Impact of Load Models on Power Flow Optimization

TL;DR

This study investigates how different aggregated load models influence power flow optimization results, revealing that model choice significantly affects grid operating points and solution accuracy.

Contribution

The paper introduces a metric based on the maximum power transfer theorem to evaluate load model behavior in optimal power flow and demonstrates the impact of model selection using real-world data.

Findings

Different load models can represent the same power demand but lead to different operating points.

PQ load models accurately represent data behavior in only 16.7% of cases.

The proposed metric helps identify limitations of load models in OPF applications.

Abstract

Aggregated load models, such as PQ and ZIP, are used to represent the approximated load demand at specific buses in grid simulation and optimization problems. In this paper we examine the impact of model choice on the optimal power flow solution and demonstrate that it is possible for different load models to represent the same amount of real and reactive power at the optimal solution yet correspond to completely different grid operating points. We introduce the metric derived from the maximum power transfer theorem to identify the behavior of an aggregated model in the OPF formulation to indicate its possible limitations. A dataset from the Carnegie Mellon campus is used to characterize three types of load models using a time-series machine learning algorithm, from which the optimal power flow results demonstrate that the choice of load model type has a significant impact on the solution set points. For example, our results show that the PQ load accurately characterizes the CMU data behavior correctly for only 16.7% of the cases.