Impact of Data Quality on Real-Time Locational Marginal Price

TL;DR

This paper analyzes how data quality issues, such as bad data, impact real-time locational marginal prices in power systems, using geometric analysis and simulations on standard networks.

Contribution

It introduces a geometric framework to characterize the impact of bad data on real-time LMP and analyzes worst-case scenarios under different data corruption models.

Findings

Power system state space is partitioned into convex polytope regions.

Worst-case bad data impacts on LMP are characterized.

Simulations demonstrate the theoretical findings on IEEE networks.

Abstract

The problem of characterizing impacts of data quality on real-time locational marginal price (LMP) is considered. Because the real-time LMP is computed from the estimated network topology and system state, bad data that cause errors in topology processing and state estimation affect real-time LMP. It is shown that the power system state space is partitioned into price regions of convex polytopes. Under different bad data models, the worst case impacts of bad data on real-time LMP are analyzed. Numerical simulations are used to illustrate worst case performance for IEEE-14 and IEEE-118 networks.