Biblio

This paper addresses the allocation method of offensive resources for man-made attacks on power systems considering extreme weather conditions, which can help the defender identify the most vulnerable components to protect in this adverse situation. The problem is formulated as an attacker-defender model. The attacker at the upper level intends to maximize the expected damage considering all possible line failure scenarios. These scenarios are characterized by the combinations of failed transmission lines under extreme weather. Once the disruption is detected, the defender at the lower level alters the generation and consumption in the power grid using DC optimal power flow technique to minimize the damage. Then the original bi-level problem is transformed into an equivalent single-level mixed-integer linear program through strong duality theorem and Big-M method. The proposed attack resource allocation method is applied on IEEE 39-bus system and its effectiveness is demonstrated by the comparative case studies.

With the electric power distribution grid facing ever increasing complexity and new threats from cyber-attacks, situational awareness for system operators is quickly becoming indispensable. Identifying de-energized lines on the distribution system during a SCADA communication failure is a prime example where operators need to act quickly to deal with an emergent loss of service. Loss of cellular towers, poor signal strength, and even cyber-attacks can impact SCADA visibility of line devices on the distribution system. Neural Networks (NNs) provide a unique approach to learn the characteristics of normal system behavior, identify when abnormal conditions occur, and flag these conditions for system operators. This study applies a 24-hour load forecast for distribution line devices given the weather forecast and day of the week, then determines the current state of distribution devices based on changes in SCADA analogs from communicating line devices. A neural network-based algorithm is applied to historical events on Alabama Power's distribution system to identify de-energized sections of line when a significant amount of SCADA information is hidden.

Static security assessment is of great significance to ensure the stable transmission of electric power and steady operation of load. The scale of power system trends to expand due to the development of interconnected grid, and the security analysis of the entire network has become time-consuming. On the basis of synthesizing the efficiency and accuracy, a new method is developed. This method adopts a novel dynamic power flow (DPF) model considering the influence of governor deadband and amplitude-limit on the steady state quantitatively. In order to reduce the computation cost, a contingency screening algorithm based on binary search method is proposed. Static security assessment based on the proposed DPF models is applied to calculate the security margin constrained by severe contingencies. The ones with lower margin are chosen for further time-domain (TD) simulation analysis. The case study of a practical grid verifies the accuracy of the proposed model compared with the conventional one considering no governor nonlinearity. Moreover, the test of a practical grid in China, along with the TD simulation, demonstrates that the proposed method avoids massive simulations of all contingencies as well as provides detail information of severe ones, which is effective for security analysis of practical power grids.

Increased penetration of distributed energy resources (DERs) creates challenges in formulating the security constrained optimal power flow (SCOPF) problem as the number of models for these resources proliferate. Specifically, the number of devices with different mathematical models is large and their integration into the SCOPF becomes tedious. Henceforth, a process that seamlessly models and integrates such new devices into the SCOPF problem is needed. We propose an object-oriented modeling approach that leads to the autonomous formation of the SCOPF problem. All device models in the system are cast into a universal syntax. We have also introduced a quadratization method which makes the models consisting of linear and quadratic equations, if nonlinear. We refer to this model as the State and Control Quadratized Device Model (SCQDM). The SCQDM includes a number of equations and a number of inequalities expressing the operating limits of the device. The SCOPF problem is then formed in a seamless manner by operating only on the SCQDM device objects. The SCOPF problem, formed this way, is also quadratic (i.e. consists of linear and quadratic equations), and of the same form and syntax as the SCQDM for an individual device. For this reason, we named it security constrained quadratic optimal power flow (SCQOPF). We solve the SCQOPF problem using a sequential linear programming (SLP) algorithm and compare the results with those obtained from the commercial solver Knitro on the IEEE 57 bus system.

In an era of technological revolution in which everything becomes smarter and connected, the blockchain can introduce a new model for energy transactions able to grant more simplicity, security and transparency for end-users. The blockchain technology is characterized by a distributed architecture without a trusted and centralized authority, and, therefore, it appears as the perfect solutions for managing exchanges between peers. In this paper, a market algorithm that can be easily transferred to a smart contract for maximizing the match between produced and consumed energy in a micro-grid is presented. The algorithm supports energy transactions between peers (both producers and consumers) and could be one of the main executables implemented using a blockchain platform. The case study presented in this paper shows how the end-users through the blockchain could select among the possible energy transactions those more suitable to offer specific ancillary services to the grid operator without involving the grid operator itself or a third-party aggregator.

The security of energy supply in a power grid critically depends on the ability to accurately estimate the state of the system. However, manipulated power flow measurements can potentially hide overloads and bypass the bad data detection scheme to interfere the validity of estimated states. In this paper, we use an autoencoder neural network to detect anomalous system states and investigate the impact of hyperparameters on the detection performance for false data injection attacks that target power flows. Experimental results on the IEEE 118 bus system indicate that the proposed mechanism has the ability to achieve satisfactory learning efficiency and detection accuracy.

The increasing development of smart grid facilitates that modern power grids inter-connect with each other and form a large power system, making it possible and advantageous to conduct coordinated power flow among several grids. The communication burden and privacy issue are the prominent challenges in the application of synchronous iteration power flow method. In this paper, a synchronous iterative method of power flow in inter-connected power grid considering privacy preservation is proposed. By establishing the masked model of power flow for each sub-grid, the synchronous iteration is conducted by gathering the masked model of sub-grids in the coordination center and solving the masked correction equation in a concentration manner at each step. Generally, the proposed method can concentrate the major calculation of power flow on the coordination center, reduce the communication burden and guarantee the privacy preservation of sub-grids. A case study on IEEE 118-bus test system demonstrate the feasibility and effectiveness of the proposed methodology.

Due to geographical locations of Indonesia, some technology such as hydro and solar photovoltaics are very attractive to be used and developed. Distribution Energy Resources (DER) is the appropriate schemes implemented to achieve optimal operation respecting the location and capacity of the plant. The Gorontalo sub-system network was chosen as a case study considering both of micro-hydro and PV as contributed to supply the grid. The needs of a smart electrical system are required to improve reliability, power quality, and adaptation to any circumstances during DER application. While the topology was changing over time, intermittent of DER output and bidirectional power flow can be overcome with smart grid systems. In this study, an automation algorithm has been conducted to aid the engineers in solving the protection problems caused by DER implementation. The Protection Security Assessment (PSA) method is used to evaluate the state of the protection system. Determine the relay settings using an adaptive rule-based method on expert systems. The application with a Graphical User Interface (GUI) has been developed to make user easier to get the specific relay settings and locations which are sensitive, fast, reliable, and selective.

With the increasing expansion of wind and solar power plants, these technologies will also have to contribute control reserve to guarantee frequency stability within the next couple of years. In order to maintain the security of supply at the same level in the future, it must be ensured that wind and solar power plants are able to feed in electricity into the distribution grid without bottlenecks when activated. The present work presents a grid state assessment, which takes into account the special features of the control reserve supply. The identification of a future grid state, which is necessary for an ex ante evaluation, poses the challenge of forecasting loads. The Boundary Load Flow method takes load uncertainties into account and is used to estimate a possible interval for all grid parameters. Grid congestions can thus be detected preventively and suppliers of control reserve can be approved or excluded. A validation in combination with an exemplary application shows the feasibility of the overall methodology.

Cyber attacks on transmission lines are one of the main challenges in security of smart grids. These targeted attacks, if not detected, might cause cascading problems in power systems. This paper proposes a bi-level mixed integer linear programming (MILP) optimization model for false data injection on targeted buses in a power system to overflow targeted transmission lines. The upper level optimization problem outputs the optimized false data injections on targeted load buses to overflow a targeted transmission line without violating bad data detection constraints. The lower level problem integrates the false data injections into the optimal power flow problem without violating the optimal power flow constraints. A few case studies are designed to validate the proposed attack model on IEEE 118-bus power system.

The need to enhance the performance of existing transmission network in line with economic and technical constraints is crucial in a competitive market environment. This paper models the total transfer capacity (TTC) improvement using optimally placed thyristor-controlled series capacitors (TCSC). The system states were evaluated using distributed slack bus (DSB) and continuous power flow (CPF) techniques. Adaptable logic relations was modelled based on security margin (SM), steady state and transient condition collapse voltages (Uss, Uts) and the steady state line power loss (Plss), through which line suitability index (LSI) were obtained. The fuzzy expert system (FES) membership functions (MF) with respective degrees of memberships are defined to obtain the best states. The LSI MF is defined high between 0.2-0.8 to provide enough protection under transient disturbances. The test results on IEEE 30 bus system show that the model is feasible for TTC enhancement under steady state and N-1 conditions.

This paper sheds light on the collaborative efforts in restoring cyber and physical subsystems of a modern power distribution system after the occurrence of an extreme weather event. The extensive cyber-physical interdependencies in the operation of power distribution systems are first introduced for investigating the functionality loss of each subsystem when the dependent subsystem suffers disruptions. A resilience index is then proposed for measuring the effectiveness of restoration activities in terms of restoration rapidity. After modeling operators' decision making for economic dispatch as a second-order cone programming problem, this paper proposes a heuristic approach for prioritizing the activities for restoring both cyber and physical subsystems. In particular, the proposed heuristic approach takes into consideration of cyber-physical interdependencies for improving the operation performance. Case studies are also conducted to validate the collaborative restoration model in the 33-bus power distribution system.

The chances of cyber-attacks have been increased because of incorporation of communication networks and information technology in power system. Main objective of the paper is to prove that attacker can launch the attack vector without the knowledge of complete network information and the injected false data can't be detected by power system operator. This paper also deals with analyzing the impact of multi-attacking strategy on the power system. This false data attacks incurs lot of damage to power system, as it misguides the power system operator. Here, we demonstrate the construction of attack vector and later we have demonstrated multiple attacking regions in IEEE 14 bus system. Impact of attack vector on the power system can be observed and it is proved that the attack cannot be detected by power system operator with the help of residue check method.

To ensure power system operational security, it not only requires security incident detection, but also automated intrusion response and recovery mechanisms to tolerate failures and maintain the system's functionalities. In this paper, we present a design procedure for remedial action schemes (RAS) that improves the power systems resiliency against accidental failures or malicious endeavors such as cyber attacks. A resilience-oriented optimal power flow is proposed, which optimizes the system security instead of the generation cost. To improve its speed for online application, a fast greedy algorithm is presented to narrow the search space. The proposed techniques are computationally efficient and are suitable for online RAS applications in large-scale power systems. To demonstrate the effectiveness of the proposed methods, there are two case studies with IEEE 24-bus and IEEE 118-bus systems.

In this paper we report preliminary results from the novel coupling of cyber-physical emulation and interdiction optimization to better understand the impact of a CrashOverride malware attack on a notional electric system. We conduct cyber experiments where CrashOverride issues commands to remote terminal units (RTUs) that are controlling substations within a power control area. We identify worst-case loss of load outcomes with cyber interdiction optimization; the proposed approach is a bilevel formulation that incorporates RTU mappings to controllable loads, transmission lines, and generators in the upper-level (attacker model), and a DC optimal power flow (DCOPF) in the lower-level (defender model). Overall, our preliminary results indicate that the interdiction optimization can guide the design of experiments instead of performing a “full factorial” approach. Likewise, for systems where there are important dependencies between SCADA/ICS controls and power grid operations, the cyber-physical emulations should drive improved parameterization and surrogate models that are applied in scalable optimization techniques.

Major blackouts are due to cascading failures in power systems. These failures usually occur at vulnerable links of the network. To identify these, indicators have already been defined using complex network theory. However, most of these indicators only depend on the topology of the grid; they fail to detect the weak links. We introduce a new metric to identify the vulnerable lines, based on the load-flow equations and the grid geometry. Contrary to the topological indicators, ours is built from the electrical equations and considers the location and magnitude of the loads and of the power generators. We apply this new metric to the IEEE 118-bus system and compare its prediction of weak links to the ones given by an industrial software. The agreement is very well and shows that using our indicator a simple examination of the network and its generator and load distribution suffices to find the weak lines.

As power grid control systems become increasingly automated and distributed, security has become a significant design concern. Systems increasingly expose new avenues, at a variety of levels, for attackers to exploit and enable widespread disruptions and/or surveillance. Much prior work has explored the implications of attack models focused on false data injection at the front-end of the control system (i.e. during state estimation) [1]. Instead, in this paper we focus on characterizing the inherent cyber-attack vulnerabilities with power flow. Power flow (and power flow constraints) are at the core of many applications critical to operation of power grids (e.g. state estimation, economic dispatch, contingency analysis, etc.). We propose two algorithmic approaches for characterizing the vulnerability of buses within power grids to cyber-attacks. Specifically, we focus on measuring the instability of power flow to attacks which manifest as either voltage or power related errors. Our results show that attacks manifesting as voltage errors are an order of magnitude more likely to cause instability than attacks manifesting as power related errors (and 5x more likely for state estimation as compared to power flow).

Cascading failure, which can be triggered by both physical and cyber attacks, is among the most critical threats to the security and resilience of power grids. In current literature, researchers investigate the issue of cascading failure on smart grids mainly from the attacker's perspective. From the perspective of a grid defender or operator, however, it is also an important issue to restore the smart grid suffering from cascading failure back to normal operation as soon as possible. In this paper, we consider cascading failure in conjunction with the restoration process involving repairing of the failed nodes/links in a sequential fashion. Based on a realistic power flow cascading failure model, we exploit a Q-learning approach to develop a practical and effective policy to identify the optimal way of sequential restorations for large-scale smart grids. Simulation results on three power grid test benchmarks demonstrate the learning ability and the effectiveness of the proposed strategy.

The risk of large-scale blackouts and cascading failures in power grids can be due to vulnerable transmission lines and lack of proper remediation techniques after recognizing the first failure. In this paper, we assess the vulnerability of a system using fault chain theory and a power flow-based method, and calculate the probability of large-scale blackout. Further, we consider a Remedial Action Scheme (RAS) to reduce the vulnerability of the system and to harden the critical components against intentional attacks. To identify the most critical lines more efficiently, a new vulnerability index is presented. The effectiveness of the new index and the impact of the applied RAS is illustrated on the IEEE 14-bus test system.

The identification of transmission sections is used to improve the efficiency of monitoring the operation of the power grid. In order to test the validity of transmission sections identified, an assessment process is necessary. In addition, Transmission betweenness, an index for finding the key transmission lines in the power grid, should also be verified. In this paper, chain attack is assumed to check the weak links in the grid, thus verifying the transmission betweenness implemented for the system. Moreover, the line outage distribution factors (LODFs) are used to quantify the change of power flow when the leading line in transmission sections breaks down, so that the validity of transmission sections can be proved. Case studies based on IEEE 39 and IEEE 118 -bus system proved the effectiveness of the proposed method.

The interconnection of networks between several techno-socio-economic sectors such as energy, transport, and communication, questions the manageability and resilience of the digital society. System interdependencies alter the fundamental dynamics that govern isolated systems, which can unexpectedly trigger catastrophic instabilities such as cascading failures. This paper envisions a general-purpose, yet simple prototyping of self-management software systems that can turn system interdependencies from a cause of instability to an opportunity for higher resilience. Such prototyping proves to be challenging given the highly interdisciplinary scope of interdependent networks. Different system dynamics and organizational constraints such as the distributed nature of interdependent networks or the autonomy and authority of system operators over their controlled infrastructure perplex the design for a general prototyping approach, which earlier work has not yet addressed. This paper contributes such a modular design solution implemented as an open source software extension of SFINA, the Simulation Framework for Intelligent Network Adaptations. The applicability of the software artifact is demonstrated with the introduction of a novel self-healing mechanism for interdependent power networks, which optimizes power flow exchanges between a damaged and a healer network to mitigate power cascading failures. Results show a significant decrease in the damage spread by self-healing synergies, while the degree of interconnectivity between the power networks indicates a tradeoff between links survivability and load served. The contributions of this paper aspire to bring closer several research communities working on modeling and simulation of different domains with an economic and societal impact on the resilience of real-world interdependent networks.

In this study, it is proposed to carry out an efficient formulation in order to figure out the stochastic security-constrained generation capacity expansion planning (SC-GCEP) problem. The main idea is related to directly compute the line outage distribution factors (LODF) which could be applied to model the N - m post-contingency analysis. In addition, the post-contingency power flows are modeled based on the LODF and the partial transmission distribution factors (PTDF). The post-contingency constraints have been reformulated using linear distribution factors (PTDF and LODF) so that both the pre- and post-contingency constraints are modeled simultaneously in the SC-GCEP problem using these factors. In the stochastic formulation, the load uncertainty is incorporated employing a two-stage multi-period framework, and a K - means clustering technique is implemented to decrease the number of load scenarios. The main advantage of this methodology is the feasibility to quickly compute the post-contingency factors especially with multiple-line outages (N - m). This concept would improve the security-constraint analysis modeling quickly the outage of m transmission lines in the stochastic SC-GCEP problem. It is carried out several experiments using two electrical power systems in order to validate the performance of the proposed formulation.

This study proposes to apply an efficient formulation to solve the stochastic security-constrained generation capacity expansion planning (GCEP) problem using an improved method to directly compute the generalized generation distribution factors (GGDF) and the line outage distribution factors (LODF) in order to model the pre- and the post-contingency constraints based on the only application of the partial transmission distribution factors (PTDF). The classical DC-based formulation has been reformulated in order to include the security criteria solving both pre- and post-contingency constraints simultaneously. The methodology also takes into account the load uncertainty in the optimization problem using a two-stage multi-period model, and a clustering technique is used as well to reduce load scenarios (stochastic problem). The main advantage of this methodology is the feasibility to quickly compute the LODF especially with multiple-line outages (N-m). This idea could speed up contingency analyses and improve significantly the security-constrained analyses applied to GCEP problems. It is worth to mentioning that this approach is carried out without sacrificing optimality.

Online Dynamic Security Assessment (DSA) is a dynamical system widely used for assessing and analyzing an electrical power system. The outcomes of DSA are used in many aspects of the operation of power system, from monitoring the system to determining remedial action schemes (e.g. the amount of generators to be shed at the event of a fault). Measurement from supervisory control and data acquisition (SCADA) and state estimation (SE) results are the inputs for online-DSA, however, the SE error, caused by sudden change in power flow or low convergence rate, could be unnoticed and skew the outcome. Therefore, generator shedding scheme cannot achieve optimum but must have some margin because we don't know how SE error caused by these problems will impact power system stability control. As a method for solving the problem, we developed SE error detection system (EDS), which is enabled by detecting the SE error that will impact power system transient stability. The method is comparing a threshold value and an index calculated by the difference between SE results and PMU observation data, using the distance from the fault point and the power flow value. Using the index, the reliability of the SE results can be verified. As a result, online-DSA can use the SE results while avoiding the bad SE results, assuring the outcome of the DSA assessment and analysis, such as the amount of generator shedding in order to prevent the power system's instability.

This paper is concerned about the security vulnerabilities in the implementation of the Congestion Revenue Rights (CRR) markets. Such problems may be due to the weakly detectable network model parameter errors which are commonly found in power systems. CRRs are financial tools for hedging the risk of congestion charges in power markets. The reimbursements received by CRR holders are determined by the congestion patterns and Locational Marginal Prices (LMPs) in the day-ahead markets, which heavily rely on the parameters in the network model. It is recently shown that detection of errors in certain network model parameters may be very difficult. This paper's primary goal is to illustrate the lack of market security due to such vulnerabilities, i.e. CRR market calculations can be manipulated by injecting parameter errors which are not likely to be detected. A case study using the IEEE 14-bus system will illustrate the feasibility of such undetectable manipulations. Several suggestions for preventing such cyber security issues are provided at the end of the paper.