identify all redundant constraints on-the-fly. Adjustments for the outer approximation are further explored to improve screening efficiency by considering the load operating range and cutting planes derived from UC cost and discrete unit status prediction. Extensive simulations are performed on a set of testbeds up to 2,383 buses to substantiate the effectiveness of the proposed schemes. Compared to classic LP-based screening, our schemes can achieve up to 8.8x acceleration while finding the same redundant constraints.