Energy big data: A survey <s> VOLUME 4, 2016 <s> We study the economic impact of a potential class of integrity cyber attacks, named false data injection attacks, on electric power market operations. In particular, we show that with the knowledge of the transmission system topology, attackers may circumvent the bad data detection algorithms equipped in today's state estimator. This, in turn, may be leveraged by attackers for consistent financial arbitrage such as virtual bidding at selected pairs of nodes. This paper is a first attempt to formalize the economic impact of malicious data attacks on real-time market operations. We show how an attack could systematically construct a profitable attacking strategy, in the meantime being undetected by the system operator. Such a result is also valuable for the system operators to examine the potential economic loss due to such cyber attack. The potential impact of the false data injection attacks is illustrated on real-time market operations of the IEEE 14-bus system. <s> BIB001 </s> Energy big data: A survey <s> VOLUME 4, 2016 <s> Malicious attacks against power systems are investigated, in which an adversary controls a set of meters and is able to alter the measurements from those meters. Two regimes of attacks are considered. The strong attack regime is where the adversary attacks a sufficient number of meters so that the network state becomes unobservable by the control center. For attacks in this regime, the smallest set of attacked meters capable of causing network unobservability is characterized using a graph theoretic approach. By casting the problem as one of minimizing a supermodular graph functional, the problem of identifying the smallest set of vulnerable meters is shown to have polynomial complexity. For the weak attack regime where the adversary controls only a small number of meters, the problem is examined from a decision theoretic perspective for both the control center and the adversary. For the control center, a generalized likelihood ratio detector is proposed that incorporates historical data. For the adversary, the trade-off between maximizing estimation error at the control center and minimizing detection probability of the launched attack is examined. An optimal attack based on minimum energy leakage is proposed. <s> BIB002 </s> Energy big data: A survey <s> VOLUME 4, 2016 <s> Malicious data attacks to the real-time electricity market are studied. In particular, an adversary launches an attack by manipulating data from a set of meters with the goal of influencing revenues of a real-time market. The adversary must deal with the tradeoff between avoiding being detected by the control center and making maximum profit from the real time market. Optimal attacking strategy is obtained through an optimization of a quasi-concave objective function. It is shown that the probability of detection of optimal attack will always be less than 0.5. Attack performance is evaluated using simulations on the IEEE 14-bus system. <s> BIB003 </s> Energy big data: A survey <s> VOLUME 4, 2016 <s> Cyber security is becoming an area of growing concern in the electric power industry with the development of smart grid. False data injection attack, which is against state estimation through SCADA network, has recently attracted wide research interests. This paper further develops the concept of load redistribution (LR) attack, a special type of false data injection attack. The damage of LR attacks to power system operations can manifest in an immediate or a delayed fashion. For the immediate attacking goal, we show in this paper that the most damaging attack can be identified through a max-min attacker-defender model. Benders decomposition within a restart framework is used to solve the bilevel immediate LR attack problem with a moderate computational effort. Its effectiveness has been validated by the Karush-Kuhn-Tucker (KKT)-based method solution in our previous work. For the delayed attacking goal, we propose a trilevel model to identify the most damaging attack and transform the model into an equivalent single-level mixed-integer problem for final solution. In summary, this paper enables quantitative analysis of the damage of LR attacks to power system operations and security, and hence provides an in-depth insight on effective attack prevention when resource budgets are limited. A 14-bus system is used to test the correctness of the proposed model and algorithm. <s> BIB004 </s> Energy big data: A survey <s> VOLUME 4, 2016 <s> Applications of cyber technologies improve the quality of monitoring and decision making in smart grid. These cyber technologies are vulnerable to malicious attacks, and compromising them can have serious technical and economical problems. This paper specifies the effect of compromising each measurement on the price of electricity, so that the attacker is able to change the prices in the desired direction (increasing or decreasing). Attacking and defending all measurements are impossible for the attacker and defender, respectively. This situation is modeled as a zero sum game between the attacker and defender. The game defines the proportion of times that the attacker and defender like to attack and defend different measurements, respectively. From the simulation results based on the PJM 5 Bus test system, we can show the effectiveness and properties of the studied game. <s> BIB005 </s> Energy big data: A survey <s> VOLUME 4, 2016 <s> 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. <s> BIB006 </s> Energy big data: A survey <s> VOLUME 4, 2016 <s> The real-time electrical market operations in smart grid require reliable and accurate data from state estimation. However, state estimation is vulnerable to data integrity attacks, in which strategically manipulated meter measurements can bypass the conventional bad data detection and introduce errors. As a result, it becomes more likely for the attackers to control real-time electrical market through manipulations of meter measurements. In this paper, we first reveal the intrinsic relations between data integrity attacks and real-time electrical market operations, and explicitly characterize their complex interactions as a process simulator. Then a simulation-based global optimization problem is formulated from which attackers could maximize financial incentives through constructed data integrity attacks. More importantly, a novel systematic online attack construction strategy is proposed, such that attackers can launch the desired attacks only by the real-time data streams of meter measurements and no power network topology or parameter information is needed. A corresponding online defense strategy is also presented to detect and identify the malicious measurements without extra meter hardware investments. Finally, we evaluate the performance of the proposed attacking strategies and countermeasure through numerical simulations in IEEE test systems with both synthetic and real data from the New York Independent System Operator. <s> BIB007
Several studies have been conducted to address integrity attack. Xie et al. BIB001 firstly investigated the impact of integrity attacks on energy market through virtual bidding. They showed the process of an attack constructing a profitable attacking strategy without being detected. This result is helpful to examine the potential loss caused by such attack. Kosut et al. BIB002 evaluated the proposed data attacks by generated market revenues and the work was further studied by Jia et al. BIB003 in maximizing the revenues. Yuan et al. BIB004 found that the data integrity attacks can result in increasing system operating cost due to the inordinate generation dispatch and energy routing. In order to control the realtime Locational Marginal Prices (LMP) directly under data attacks, Tan et al.  employed a control theory based approaches to analyze the attack effect on pricing stability. Esmalifalak et al. BIB005 adopted a novel approach to characterize the relationship between attackers and defenders. Tan et al. BIB007 proposed a novel systematic online attack construction strategy which doesn't need any power grid topology or parameter information. They presented a corresponding online defense strategy to identify the malicious attack in smart grid. Further, Jia et al. BIB006 proposed an analysis framework to quantify the data quality impact on real-time LMP. All these related researches are based on the assumption that attackers fully know about the targeted power systems including the grid topology architecture, branch parameters and other related information.