Aggregated Load and Generation Equivalent Circuit Models with Semi-Empirical Data Fitting

TL;DR

This paper introduces a semi-empirical equivalent circuit modeling framework for aggregated electrical load and generation, enabling accurate, real-time simulations and compatibility with machine learning techniques.

Contribution

It presents a novel split equivalent circuit formulation that unifies various power flow analyses and facilitates data-driven parameter synthesis from measurements.

Findings

Models accurately represent transmission and distribution components.

Framework supports real-time simulation of time-varying loads and generation.

Compatible with machine learning for parameter fitting.

Abstract

In this paper we propose a semi-empirical modeling framework for aggregated electrical load and generation using an equivalent circuit formulation. The proposed models are based on complex rectangular voltage and current state variables that provide a generalized form for accurately representing any transmission and distribution components. The model is based on the split equivalent circuit formulation that was previously shown to unify power flow, three phase power flow, harmonic power flow, and transient analyses. Importantly, this formulation establishes variables that are analytical and are compatible with model fitting and machine learning approaches. The parameters for the proposed semi-empirical load and generation models are synthesized from measurement data and can enable real-time simulations for time varying aggregated loads and generation.