In-depth understanding of the multi-energy consumption behavior pattern is the essential to improve the management of multi-energy system (MES). This paper proposes a data-driven three-stage adaptive pattern mining approach for multi-energy loads, which addresses the issues of complex multi-dimensional time-series mining, uncommon daily loads discovery, typical load classification and parameter setting requiring user intervention. In the first stage, the relative state changes over time between different energy loads are excavated based on Autoplait, which realizes time pattern discovery, segmentation and match for multi-dimensional loads. In the second stage, adaptive affinity propagation (AAP) considering trend similarity distance (TSD) is proposed to classify loads into common and uncommon clusters, where uncommon loads are eliminated and daily pattern is obtained by taking average of common loads. In the third stage, AAP with windows dynamic time warping (WDTW) identifies various profiles to obtain typical pattern of daily loads. Specifically, pattern mining provides the key information of multi-energy loads, which is significant to the applications for the demand side, such as load scene compression, load forecasting and demand response analysis. A case study uses MES data from Arizona State University to verify the effectiveness and practicality of the proposed approach.

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.

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.

This paper presents a new anti-islanding protection (AIP) scheme for inverters in integration of distributed generation (DG) sources into the grid. The main advantage of this proposed AIP scheme is that it can be embedded into the control of DG inverters without a need of additional part in the hardware of the inverter and any change in the controller. The AIP scheme does not rely on a communication network. It detects the islanding condition based on continuous monitoring of the active power, the reactive power, and the voltage magnitude mismatch, which is shown to be an alternative to the grid impedance measurement technique. The AIP scheme isolates the inverter from the grid after the grid experiences an islanding condition for all possible scenarios of islanding. A detailed modeling of its implementation in a DG inverter is presented and the performance of the AIP scheme is verified by simulations and experiments.

This paper presents a control-loop stability analysis and parameter design of the grid-connected virtual synchronous machine (VSM) inverters that enables the VSM operate in a very weak grid condition. In order to study the stability and design the parameters, a line-frequency-averaged small-signal model of the VSM inverter has been derived. A transfer function of the control-loop has been obtained, including the coupling resulting from the inherent nature of the VSM as both active power and reactive power related to the voltage and frequency. An example of parameter design is presented by using the derived model. It is shown that the designed parameters ensure the stability and robustness of the VSM inverters connected into a weak grid with a wide range of the grid impedance variation.

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.

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.