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

2026-05-05

AI Summary

This study addresses the multi-objective, time-coupled optimization problem in transmission topology planning and congestion management by incorporating practical constraints such as N-1 security, topological depth, number of switching operations, and duration of non-reference topologies. The problem is formulated as a 24-hour multi-objective optimization task. The authors propose an exact block algorithm based on a feasible-policy time-block structure, whose computational complexity grows polynomially with the planning horizon under fixed constraints. To enable efficient approximate solutions, they further design a structure-guided variant of NSGA-III. Evaluated on high-load-day data from TenneT in the Netherlands, the block algorithm computes the complete Pareto front within three minutes, providing a reliable benchmark for operational decision-making and subsequent heuristic or learning-based approaches.

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.

Problem

• transmission topology planning
• congestion management
• multi-objective optimization
• N-1 security
• Pareto front

Innovation

• block algorithm
• multi-objective optimization
• transmission topology planning
• Pareto front enumeration
• NSGA-III