More and more thermostatic loads in power systems are transformed into thermostatically controlled loads (TCLs) through the heterogeneous Internet of Things (IoT) devices. The proportion of TCLs exposed to the Internet has also further increased. Considering TCLs’ response characteristics and the network security vulnerability of their associated IoT devices, it is possible to form a new threat to power systems, manipulation of demand (MAD) attacks. In this article, we reveal a potential attack scenario of this threat in the electricity market and propose a highly concealable and destructive cross-domain MAD attack strategy based on compromised TCLs, causing a tremendous economic loss for the power retailer (PR) and end-use consumers (EUCs). Finally, the effectiveness of the proposed MAD attack strategy is verified by simulation cases, highlighting the endanger of large-scale compromised TCLs in power systems and the importance of their associated IoT devices’ network security.

The dynamic line rating (DLR) is recognized as an effective method to improve the integration of renewable energies, such as wind power by enhancing the flexibility of the system. The principle of DLR is to increase the transmission capacity of a transmission line according to the forecasted weather data. However, the uncertainties of DLR and wind power caused by the forecasting errors might bring in some severe consequences to the power system, such as line overloads. In this article, a tri-level preventive dispatch model is proposed to deal with this issue. To reduce the conservativeness of the solution, the density-based spatial clustering of application with noise method is employed to identify and remove the extreme scenarios of DLR. And a decomposition method is proposed to solve the tri-level model. In particular, a fast solution method by identifying the high-risk lines is also presented to obtain the optimal solution quickly. The numerical simulations on several IEEE bus systems demonstrate the effectiveness of the proposed model.

In false data injection (FDI) attacks, the attackers can impose security impacts by injecting false data into meter measurements to affect the security-constraint economic dispatch, which develops the dispatch for the end of a dispatch interval. Thus, the existing security analysis methods basically assess the attack impact based on a snapshot of system condition at the interval end, at which the dispatch target is achieved. Such analysis falls into a static analytical framework (SAF). However, with the increasing share of intermittent resources, the system is suffering short-term fluctuations, which may increase the variability and render security weak points inside of a dispatch interval. Smart attackers can judiciously exploit these weak points to impose large impacts on the Intra-Interval operational security. Such cyber risk may be underestimated under the SAF adopted by existing methods. To address such risk, a dynamic analytical framework is proposed in this article for analyzing the Intra-Interval operational security impacts of potential FDI attacks by considering the system variability in the dispatch interval. Simulations are performed based on the historical wind data on the IEEE 118-bus system, which verify the effectiveness of the proposed approach and highlight the risk of such issues in power systems with significant wind penetration.

A system disturbance can cause severe exceedances of the power system operating limits in which the transmission network overloads, if not being timely mitigated, can initiate cascading failures. So, an optimal and efficient postevent overload mitigation (PEOM) tool is necessary. However, research in the postevent mitigation is relatively limited as compared to that for the normal state and pre-event actions. Some existing dispatch methods consider the postcontingency scenario, but they do not fully address the postevent security and may lead to security issues such as power imbalance or violations of network security criterion. To address these issues and to improve the mitigation efficiency, this paper proposes a PEOM tool. PEOM implements a time-optimal load shedding for managing the power imbalance, ensures the time-dependent network security during the mitigation time period, and enhances the efficiency by prioritizing the mitigations. The simulations on the IEEE 118-bus and 2383-bus systems verify the effectiveness and efficiency of PEOM.

This paper analyzes a malicious data attack in which the attacker targets at the generation side of the system and aims to compromise the system security by causing large power imbalance in the real-time operations. Such an attack is called the ramp-induced data (RID) attack in the literature which revealed that the attacker can manipulate the limits of generator ramp constraints in real-time dispatch (RTD) and, thus, impact the power market operations. In this paper, we propose an optimal attack model to analyze the impact of the RID attack on power system operational security and show that the attacker can introduce large power imbalance into real-time operations that can cause security issues or even catastrophic consequences. To address the risk of such an attack, a countermeasure is presented that reassesses the regulation reserve adequacy against a given risk level of the attack. Simulations on the IEEE 118-bus system verify the impact of the proposed RID attack and the effectiveness of the regulation assessment approach.

Like the Internet in the computer industry that shifted from a centralized mainframe to a client-based structure, “energy Internet” was proposed to involve the customers and incorporate renewable energy. To empower such bidirectional communication and power management, the power grid deploys massive smart meters and wireless communication techniques. In this context, false data attacks are more likely to occur in an energy Internet system, and considerable imprecision could be caused for the state estimator. In this paper, resilient and real-time state estimation is proposed. First, we investigate the mechanism of the stealthy false data attack and the countermeasures. Then, based on the accurate and reliable pseudovoltage measurements, a changed measurements-based sequential state estimation is introduced. Finally, both simulation results and practical implementation demonstrate the efficacy and efficiency of the proposed state estimation method for the energy Internet.

In past studies on cascading failure assessment, the risk of N - x initiating contingencies (ICs) regarding their initiating effect and hidden contingency risk (a high-risk x-component outage sequence within the IC) may not be fully investigated, which may underestimate the risk of IC-triggered cascading overload failures (COFs). In addition, the research on the efficient screening of N - x IC (x ≥ 2) and the identification of the cascading with the largest system impact is limited. To undertake this challenge, this paper proposes a COF identification approach (CIA) which integrates the IC screening and the COF identification for finding the high-impact COFs initiated by N - x ICs with hidden risks. The CIA is validated by simulations on the IEEE 118-bus system. The simulation results demonstrate that the CIA can efficiently screen out the high-risk N - 2 and N - 3 ICs that have an optimal outage sequence and trigger COF chains. The results also show that the CIA can identify the IC-triggered high-impact COFs and quantifies their impacts. The approach can be used as either a cascade-triggering event screening tool (by applying the IC screening module) or a COF risk assessment tool for ensuring system reliability.

In this paper, we address the risk of hidden N-k line contingencies in power systems by proposing a preventive mitigation strategy to mitigate these hidden contingencies. The strategy comprises a screening model, an evaluation model, and a mitigation cut generating mechanism. Using this strategy, we first screen out the high-risk hidden N-k line contingencies using the screening model, and then mitigate them by iteratively reducing their hidden probabilities assessed in the evaluation model. This is achieved by incorporating mitigation cuts into an enhanced security constrained economic dispatch module, while taking into consideration both reliability and economic criteria. This mitigation strategy can be employed as a preventive action by the system operators. The simulations on the IEEE 118-bus system demonstrate that the proposed approach can reduce the hidden probabilities of these high-risk contingencies below a given threshold with a reasonable increase in the generation cost, and, thus, verify the approach as an effective tool to address the risk of these contingencies.

Today's power systems become more prone to cyber-attacks due to the high integration of information technologies. In this paper, we demonstrate that the outages of some lines can be masked by injecting false data into a set of measurements. The success of the topology attack can be guaranteed by making that: 1) the injected false data obeys Kirchhoff current law and Kirchhoff voltage law to avoid being detected by the bad data detection program in the state estimation and 2) the residual in the line outage detection is increased such that the line outage cannot be detected by phasor measurement unit data. A bilevel optimization problem is set up to determine the optimal attack vector that can maximize the residual of the outaged line. The IEEE 39-bus and 118-bus systems are used to demonstrate the masking scheme.

Power network is one of the most critical infrastructures in a nation and is always a target of attackers. Recently, many schemes are proposed to protect the security of power systems. However, most of existing works did not consider the component attacking cost and ignored the relationship between the budget deployed on the component and its attacking cost. To address this problem, in this paper we introduce the concept of budget-cost function, which describes the dynamic characteristics of component attacking cost, and propose a new model to protect power grid against intentional attacks. In our model, the attackers have limited attacking capacity and aim to maximize the damage of attacks. On the other hand, the defenders aim to find the optimal strategy of the budget deployment to limit the damage to an expected level. We formulate the above problem as a nonlinear optimization problem and solve it by employing the primal-dual interior-point method. To the author's best knowledge, this is the first work which analyzes the optimal budget deployment strategy based on budget-cost function. Simulations on the IEEE 5-bus system demonstrate the correctness and effectiveness of the proposed model and algorithms. The results provide a basis of budget investment for power systems.