Transient Stability-Constrained OPF: Neural Network Surrogate Models and Pricing Stability

TL;DR

This paper introduces a neural network-based surrogate model for transient stability-constrained optimal power flow, enabling efficient and cost-effective frequency stability enforcement in power systems, with implications for market pricing and system reliability.

Contribution

It proposes a novel model-driven active sampling algorithm for training neural networks to accurately predict stability boundaries in power flow problems.

Findings

NN surrogate models effectively predict stability boundaries.

TSC-OPF enhances frequency stability with low computational and financial costs.

Certain NN input choices enable stabilization of all unstable load scenarios.

Abstract

A Transient Stability-Constrained Optimal Power Flow (TSC-OPF) problem is proposed that enforces frequency stability constraints using Neural Network (NN) surrogate models. NNs are trained using a novel model-driven active sampling algorithm that iteratively generates NN training data located near the stability boundary and contained within the feasible set of the Alternating Current Optimal Power Flow (AC-OPF) problem. In the context of wholesale electricity markets, pricing structures are analyzed along with their dependencies on the selected input features to the NN surrogate model. An important insight identifies a trade-off between the accuracy of the NN surrogate model and sensible locational pricing structures. NN surrogate models for frequency stability are validated by ensuring the resulting TSC-OPF solution is stable over randomly generated load samples using a small Hawaii test case. The proposed TSC-OPF problem is shown to significantly enhance frequency stability at low computational cost and low financial cost to the system. For certain selections of NN inputs, the TSC-OPF problem is able to stabilize all load scenarios for which the solution to the AC-OPF problem resulted in instability.