We address day-ahead transmission topology planning and congestion management as a sequential, multi-objective optimization problem and develop two complementary algorithms for it: an exact enumeration method and a tailored evolutionary heuristic. The problem is formulated with four operational objectives reflecting real TSO decision criteria: worst-case line loading under $N-1$ security, topological depth, number of switching actions, and time spent in non-reference topologies, over a 24-hour horizon. We introduce the block algorithm, an exact method that exploits the temporal block structure of feasible strategies to enumerate the complete Pareto front; for fixed operational bounds on depth and switch count, its evaluation count grows polynomially with the planning horizon. We complement it with a multi-objective evolutionary algorithm based on NSGA-III, with structure-guided initialization and problem-specific variation operators tailored to the topology-planning structure. Using real operational data from the Dutch high-voltage grid operated by TenneT TSO, we show that the block algorithm computes the full Pareto front for a highly congested day in under three minutes, and that the evolutionary algorithm converges toward but does not recover the exact front. The block algorithm thus provides both a practical decision-support tool and a ground-truth benchmark for future heuristic and learning-based methods on this problem class.

翻译：针对日前输电拓扑规划及阻塞管理问题，本文将其建模为序贯多目标优化问题，并开发了两种互补算法：精确枚举法与定制化进化启发式算法。该问题以实际输电系统运营商（TSO）决策准则为基准，构建了反映24小时决策周期的四个运行目标：N-1安全准则下的最严重线路负载率、拓扑深度、开关动作次数及非参考拓扑运行时间。我们提出了区块算法——一种利用可行策略时序块结构的精确方法，可枚举完整帕累托前沿；在深度与开关次数运行边界固定的条件下，其评估次数随规划周期呈多项式增长。作为补充，我们基于NSGA-III框架设计了多目标进化算法，该算法通过结构引导初始化和面向拓扑规划结构的专用变异算子进行优化。基于TenneT TSO运营的荷兰高压电网实际数据验证表明：区块算法可在三分钟内计算高度拥堵日的完整帕累托前沿，而进化算法虽趋近但未恢复精确前沿。因此，区块算法既可作为实用决策支持工具，又能为这类问题的未来启发式与学习方法提供真实基准。