Daline: A Data-driven Power Flow Linearization Toolbox for Power Systems Research and Education

Abstract

Power flow linearization has long been a fundamental tool in both academia and industry. While physics-driven power flow linearization (P-PFL) methods are relatively accessible, data-driven power flow linearization (D-PFL) approaches are significantly less so. As a promising field, D-PFL has demonstrated its potential to enhance linearization accuracy and address scenarios where traditional P-PFL methods are limited, particularly when physical parameters are unavailable. Despite their significance, over 95% of existing D-PFL algorithms lack publicly available codes, thereby limiting the utilization and comparison of the latest advancements in power flow linearization. To establish D-PFL methods into easily accessible fundamental tools, we developed Daline, a MATLAB-based, open-source toolbox that includes 57 linearization methods (42 D-PFL methods reproduced from existing literature, 11 D-PFL methods not reported before, and four widely used P-PFL methods), offering extensive flexibility in method selection and customization. Beyond linearization, Daline offers comprehensive functionality, ranging from data generation and processing, to model training, testing, and visualizable evaluation of accuracy or computational efficiency. These modules are grounded on Daline's comprehensive yet user-friendly architecture: with over 300 options, Daline caters to diverse needs with minimal coding, making complex tasks achievable with just one or two lines of code.