We study day-ahead transmission topology control for high-voltage grid operation under $N-1$ security constraints. The operational task is to select, over a 24-hour horizon, a sequence of substation topologies obtained via busbar-coupler switching to relieve line overloads while limiting switching effort and topological complexity. We formulate this task as a sequential multi-objective optimization problem with four objectives used in TSO decision making: worst-case $N-1$ line loading, maximum topological depth, number of topology changes, and time spent outside the reference topology. We propose an exact block algorithm that exploits the temporal structure of topology plans: consecutive hours with the same topology are represented as blocks, enabling enumeration of the complete Pareto front over the admissible set of topologies under fixed operational bounds on depth and switching. We also develop a tailored NSGA-III-based evolutionary heuristic and evaluate it against the exact front. Using real operational data from the Dutch high-voltage transmission grid operated by TenneT, the block algorithm computes the exact front for a highly congested day in under three minutes after topology-level load-flow preprocessing. The exact front reveals low-switching plans with no DC $N-1$ thermal overloads that the tested evolutionary search fails to find. The proposed method, therefore, provides both a practical day-ahead decision-support tool for transmission operators and a benchmark for heuristic and learning-based topology-control methods.

翻译：我们研究了在$N-1$安全约束下，面向高压电网日前运行的输电拓扑控制问题。运行任务是在24小时的时间范围内，通过母线耦合器切换选择一系列变电站拓扑，以缓解线路过载，同时限制切换动作次数与拓扑复杂度。我们将该问题建模为含四个目标的顺序多目标优化问题，这些目标用于输电系统运营商（TSO）的决策：最差情况下的$N-1$线路负载率、最大拓扑深度、拓扑变化次数以及偏离参考拓扑的持续时间。我们提出了一种精确分块算法，该算法利用拓扑规划的时间结构：将连续且拓扑相同的小时表示为分块，从而能够在深度和切换次数的固定运行约束下，枚举可行拓扑集上的完整帕累托前沿。我们还开发了基于NSGA-III的定制化进化启发式算法，并将其结果与精确前沿进行对比。利用荷兰TenneT运营的高压输电电网的实际运行数据，在完成拓扑级潮流预处理后，该分块算法可在三分钟内计算出一个高拥堵日的精确帕累托前沿。该精确前沿揭示了一些低切换次数的规划方案，这些方案不存在直流$N-1$热过载问题，但测试的进化搜索却未能找到。因此，所提方法既为输电运营商提供了实用的日前决策支持工具，也为基于启发式与学习方法的拓扑控制研究提供了基准。