Fast AC Steady-State Power Grid Simulation and Optimization Using Prior Knowledge

TL;DR

This paper presents a globally convergent homotopy-based algorithm for fast, accurate AC steady-state power grid simulation and optimization, leveraging prior network knowledge to improve scalability and reliability in large-scale transmission studies.

Contribution

Introduces a novel homotopy method that uses prior network configurations for efficient and accurate AC power flow simulation and optimization, ensuring global convergence.

Findings

Effective for networks up to 70,000 buses

Demonstrates improved speed and accuracy over existing methods

Applicable to large-scale resiliency and optimization studies

Abstract

Fast and accurate optimization and simulation is widely becoming a necessity for large scale transmission resiliency and planning studies such as N-1 SCOPF, batch contingency solvers, and stochastic power flow. Current commercial tools, however, prioritize speed of convergence over accuracy by relying on initial conditions that are taken from the steady state solution of similar network configurations that are not guaranteed to lie within a convex region of a valid solution. In this paper we introduce a globally convergent algorithm to facilitate fast and accurate AC steady state simulation and optimization based on prior knowledge from similar networks. The approach uses a homotopy method that gradually and efficiently translates a previously known network configuration to the current network configuration. The proposed formulation is highly scalable, and its efficacy is demonstrated for resiliency study and optimization of large networks up to 70k buses.