Input Convex Neural Networks for Optimal Voltage Regulation

TL;DR

This paper introduces an input convex neural network that learns the voltage-to-injection mapping for optimal reactive power control, enabling fast, topology-agnostic voltage regulation in distribution networks with renewables.

Contribution

It proposes a novel neural network architecture that combines learning and optimization for voltage regulation without requiring system topology or parameters.

Findings

The neural network accurately models voltage-injection relationships.

The method achieves near-optimal reactive power control in simulations.

The approach is scalable and adaptable to different distribution systems.

Abstract

The increasing penetration of renewables in distribution networks calls for faster and more advanced voltage regulation strategies. A promising approach is to formulate the problem as an optimization problem, where the optimal reactive power injection from inverters are calculated to maintain the voltages while satisfying power network constraints. However, existing optimization algorithms require the exact topology and line parameters of underlying distribution system, which are not known for most cases and are difficult to infer. In this paper, we propose to use specifically designed neural network to tackle the learning and optimization problem together. In the training stage, the proposed input convex neural network learns the mapping between the power injections and the voltages. In the voltage regulation stage, such trained network can find the optimal reactive power injections by design. We also provide a practical distributed algorithm by using the trained neural network. Theoretical bounds on the representation performance and learning efficiency of proposed model are also discussed. Numerical simulations on multiple test systems are conducted to illustrate the operation of the algorithm.