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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. [ABSTRACT FROM AUTHOR]

Modern power system faces high risks of multi-period uncertain attacks, call for effective countermeasures. In this paper, we propose a hybrid robust tri-level defender–attacker–defender (DAD) model to mitigate the multi-period attack risk by minimizing the expected load loss for the power system. The proposed model integrates the N-K criterion and attack scenario uncertainties to provide a resilient defense strategy. It also considers the optimal allocation of offensive resources during multi periods of an attack to develop a more practical defense to develop a more practical defense strategy. The column-and-constraint generation algorithm is applied to derive its optimal solutions. Extensive case studies using the IEEE RTS79 and RTS96 systems are conducted to validate the effectiveness of the proposed approach. [ABSTRACT FROM AUTHOR]

An incentive-compatible demand response (DR) strategy is proposed for engaging spatially-coupled Internet data center (IDC) and their spatial load regulation potentials in electricity markets. First, an optimal power flow (OPF) model which considers IDC (referred to as IOPF) is proposed to coordinate IDC DRs and power system operations, in which the formulated IDC load model is compatible with the conventional OPF model. Second, the IOPF-based locational marginal price (LMP) (referred to as ILMP) is derived, which is used to analyze the impact of spatially-coupled DR options on ILMPs and the IDC DR’s clearing price. Third, IDC DR activation strategy is proposed as an extension of Net Benefit Test (NBT), where the risk of negative benefit to IDCs is derived. An IDC DR’s activation strategy is proposed based on NBT to determine whether non-zero IDC DR dispatches in IOPF are cost-effective. Last, an extra benefit redistribution mechanism is proposed to achieve the incentive-compatibility between social welfare and IDC benefit. The proposed approach is based on the Vickrey–Clarke–Groves mechanism and the contribution factor theory, where the market’s revenue adequacy is maintained, and benefits to IDCs and other customers are guaranteed. Simulation results verify the efficiency of the proposed method, implying that the proposed spatially-coupled DRs are compatible with and can enhance existing DR mechanisms. [ABSTRACT FROM AUTHOR]

The coordination of multiple coupled regulation methods in Internet data centers (IDCs) is proposed in this article to make a full use of IDCs’ spatial and temporal load regulation potentials for demand response (DR). A concise and analytical IDC load model is proposed to facilitate the DR in IDCs. First, the unified IDC load considering multiple coupled regulation methods is modeled represented by workloads and servers. The extra power consumption in IT equipment due to redundancy requirements in computing resources is separated to obtain an explicit mathematical relationship among geo-distributed IDCs. Second, the compatible IDC load model is derived based on a bottom-up approach, where electrical decision variables representing the regulation are introduced to replace non-electrical variables and demonstrate the coupling among multiple regulation methods. Last, a hierarchical control design is deduced to reveal the potentials of the compatible IDC load model in power system applications. Simulations show that the joint implementation of coupled methods can enhance IDC DR capabilities. Simulation results also verify the efficiency of compatible IDC load model in DR and reveal its advantages in load characteristic analyses, computational efficiency, and sensitive information protection of IDCs. [ABSTRACT FROM AUTHOR]

Load data is an essential input for power system analysis and management. However, traditional load analysis methods do not fit well with emerging deep learning models that may require image matrix as input, such as Convolutional Neural Network (CNN) and Generative Adversarial Nets (GAN). This article proposes a novel analysis method “Load Photo” to create the required image matrix for various loads in power systems by using the HSV color space. A load photo is a 2-D pixel graphical representation of load, in which the x-axis represents sampling time points (e.g., hours) for every day from Sunday to Saturday, the y-axis represents weeks from the first week till the last week, and the pixel color demonstrates the normalized load. A load photo stores all essential load-related parameters such as start time, end time, and sampling period. A load photo can be used to characterize the energy consumption pattern of any load type, just like taking a photo for the load that clearly demonstrates the load’s behavior. Furthermore, a load photo could work before feature selection as an input to deep learning models. Accordingly, the latest advancement in deep learning models related to image processing can be easily applied to the analysis and data mining of load data. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

The integration of information and network technologies has significantly boosted the efficiency of electric power delivery, but at the same time poses new cyber threats to a power grid. An attacker can compromise meter readings by injecting false data. It was revealed that such false data is able to escape the traditional bad data detection once it obeys the physical laws of the system. However, in this letter we demonstrate that such false data usually can be treated as an outlier, which can be effectively detected by several anomaly detection methods. Unfortunately, we further demonstrate that such false data can be stealthy through hiding among normal data if it is carefully designed. Such false data is defined as dummy data, whose stealthiness is validated by the simulations on the IEEE 24-bus and IEEE 30-bus systems. [ABSTRACT FROM AUTHOR]

Cascading failures in power grids are typically triggered by initiating contingencies (ICs). Traditional assumption is that those multiple-component ICs have low chances of occurring. However, considering the increasing cyber events in power grids, this brief reveals that smart attackers can target at critical ICs and implement well-designed cyber attacks to highly increase the ICs’ occurrence probabilities by overloading key branches in the grid. This will greatly increase the grid vulnerability to cascading failures. In this brief, such issue is revealed and analyzed using a two-step approach, which consists of a high-probability IC screening and a deterministic cascading testing. The simulation results on the IEEE 118-bus systems verify the effectiveness of the proposed approach and highlight the increased risk imposed by cyber-attacks. [ABSTRACT FROM AUTHOR]

Security-constrained economic dispatch is executed based on consecutive dispatch intervals in real time. The short-term variations of wind power can cause the system to enter an unsecured operating region inside a dispatch interval, which is referred to as the intra-interval security (IIS) problem in this paper. IIS may not be addressed by existing dispatch methods as the traditional dispatch is to ensure the security at the interval end. This can lead to IIS risks in an interval when a high-wind-penetrated system encounters large intra-interval variations (IIVs) of wind power. To address this issue, an IIS preventive dispatch approach is proposed in this study. This approach models the wind power IIVs and develops a preventive dispatch to ensure the IIS over the interval in terms of line overloads while taking into account the tradeoff between security and economics. The proposed approach is implemented in a linear-programming model whose global optimum can be efficiently obtained by adding Benders-like cuts. Case study on the IEEE 118-bus system proves that a high level of nonlinear IIVs can cause the IIS issue (inside of an interval) even with accurate wind forecasts at both interval ends. The simulations show that some lines can have severe overloads inside the dispatch interval in spite of having flows below their capacity limits at both interval ends, and demonstrate that the proposed approach can mitigate such intra-interval overloads even under the worst case IIV scenario. [ABSTRACT FROM AUTHOR]

Cyber-attack is recognized as an emerging risk in smart grids. In this paper, we reveal a potential link between data attack and physical consequences and analyzed how the attacker can launch a malicious data attack to trigger sequential outages and thus impose large damages to the grid. In this attack mechanism, the attacker constructs an optimal false data injection attack to intentionally trigger a targeted branch outage sequence that trips multiple branches and then leads to subsequent failures. The studied attack mechanism integrates constructing an optimal data attack and identifying critical lines, and imposes a substantial security impact with a high chance of occurring. Simulations on the IEEE 118-bus system verify that the introduced attack mechanism and highlight the risk of such attacks in today's smart grids. [ABSTRACT FROM AUTHOR]

It was revealed that modern power systems are at high risk of cyber-attacks, as an attacker can stealthily execute false data injection attacks against the state estimation without knowing the full topology and parameter information of the entire power network. To mitigate the risk, in this paper, we propose a bilevel mixed integer linear programming (MILP) model to determine the least number of measurements to be protected. A decomposition approach is adopted to obtain the suboptimal solution. To further reduce the computation complexity, we also propose to separate the power grid into several subnetworks using a MILP approach and apply distributed protection strategy to each subnetwork. The simulations on the IEEE 14-bus, IEEE 24-bus, IEEE 30-bus, and IEEE 118-bus systems verify the correctness and effectiveness of the proposed protection strategy. [ABSTRACT FROM PUBLISHER]

The increasing penetration of renewable energy sources (RES) requires more flexibility resources (FR), such as thermal units and storages. FR kept in the system can help accommodate the uncertainties from RES. The challenge is how the system can survive when the RES level is very high. In this paper, RESs are considered as full-role market participants. They can bid in the day-ahead market, and the powers they deliver to the market are controllable up to their maximum available powers. Therefore, RESs are effectively dispatchable and can function as FR. To integrate dispatchable renewables, a two-stage robust unit commitment (UC) and dispatch model is established. In the first stage, a base UC and dispatch is determined. In the second stage, all FRs including RESs are used to accommodate the uncertainties, which is a mixed-integer programming (MIP) problem. It is proved that the solution to the max–min problem can be identified directly whether the strong duality holds or not for the inner minimization problem. The solution robustness can be guaranteed by considering only one extra scenario. Numerical results show the effectiveness of the proposed model and its advantages over the traditional robust UC model with high-level RES penetration. [ABSTRACT FROM PUBLISHER]

With increasing renewable penetration in power systems, considerable research efforts have been focused on how to accommodate the uncertainties from renewables in the Security-Constraint Unit Commitment (SCUC) problem. One of the candidate approaches to handling uncertainties is the two-stage Robust SCUC (RSCUC), which enables system to survive in any scenario. The survivability is guaranteed by the solution optimality of the max-min problem in the second stage. However, as the non-convex max-min problem is NP-hard, it is difficult to get the exact optimal solution in acceptable time. In this paper, we propose a new efficient formulation which recasts the max-min problem to a Mixed Integer Programming (MIP) problem using Binary Expansion (BE). The upper bound of the gap between the new MIP problem and the original max-min problem is derived. The gap, which quantifies the solution optimality of the max-min problem, is controllable. Two effective acceleration techniques are proposed to improve the performance of the MIP problem by eliminating inactive flow constraints and decomposing time-coupled uncertainty budget constraints. Accordingly, the computation burden of solving the max-min problem is reduced tremendously. The simulation results for the IEEE 118-Bus system validate and demonstrate the effectiveness of the new BE-based solution approach to the two-stage RSCUC and the acceleration techniques. [ABSTRACT FROM AUTHOR]

Battery-Based Energy Storage Transportation (BEST) is a potential solution for optimizing the power system operations with a high penetration of wind energy. In this paper, we propose a scenario-based stochastic model for the BEST-integrated power system scheduling. In this model, load and wind energy forecasting inaccuracies and random disturbances are modeled in scenario trees using the Monte Carlo simulation method. Random disturbances represent forced outages of both power system and railway system components, including generation units, transmission lines, railway stations, and railway lines. Benders decomposition is adopted to solve the stochastic model. Two BEST-integrated power systems are used to illustrate the proposed model and the performance of the proposed solution algorithm. The first one is a 6-bus power system integrated with a 3-station and 3-line railway network. The second one is the modified IEEE 118-bus power system integrated with a railway network composed of 8 railway stations and 10 rail lines. Simulation results show that the BEST system implementation is a viable option for managing the large-scale integration of wind power which can reduce the curtailment of wind power and accordingly lower the operation cost of power systems. [ABSTRACT FROM PUBLISHER]

Modern power grids are becoming more prone to cyberattacks. Even worse, an attacker without the full topology and parameter information of a power grid can still execute a false data injection attack without being detected by the state estimator. This paper proposes an efficient strategy for determining the optimal attacking region that requires reduced network information. The effectiveness of the proposed algorithm is verified through extensive simulations. This paper introduces a new front in the study of smart grid cyber security: determination of a feasible attacking region by obtaining less network information. This paper is also essential and significant for finding effective protection strategies against false data injection attacks based on the deep understanding of the mechanisms and strategies of the attacks. [ABSTRACT FROM PUBLISHER]

Independent system operators (ISOs) of electricity markets solve the security-constrained unit commitment (SCUC) problem to plan a secure and economic generation schedule. However, as the size of power systems increases, the current centralized SCUC algorithm could face critical challenges ranging from modeling accuracy to calculation complexity. This paper presents a distributed SCUC (D-SCUC) algorithm to accelerate the generation scheduling of large-scale power systems. In this algorithm, a power system is decomposed into several scalable zones which are interconnected through tie lines. Each zone solves its own SCUC problem and a parallel calculation method is proposed to coordinate individual D-SCUC problems. Several power systems are studied to show the effectiveness of the proposed algorithm. [ABSTRACT FROM PUBLISHER]

Line switching provides the flexibility for an operator to reduce the operation cost by switching some lines. However, the issue of multiple solutions in the existing models has never been explored and addressed. This will make the traditional models fail to determine the best line switching strategy in the context of multiple solutions. In this letter, we demonstrate that the multiple solution issue in line switching is very significant and propose a multi-objective optimization approach to address the multiple solutions of line switching. The simulation results on the IEEE 118-bus and IEEE 300-bus systems verify the effectiveness of the proposed approach. [ABSTRACT FROM PUBLISHER]

Power grid is one of the most critical infrastructures in a nation and could suffer a variety of cyber attacks. Recent studies have shown that an attacker can inject pre-determined false data into smart meters such that it can pass the residue test of conventional state estimator. However, the calculation of the false data vector relies on the network (topology and parameter) information of the entire grid. In practice, it is impossible for an attacker to obtain all network information of a power grid. Unfortunately, this does not make power systems immune to false data injection attacks. In this paper, we propose a local load redistribution attacking model based on incomplete network information and show that an attacker only needs to obtain the network information of the local attacking region to inject false data into smart meters in the local region without being detected by the state estimator. Simulations on the modified IEEE 14-bus system demonstrate the correctness and effectiveness of the proposed model. The results of this paper reveal the mechanism of local false data injection attacks and highlight the importance and complexity of defending power systems against false data injection attacks. [ABSTRACT FROM PUBLISHER]

Security-constrained unit commitment (SCUC), as one of key components in power system operation, is being widely applied in vertically integrated utilities and restructured power systems. The efficient solution framework is to implement iterations between a master problem (unit commitment) and subproblems (network security evaluations). In industrial applications, both Lagrangian relaxation and mixed-integer programming are commonly applied for the unit commitment problem, and both linear sensitivity factor and Benders cut methods are used to generate additional constraints in the phase of network security evaluations. This paper evaluates capabilities and performances of each algorithm through technical discussion and numerical testing. Special topics on the large-scale SCUC engine development are also discussed in this paper, such as input data screening, inactive constrains elimination, contingency management, infeasibility handling, parallel computing, and model simplification. This paper will benefit academic researchers, software developers, and system operators when they design, develop and assess effective models and algorithms for solving large-scale SCUC problems. [ABSTRACT FROM PUBLISHER]

This paper presents a novel methodology for the spatial power network planning problem based on the raster map in geographic information systems (GIS). Considering candidate lines and cell linkages, the proposed spatial power network planning model integrates the optimal electric line routing and the power network evaluation, with the objective of the minimum line investment cost. The network connectivity with candidate line routes and the hourly network evaluation for peak power loads are considered to ensure the feasibility of the line path and the security of power systems. The derived mixed-integer nonlinear programming (MINLP) formulation is transformed into a mixed-integer linear programming (MILP) problem by integer algebra techniques. Furthermore, in order to improve the computational efficiency, a tighter MILP formulation with the proposed enhanced line routing constraints is proposed. Numerical case studies illustrate the effectiveness of the proposed approach for the spatial power network planning problem. [ABSTRACT FROM PUBLISHER]

Today's smart power networks are exposed to an increasing number of cyber-attack events due to the highly integration of information techniques. It was revealed that an attacker can significantly increase the operation cost of a power system by launching undetectable false data injection attacks. However, the attack vector is determined by solving a bi-level linear programing (LP) problem, which is intractable for large systems. In this letter, we present a simple approach to determine an effective attack vector that can cause a significant increase in the operation cost. This is achieved by solving one LP. The simulation results on the IEEE testing systems verify the effectiveness of the proposed model and reveal that large-scale power systems are under high risk of cyber-attacks. [ABSTRACT FROM PUBLISHER]