Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning

• URL: http://arxiv.org/abs/2605.03753v1
• Date: Tue, 05 May 2026 13:38:30 GMT
• Title: Exact and Evolutionary Algorithms for Sequential Multi-Objective Transmission Topology Planning
• Abstract summary: Day-ahead transmission topology planning and congestion management is a sequential, multi-objective optimization problem.<n>We develop two complementary algorithms for it: an exact method and a tailored evolutionary enumeration.
• Score: 39.32074713418988
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 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.

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