Learning a Non-linear Surrogate Model for Multistage Stochastic Transmission Planning

TL;DR

This paper introduces a hybrid machine learning-optimisation framework using neural network surrogates to improve the computational efficiency of multistage stochastic transmission planning, enabling large-scale analysis.

Contribution

It develops a novel surrogate modeling approach that approximates operational costs within stochastic TEP, significantly reducing computational time while maintaining near-optimal solutions.

Findings

Achieves up to 13 times reduction in computational time.

Maintains near-optimal investment costs in case studies.

Enables extensive scenario analysis previously computationally infeasible.

Abstract

Transmission expansion planning (TEP) plays a critical role in ensuring power system reliability and facilitating the integration of renewable energy resources. However, this process requires planners to constantly deal with significant uncertainty. While multistage stochastic TEP models provide a robust framework for identifying investment plans under uncertainty, the rapid growth in problem size hinders their computational tractability. To address this challenge, this paper develops a hybrid machine learning-optimisation framework for stochastic TEP. The proposed approach uses investment decisions and uncertainty scenarios as input features to train surrogate neural networks, which are then reformulated as mixed-integer linear constraints and embedded within an optimisation model. The surrogate model approximates expected operational costs to inform TEP decisions, reducing the burden arising from large operational problems. Case study applications on IEEE test systems demonstrate that, after training, the proposed approach achieves near-optimal investment costs while reducing total computational time by up to a factor of around 13 compared to a single full-optimisation stochastic formulation. This enables performing extensive multi-scenario analysis and stress testing that would otherwise be computationally prohibitive at scale.