A Feasible Reduced Space Method for Real-Time Optimal Power Flow

TL;DR

This paper introduces a feasible-path algorithm for real-time optimal power flow that maintains feasibility at each step, leveraging second-order derivatives and GPU acceleration for efficiency.

Contribution

It presents a novel reduced space method that directly enforces physical constraints and is suitable for real-time applications, unlike traditional interior-point methods.

Findings

The method maintains feasibility at each iteration.

GPU acceleration makes second-order computations tractable.

Effective for both static and real-time OPF problems.

Abstract

We propose a novel feasible-path algorithm to solve the optimal power flow (OPF) problem for real-time use cases. The method augments the seminal work of Dommel and Tinney with second-order derivatives to work directly in the reduced space induced by the power flow equations. In the reduced space, the optimization problem includes only inequality constraints corresponding to the operational constraints. While the reduced formulation directly enforces the physical constraints, the operational constraints are softly enforced through Augmented Lagrangian penalty terms. In contrast to interior-point algorithms (state-of-the art for solving OPF), our algorithm maintains feasibility at each iteration, which makes it suitable for real-time application. By exploiting accelerator hardware (Graphic Processing Units) to compute the reduced Hessian, we show that the second-order method is numerically tractable and is effective to solve both static and real-time OPF problems.