Remedial Action Schemes Research Articles

The value of real-time adjustable remedial action schemes

Remedial action schemes (RAS) are often seen as an inexpensive way to relieve contingency-related grid congestion without building new transmission infrastructure. However, RAS settings often remain fixed during real-time operation and do not adapt to variation in operating conditions due to renewable and distributed generation. This lack of adaptability may cause suboptimal settings and possibly insecure operations. To assess the value of allowing RAS settings to vary real time and the benefit of considering multiple load and generation scenarios, we propose a mixed integer optimization framework which identifies optimal RAS actions while incorporating multiple load and renewable energy scenarios. We also propose an iterative algorithm that efficiently solves the optimization problem, leveraging the fact that only a few scenarios and contingencies are binding at optimality. We demonstrate the benefits of (i) updating RAS more frequently and (ii) considering multiple load scenarios by performing case studies on the RTS-GMLC system.

Simultaneous voltage control and frequency oscillation damping in Hydro-Québec’s network by using an updated 735-kV shunt reactors automatic switching system

The Hydro-Québec transmission system is among the most extensive and complex power grid in North America due to its very long lines (over 1000 km) joining the generation facilities in the north of the Québec province to the main centers of consumption in the south. Since the 1990s, Hydro-Québec has adopted a defense plan, consisting of various types of protection solutions including different remedial action schemes (RAS) and automatic action strategies, to increase the stability and reliability of its transmission system against extreme contingencies. One of these protection strategies is the automatic switching of shunt reactors (MAIS) 1MAIS is the acronym of the French name of the project as Manoeuvre Automatique d’Inductance Shunt (MAIS). which has been in operation since 1997. The goal of the MAIS system is to keep the voltage profile of the whole 735-kV grid in an acceptable range to increase the transmission capacity. The MAIS system at each substation is entirely independent from neighbour substations and will close or trip shunt reactors just based on local measurements. As a periodical upgrade in all existing protection solutions in the Hydro-Québec power system, new functionalities have been added into the existing MAIS system to develop its new version to improve both the voltage profile and the frequency response. This paper presents this new version including the algorithm and the concept behind the new additional functionalities. The simulation results show the efficiency and performance of the proposed method. On that basis, a deployment on Hydro-Québec’s grid to replace almost all the existing MAIS system with this new version will be realized by 2030.

A Remedial Action Scheme To Mitigate Market Power Caused by Cyberattacks Targeting a Smart Distribution System

A Remedial Action Scheme To Mitigate Market Power Caused by Cyberattacks Targeting a Smart Distribution System

Experimental Validation of a Remedial Action via Hardware-in-the-Loop System Against Cyberattacks Targeting a Lab-Scale PV/Wind Microgrid

This paper experimentally validates the effectiveness of a primary/backup framework in preventing/mitigating the impacts of false data injection (FDI) cyberattacks targeting a lab-scale wind/photovoltaic (PV) microgrid. As the primary mecha-nism, an artificial intelligence-based false data detection algorithm is proposed to forecast the upcoming measurements and alert the operator about the accuracy of sensors readings, directly collected from the field devices. Given that some FDI attacks are designed to bypass the utilized detection methods, it is essential to be prepared for such circumstances. Hence, as the backup mechanism, a remedial action scheme (RAS) is also introduced to mitigate the impacts of such malicious cyberattacks and keep the functionality of the microgrid. The proposed framework (i.e., FDI model, detection approach, and remedial action scheme) is developed as a hardware-in-the-loop (HIL) testbed within the cyber-physical structure of the smart microgrid. The experimental results validate 1) the negative impacts of modeled FDI attacks on the lab-scale microgrid, 2) the effectiveness of the developed false data detection technique, and 3) the efficiency of the proposed RAS to keep the normal operation of the targeted microgrid when the detection system fails to recognize the attack.

Proximal policy optimization through a deep reinforcement learning framework for remedial action schemes of VSC-HVDC

A proximal policy optimization (PPO)-based back-to-back VSC-HVDC emergency control strategy based on multi-agent deep reinforcement learning (DRL) approach is proposed for use in an energy management system (EMS). In this scheme, an advanced DRL algorithm is proposed by implementing both PPO and a communication neural network for large power systems. The PPO modeled as intelligent agents with objective functions have shown a higher convergence performance than have existing DRL algorithms. Further, the model was demonstrated to effectively address voltage variances caused by the high penetration of renewable energy sources. By implementing PPO, the learning procedure is stabilized and made robust to continuous changes in network topology. To escalate the effectiveness of the proposed algorithm, a comprehensive case studies were conducted on an standard test systems and Korean power system considering variations in load and PV generation and a weak centralized communication environment. The results indicate that outstanding control performance and autonomously regulated bus voltage and line flows, thereby validating the effectiveness of the method.

Security Study on Freestanding Minigrid with Multiple Rotary Energy Sources

Traditional grid has been extensively studied for system security. For freestanding renewable energy-based minigrid, a three-source stand-alone minigrid is in this study examined for system security under large disturbances. By real-time simulation, rotor responses of the generators and voltage characteristics of the buses are assessed at sudden load change and under fault condition. Post-perturbation behaviour of the system shows synchronism is not compromised by any of the generators and the bus voltage profiles remains within their pre-perturbation statutory limits. This implies that a number of rotating machine-based energy mini-sources could be interconnected into a freestanding minigrid without compromising the stability of the system. The assessment could find use in predictive and corrective compensation for stability issues in mini integrated power systems and serve as simple generic investigative approach that could be integrated into Minigrids Remedial Action Scheme or Minigrids Energy Management System for dynamic security assessment of multi-machine island minigrids.

Active Power Control of DFIG Wind Turbines for Transient Stability Enhancement

This paper proposes an adaptive Dynamic Power Reduction (aDPR) scheme for Type-3 Wind Turbine-Generators (WTGs) to enhance transient stability of synchronous generators (SGs), with benefits of increasing transfer limits on already fully loaded transmission paths. The scheme consists of three components to deal with a fault close to a SG. Initially, the WTG curtails its active power to a predefined level to act as a dynamic brake for the SG. Then the controller monitors the rate of change of frequency to adaptively ramp the WTG back to its original power output while minimizing the WTG pitch and rotor motion. Finally, to reduce the risk of second-swing instability, the converter uses its reactive current to damp SG power swings. The aDPR scheme can be classified as a remedial action scheme and is enabled if its action can ensure transient stability. To demonstrate the effectiveness of aDPR and to benchmark it against other WTG active current and frequency feedback control techniques, a single-machine infinite-bus system with one WTG is utilized. Next, an aDPR enabled WTG is integrated in the NPCC 68-bus system. Finally, the aDPR controller’s ability to prevent transient instability is demonstrated on the two-area system.

Toward Adaptive Load Shedding Remedial Action Schemes in Modern Electrical Power Systems

Toward Adaptive Load Shedding Remedial Action Schemes in Modern Electrical Power Systems

Feedback Control Strategy for Transient Stability Application

Power systems are subjected to a wide range of disturbances during daily operations. Severe disturbances, such as a loss of a large generator, a three-phase bolted fault on a generator bus, or a loss of a transmission line, can lead to the loss of synchronism of a generator or group of generators. The ability of a power system to maintain synchronism during the few seconds after being subjected to a severe disturbance is known as transient stability. Most of the modern methods of controlling transient stability involve special protection schemes or remedial action schemes. These special protection schemes sense predetermined system conditions and take corrective actions, such as generator tripping or generation re-dispatch, in real time to maintain transient stability. Another method is the use of a real-time feedback control system to modulate the output of an actuator in response to a signal. This paper provides a fundamental evaluation of the use of feedback control strategies to improve transient stability in a power system. An optimal feedback control strategy that modulates the real power injected and absorbed by distributed energy-storage devices is proposed. Its performance is evaluated on a four-machine power system and on a 34-machine reduced-order model of the Western North American Power System. The result shows that the feedback control strategy can increase the critical fault clearing time by 60%, thereby improving the transient stability of the power system.

A Remedial Action Scheme Against False Data Injection Cyberattacks in Smart Transmission Systems: Application of Thyristor-Controlled Series Capacitor (TCSC)

This article proposes a remedial action scheme (RAS) based on thyristor-controlled series capacitors (TCSCs) to cope with false data injection (FDI) cyberattacks leading to tie-line congestions in transmission systems. To develop such a RAS, a three-level framework is designed. The first level of the framework is associated with the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">planning</i> phase, where strategic locations of TCSCs are determined. The second level, which is related to the <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">operation</i> phase, challenges the upgraded system via FDI attacks, resulting in tie-line congestions. This necessitates the third level of the framework, where system operator takes advantage of a RAS based on the preinstalled TCSCs to <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">rapidly</i> alleviate the congestions caused by FDIs. The effectiveness of the developed framework is verified on two IEEE test systems, where it is assumed that a higher budget is spent on securing remote terminal units of TCSCs; hence, phasor measurement units are more prone to cyberattacks.

Frequency Improvement in Microgrids Through Battery Management System Control Supported by a Remedial Action Scheme

In this work, we propose a battery management system control (BMSC) for primary frequency regulation. In many operational scenarios, the microgrid (MG) results in a weak frequency due to the low inertia of the renewable energy sources and the highly dynamic loads. The proposed BMSC improves the operation and control of the MG by managing the energy stored in the battery storage systems (BESS) through the battery management system (BMS); continuous frequency control of the MG is achieved, preserving the energy availability of BESS. The proposed system performs frequency control actions in real time in the MG operation through BMSC, and it is not required to know the insolation and wind speed forecasts, due to the high uncertainty in the forecasts. Frequency regulation is achieved by evaluating the energy required by the MG, and by controlling the charging and discharging operations that ensure that the BESS resource is available. Due to the highly dynamic MGs, the contribution of the battery in each period is limited to a percentage of its capacity, avoiding deep discharges and the loss of premature energy provided by the battery. The control will apply a remedial action scheme to keep the frequency within the operating margins if the BMS cannot regulate the frequency. The system proposed here is evaluated using an MG system in an island operation. The results show the feasibility of the proposed system under different operating conditions and the compliance with the technical operational specifications of the BESS.

Deep Learning-Based Adaptive Remedial Action Scheme with Security Margin for Renewable-Dominated Power Grids

The Remedial Action Scheme (RAS) is designed to take corrective actions after detecting predetermined conditions to maintain system transient stability in large interconnected power grids. However, since RAS is usually designed based on a few selected typical operating conditions, it is not optimal in operating conditions that are not considered in the offline design, especially under frequently and dramatically varying operating conditions due to the increasing integration of intermittent renewables. The deep learning-based RAS is proposed to enhance the adaptivity of RAS to varying operating conditions. During the training, a customized loss function is developed to penalize the negative loss and suggest corrective actions with a security margin to avoid triggering under-frequency and over-frequency relays. Simulation results of the reduced United States Western Interconnection system model demonstrate that the proposed deep learning–based RAS can provide optimal corrective actions for unseen operating conditions while maintaining a sufficient security margin.

A remedial action framework against cyberattacks targeting energy hubs integrated with distributed energy resources

Operation of modern power systems integrated by distributed energy resources is only possible if information/communication technologies are leveraged in the system. This results in cyber-physical power systems which are vulnerable to malicious cyberattacks. Hence, it is crucial to propose practical solutions to enhance the resilience of smart grids against cyberattacks. Targeting energy hubs integrated by distributed energy resources, a cyberattack based on min–max formulation is presented in this paper. A remedial action scheme, which changes status (i.e., connection/disconnection) of the energy hub components, is proposed to mitigate the economic effect of the aforementioned cyberattack. The electricity/heat demands of the energy hub are supplied by electricity/gas networks and the energy hub components including combined heat and power, wind turbine, electrical/thermal storages, boiler, and demand response. The attacker utilizes the energy hub components to increase the associated costs. However, the system operator controls the costs by changing the status of the energy hub devices. Obtained results verify that the proposed framework leads to an effective mechanism to proactively mitigate the economic-related consequences of cyberattacks on energy hubs. The simulation results demonstrate that: 1) disconnection of the energy hub from electricity networks under the cyberattack mitigates the increased cost by 40%, 2) disconnection of the energy hub from boiler and connection of thermal storage to the system under the experienced cyberattack reduce the imposed cost by 76%.

Sensitivity-based Optimal Remedial Actions to Damp Oscillatory Modes Considering Security Constraints

This paper proposes a comprehensive analytic method for applying various optimal remedial actions to improve critical electromechanical modes damping without jeopardizing damping of non-critical modes and violating security constraints of power system. Generators and reactive power sources redisptach, demand side management and the generators voltage reference tuning are remedial actions that are considered here. Dynamic equations of the flux-decay dynamic model of generators, standard dynamic models of excitation system and power system stabilizer and algebraic equations of active and reactive powers balance are formulated in the quadratic eigenvalue problem framework. With simultaneous use of system parameters and online measured data, sensitivity factors of electromechanical modes, generators reactive power, transmission lines apparent power and the buses voltage amplitude with respect to the mentioned remedial actions are calculated. Consumers, generators and reactive power sources are independent agents that offer their price bid to contribute in remedial action schemes. Finally, using sensitivity factors, the best remedial action is determined by mixed-integer nonlinear optimization subject to the minimum required damping ratio and security constraints of transmission lines congestion, generators reactive power capability and buses voltage amplitude. The proposed method is demonstrated on the IEEE 39-bus power system and the results highlight its efficacy.

Reliability Evaluation of Cyber-Physical Power Systems Considering Random Failures in Measurement and Remote Control

In integrated cyber-physical power systems (CPPS), the malfunctions of cyber system deeply affect the dispatcher’s remedial action scheme, and thus have a significant impact on the reliability of the physical power system. The random failures of electrical devices in physical power system and the random outages of measurement and remote control in cyber system are both considered for the CPPS reliability evaluation in this paper. Firstly, the component outage models in physical, access, backbone and application layers are presented. Then, a linearized DC state estimation (SE) model is introduced and the SE errors incurred by measurement failures, i.e. bad and lost measurements, are interpreted. Moreover, the impact of SE errors and remote control failures on the corrective action scheme of the control center during the fault recovery process is simulated by a novel modification of the classical optimal load shedding (OLS) model. Finally, to verify the effectiveness and validity of the proposed CPPS reliability evaluation method, the communication network topologies of the cyber system for the RBTS and IEEE-RTS96 are designed, and then the reliability evaluation based on the presented component outage models and the novel modified OLS model are conducted by the non-sequential Monte Carlo simulation (NMCS).

A Cyber-Physical Anomaly Detection for Wide-Area Protection Using Machine Learning

Wide-area protection scheme (WAPS) provides system-wide protection by detecting and mitigating small and large-scale disturbances that are difficult to resolve using local protection schemes. As this protection scheme is evolving from a substation-based distributed remedial action scheme (DRAS) to the control center-based centralized RAS (CRAS), it presents severe challenges to their cybersecurity because of its heavy reliance on an insecure grid communication, and its compromise would lead to system failure. This article presents an architecture and methodology for developing a cyber-physical anomaly detection system (CPADS) that utilizes synchrophasor measurements and properties of network packets to detect data integrity and communication failure attacks on measurement and control signals in CRAS. The proposed machine leaning-based methodology applies a rules-based approach to select relevant input features, utilizes variational mode decomposition (VMD) and decision tree (DT) algorithms to develop multiple classification models, and performs final event identification using a rules-based decision logic. We have evaluated the proposed methodology of CPADS using the IEEE 39 bus system for several performance measures (accuracy, recall, precision, and F-measure) in a cyber-physical testbed environment. Our experimental results reveal that the proposed algorithm (VMD-DT) of CPADS outperforms the existing machine learning classifiers during noisy and noise-free measurements while incurring an acceptable processing overhead.

Decentralized Outages Prevention: A Remedial Action Scheme for Cyberattacks Targeting Market Retailers in Smart Distribution Systems

This article proposes a framework as a remedial action scheme (RAS) for close to real-time system operation when system operator becomes aware of possibility of decentralized outages due to simultaneous attacks on several market retailers. The upper level of the framework is an offline RAS where system operator immediately implements a customized reconfiguration preventing the decentralized outages. Due to immediate implementation of upper-level reaction, it is possible to face decentralized congestions in the new topology having minimum reliance on the attacked market retailers. Since severity of congestions is less than that of outages, the lower level of the framework is implemented as an online RAS. In this level, eligible nonattacked market retailers are invited into a secondary market to compete for generation/consumption redispatch and contribute into alleviation of congestions with the purpose of increasing their income. The effectiveness of the proposed framework is validated on a well-known 136-bus distribution system.

Enhanced Contingency Analysis—A Power System Operator Tool

Contingency analysis (CA) is a well-known function in power system planning and operation. In accordance with CA results, the system operator dispenses information regarding static security of the power system (overloads and/or voltage outside tolerable limits). However, classic CA with remedial action schemes cannot distinguish safe operating regimes from potentially dangerous ones in terms of voltage (in)stability. In fact, voltage instability is considered as one of the major threats leading to power system insecurity. Therefore, in this study an enhanced contingency analysis (ECA) is presented where the classical CA is extended with static voltage analysis based on the modal analysis. The article presents a dedicated methodology for the proposed ECA tool, with special emphasis on the analysis of corrective measures provided by the system operator, intended for enhancing power system security (regulation transformer action, distributed generation and energy storage). Also the influence of the load model was analyzed by simulation and the main conclusions are presented. The study demonstrated the advantages that distributed generation resources and energy storage can provide in the context of voltage stability. Also, the simulations acknowledged the importance of correct load modeling, since over or under estimation of a certain load-type component can result in too optimistic or too pessimistic power system operation limits.

What does the GB power outage on 9 August 2019 tell us about the current state of decarbonised power systems?

The GB power blackout, that happened on 9 August 2019, was a unique stress test exposing fault lines brought about by the rapid changes due to the decarbonisation drive and penetration of smart grids technologies. It has demonstrated that, as a significant amount of new equipment and controls were added to the system in a very short time, the probability of common, hidden modes of failures has significantly increased. In the face of declining reliability, maintaining the status quo is not an option. While currently increasing the (N-1) security margin could prove to be expensive, the balance of costs and benefits is likely to change in future. Especially wider application of innovative frequency controls, including “virtual inertia” and Remedial Action Schemes, could help reduce the costs. Distributed Generation (DG) reached such a high penetration level that it cannot be treated any longer as negative demand. Traditional under-frequency load shedding should be made more selective. Interactions between the power system and other infrastructures are still poorly understood and there is a significant risk that if the current compartmentalised approach to their governance and operation is not changed, we may see more unexpected consequences of disturbances across the whole system.

Remote testing architecture for synchrophasor‐based remedial action schemes

A wide range of remedial action schemes (RAS) have been developed and deployed in field to take automated control actions to maintain system reliability and stability during adverse system conditions. According to the regulatory standards from North American Electricity Reliability Corporation (NERC), RAS applications deployed in field and their functionalities are required to be tested periodically. To do testing for the RAS, regular process that requires taking critical components offline is usually labor-intensive and time-consuming, especially for complex applications utilizing multiple measurement units and controllers from different substations. To overcome these challenges, this paper presents a remote testing architecture for remedial action schemes. Our proposed remote testing architecture is capable of accessing test signal generators, phasor measurement units (PMUs), RAS control units from multiple substations, managing tests for both centralized and distributed implementations of RAS. To address the effectiveness of the remote testing architecture, this paper presents the minimal wind curtailment applications with detailed alternative formulations of optimization models for RAS and highlights performance of the proposed remote testing of RAS. Simulation results for an IEEE standard test system and representative industry test cases demonstrate effective performance of the developed RAS testing mechanism.