  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.
