modelSolver: A Symbolic Model-Driven Solver for Power Network Simulation and Monitoring

TL;DR

modelSolver introduces a symbolic, model-driven framework for power system analysis that simplifies defining complex models without programming, making advanced power network simulations more accessible to non-programmers.

Contribution

It presents a novel symbolic modeling paradigm that enables flexible, model-driven power system analysis tools without requiring traditional programming skills.

Findings

Supports complex power flow models with voltage regulators and load tap changers

Enables custom models using real or complex variables

Ensures compatibility with existing tools like MATPOWER

Abstract

The development of advanced software tools for power system analysis requires extensive programming expertise. Even when using open-source tools, programming skills are essential to modify built-in models. This can be particularly challenging for domain experts who lack coding proficiency. This paper introduces modelSolver, a software solution with a new framework centered around symbolic mathematical modeling. The proposed paradigm facilitates defining models through intuitive mathematical expressions, thus eliminating the need for traditional programming constructs such as arrays, loops, and sparse matrix computations. The modelSolver focuses on power flow and state estimation using an open-box approach, which allows users to specify custom models using either real or complex variables. Unlike existing tools that rely on hard-coded models, modelSolver enables the representation of a wide range of advanced functionalities, including power flow with voltage regulators and load tap changers, continuation power flow, and Gauss-Newton state estimation with equality constraints. Compatibility with MATPOWER is ensured via a converter that automates importing data files. The framework prioritizes model-driven development and empowers domain experts to focus on power system modeling without programming barriers. It aims to simplify power system computations, making them more accessible to students, scientists, and practitioners.