A Framework for Analyzing the Impact of Data Integrity/Quality on Electricity Market Operations

This dissertation examines the impact of data integrity/quality in the supervisory control and data acquisition (SCADA) system on real-time locational marginal price (LMP) in electricity market operations. Measurement noise and/or manipulated sensor errors in a SCADA system may mislead system operators about real- time conditions in a power system, which, in turn, may impact the price signals in real-time power markets. This dissertation serves as a first attempt to analytically investigate the impact of bad/malicious data on electric power market operations. In future power system operations, which will probably involve many more sensors, the impact of sensor data integrity/quality on grid operations will become increasingly important.

The first part of this dissertation studies from a market participant?s perspective a new class of malicious data attacks on state estimation, which subsequently influences the result of the newly emerging look-ahead dispatch models in the real-time power market. In comparison with prior work of cyber-attack on static dispatch where no inter-temporal ramping constraint is considered, we propose a novel attack strategy, named ramp-induced data (RID) attack, with which the attacker can manipulate the limits of ramp constraints of generators in look-ahead dispatch. It is demonstrated that the proposed attack can lead to financial profits via malicious capacity withholding of selected generators, while being undetected by the existing bad data detection algorithm embedded in today?s state estimation software.

In the second part, we investigate from a system operator?s perspective the sensitivity of locational marginal price (LMP) with respect to data corruption-induced state estimation error in real-time power market. Two data corruption scenarios are considered, in which corrupted continuous data (e.g., the power injection/flow and voltage magnitude) falsify power flow estimate whereas corrupted discrete data (e.g., the on/off status of a circuit breaker) do network topology estimate, thus leading to the distortion of LMP. We present an analytical framework to quantify real-time LMP sensitivity subject to continuous and discrete data corruption via state estimation. The proposed framework offers system operators an analytical tool to identify economically sensitive buses and transmission lines to data corruption as well as find sensors that impact LMP changes significantly.

This dissertation serves as a first step towards rigorous understanding of the fundamental coupling among cyber, physical and economical layers of operations in future smart grid.