Learning-Based Topology Optimization of Power Networks

Abstract

Transitions of power networks demand more powerful solution approaches for optimal transmission switching (OTS) and distribution network reconfiguration (DNR) to tackle complicated cases with high computational efficiency. In this paper, we propose a learning-based solution approach for OTS and DNR, which is established based on an identified common pattern behind existing heuristic algorithms. The original hand-crafted and short-sighted evaluation function is substituted by a parameterized function whose architecture is well-designed by mainly exploiting the gated graph neural network and multilayer perceptrons. For learning the parameterized function that lacks training labels, we further design a learning algorithm that combines the double deep Q-network algorithm and multi-step learning. Then, with the learned parameterized function, the learning-based heuristic algorithm is developed by embedding a feasibility recovery procedure. Finally, numerical studies demonstrate the superiority of the learning-based heuristic algorithm in both solution optimality and computational efficiency.