Vulnerabilities in two-area Automatic Generation Control systems under cyberattack

The stability of modern power grids relies heavily on effective frequency control, mainly achieved through automatic generation control (AGC) systems. However, AGC systems have become increasingly vulnerable to cyber-physical attacks due to their dependence on communication infrastructure and cyber-physical devices. In this article, we present a novel attack strategy that leverages the learning capabilities of a convolutional neural network and long short-term memory on encrypted data to predict the imminent operating state of a power system. By accurately forecasting vulnerable operational conditions through encrypted traffic analysis, attackers can orchestrate timely denial-of-service (DoS) attacks on AGC systems, significantly amplifying the physical impact of their cyber-attacks. Despite the implementation of network traffic protection measures through the utilization of the IPsec/ESP protocol, which offers encryption at the network layer to ensure the confidentiality of the original packet content and enhances the complexity of traffic analysis, our research demonstrates that attackers can still extract spatiotemporal features from high-entropy encrypted synchrophasor data packets. This finding underscores the inherent limitations of IPsec/ESP protocols in completely obstructing malicious analysis of network traffic and achieving absolute prevention of such nefarious activities. Consequently, the confidentiality of the power system's operational state remains compromised. To gauge the effectiveness of the proposed attack strategy, we conduct various test cases and simulations, revealing a significant increase in the success rate of DoS attacks. These findings underscore the urgency of implementing countermeasures to thwart attackers from exploiting traffic analysis techniques and emphasize the necessity for strengthened security measures in power grid infrastructure.

In recent years, wind power which is an intermittent and random energy source affects the frequency stability of automatic generation control (AGC) system with the large-scale wind power integration. So, it is necessary to improve the conventional frequency control method and model. Firstly, a dynamic wind farm model is set up based on wind turbines clustering for the accuracy of data in this paper. Secondly, it is analyzed that the difference between multi-area interconnection AGC model and area control error (ACE) with wind farm and without wind farm. Thirdly, an intelligent control strategy of cascade control with outer loop using fuzzy PI algorithm and inner loop using internal model controller is designed to reduce the impacting of wind power changes on AGC system frequency and improve the performance of AGC system. Finally, frequency control effects of conventional AGC system and improved AGC system are contrasted by simulation study of four-area system. It is proved that the improved AGC system has stronger ability to resist fluctuation of wind power and better frequency control performance.

Communication plays a vital role in incorporating smartness into the interconnected power system. However, historical records prove that the data transfer has always been vulnerable to cyber-attacks. Unless these cyber-attacks are identified and cordoned off, they may lead to black-out and result in national security issues. This paper proposes an optimal two-stage Kalman filter (OTS-KF) for simultaneous state and cyber-attack estimation in automatic generation control (AGC) system. Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics. Five types of cyber-attacks, i.e., false data injection (FDI), data replay attack, denial of service (DoS), scaling, and ramp attacks, are injected into the measurements and estimated using OTS-KF. As the load variations of each area are seldom available, OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system. The proposed technique is validated on the benchmark two-area, three-area, and five-area power system models. The simulation results under various test conditions demonstrate the efficacy of the proposed filter.

This paper proposes a framework to quantify the impact of uncertainty that arises from load variations, renewable-based generation, and noise in communication channels on the automatic generation control (AGC) system. To this end, we rely on a model of the power system that includes the synchronous generator dynamics, the network, and the AGC system dynamics, as well as the effect of various sources of uncertainty. Then, we develop a method to analytically propagate the uncertainty from the aforementioned sources to the system frequency and area control error (ACE), and obtain expressions that approximate their probability distribution functions. We make use of this framework to obtain probabilistic expressions for the frequency performance criteria developed by the North American Electric Reliability Corporation (NERC); such expressions may be used to determine the limiting values of uncertainty that the system may withstand. The proposed ideas are illustrated through the Western Electricity Coordination Council (WECC) 9-bus 3-machine system and a 140-bus 48-machine system.

Mitigation of cyber-physical attacks in multi-area automatic generation control

Pumped storage power (PSP) plants are expected to be an important player in modern electrical power systems when dealing with increasing shares of new renewable energies (NRE) such as solar or wind power. The massive penetration of NRE and consequent replacement of conventional synchronous units will significantly affect the controllability of the system. In order to evaluate the capability of variable speed PSP plants participation in the frequency restoration reserve (FRR) provision, taking into account the expected performance in terms of improved ramp response capability, a comparison with conventional hydro units is presented. In order to address this issue, a three area test network was considered, as well as the corresponding automatic generation control (AGC) systems, being responsible for re-dispatching the generation units to re-establish power interchange between areas as well as the system nominal frequency. The main issue under analysis in this paper is related to the benefits of the fast response of variable speed PSP with respect to its capability of providing fast power balancing in a control area.

This article investigates the impact of phase-locked loop (PLL) systems along with virtual inertia on system dynamics of a parallel AC/HVDC tie-line integrated two-area automatic generation control (AGC) system under a deregulated scenario. The multi-area AGC system comprises two thermal and hydro-gas in area-1, 2. An attempt is made to utilize a new secondary controller named the two-degree-of-freedom tilt-integral-derivative (2DOF-TID) controller. Its gains and PLL parameters are optimized by a novel meta-heuristic algorithm called a bird swarm algorithm (BSA). The system dynamics corresponding to the proposed 2DOF-TID are compared with 2DOF-PID, 2DOF-PI explored better system dynamics with a 2DOF-TID controller. The system dynamics are compared with AC alone, AC/HVDC, AC/HVDC tie-line incorporating with PLL, virtual inertia revealed the better performance of the latter. Moreover, the effect of variation in PLL parameter on the system dynamics are also analyzed and studies suggested that the obtained response at the nominal value of natural frequency (ωn ) and damping coefficient (ζ) reduces the subsequent oscillation and provides better dynamics other than varying condition.

It is normally accepted that Battery Energy Storage Systems improve frequency regulation by providing fast response to the Automatic Generation Control. However, currently available control strategies may lead to early Energy Storage Systems aging given that Automatic Generation Control requirements are increasing due to zero carbon power generation integration. In this sense, it is important to analyze the aging phenomena in order to assess the technical–economical usefulness of Battery Energy Storage Systems towards zero carbon power systems. In order to avoid early aging, various proposals on aging-reducing algorithms can be found; however, it is unclear if those aging-reducing algorithms affect the performance of Battery Energy Storage Systems. It is also unclear whether those effects must be internalized to properly dimension the capacity of Battery Energy Storage Systems to both comply with performance standards and to prevent early aging. Thus, this paper estimates the storage capacity of a Battery Energy Storage Systems to comply with Automatic Generation Control performance standard under aging-reducing operating algorithms by dynamics simulations of a reduced-order, empirically-validated model of the Electric Reliability Council of Texas. The results show the relationship between the required performance of Automatic Generation Control and Battery Energy Storage System capacity, considering a 1-year simulation of Automatic Generation Control dynamics. It can be concluded that the compliance with performance standards is strongly related to the storage capacity, regardless of how fast the device can inject or withdraw power from the grid. Previous results in the state-of-the-art overlook the quantification of this relationship between compliance with performance standards and storage capacity.

A fundamental responsibility of power system operators is to balance load and generation in real-time, where load represents the summation of system native load and the scheduled exchanges to other utilities. To balance load and generation in real-time, a power system operator needs to ensure that the automatic generation control (AGC) system that regulates a number of generating units to match generation to load is working within the North American Reliability Council's (NERC) established reliability standards. The objective of AGC is to balance generation and load on a minute-to-minute basis when operators do not have sufficient time to control generators. AGC system consists of two technology elements: (i) AGC application; and (ii) AGC infrastructure.

The Automatic Generation Control (AGC) system provides secondary frequency regulation by commanding online generating units in order to compensate the active power imbalances in power systems. System operators are responsible for monitoring and evaluating the performance of this service that guarantee the secure operation. However, due to the complexity of the AGC system and the amount of available data, the performance evaluation could be challenging. As an alternative, a data-driven approach was used to develop a tool that support the operator performing this task, providing an insight in a few minutes. The proposed machine learning-based tool for AGC performance monitoring and evaluation was tested on the Colombian system operator.

Development and implementation of new Automatic Generation Control (AGC) algorithms requires testing them on a model that adequately simulates primary energetic, information and control processes. In this article an implementation of a test platform based on HRTSim (Hybrid Real Time Simulator) and SCADA CK-2007 (which is widely used by the System Operator of Russia) is proposed. Testing of AGC algorithms on the test platform based on the same SCADA system that is used in operation allows to exclude errors associated with the transfer of AGC algorithms and settings from the test platform to a real power system. A power system including relay protection, automatic control systems and emergency control automatics can be accurately simulated on HRTSim. Besides the information commonly used by conventional AGC systems HRTSim is able to provide a resemblance of Phasor Measurement Unit (PMU) measurements (information about rotor angles, magnitudes and phase angles of currents and voltages etc.). The additional information significantly expands the number of possible AGC algorithms so the test platform is useful in modern AGC system developing. The obtained test results confirm that the proposed system is applicable for the tasks mentioned above.

Recent developments in power systems to build a smart grid depend critically on communication networks which provide unprecedented advantages for comprehensive system visibility, monitoring, protection, and control. However, it increases the system complexity and vulnerability to cyberattacks that could jeopardize the seamless system operation. The attack must be detected effectively, and then proper measures to mitigate the effect quickly to restore normality. This work implements a reduced-order <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> filter to simultaneously estimate the state variable and unknown inputs such as noise or attack signals in the automatic generation control (AGC) system. Further, the knowledge of the disturbances is utilized to mitigate the attack by appropriately compensating the sensor readings, such that the control decisions are based on the correct states, not the corrupted signals generated in the attack scenario. The stability criterion is provided based on Lyapunov functions to check the estimation error in the presence of the exogenous disturbance. Further, the proposed <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$H_{\infty }$ </tex-math></inline-formula> estimator performance is evaluated by comparing it with the Kalman filter to verify its effectiveness. The estimator’s performance is validated by developing a cosimulation platform using the server–client architecture of data exchange. Further, the performance is validated in a real-time digital simulator (RTDS) platform used as the power system interacting with an estimator deployed on Raspberry Pi board sensing the attacks.

The article discusses the limitations of existing automatic generation control (AGC) systems that appear under the impact of variable energy resources. To overcome identified issues, the authors proposed an approach that advances the functional block responsible for computation of plant participation factors (PF). This approach connects an optimizer with a component for power flow calculations and allows online estimation of plant PFs to increase flexibility and selectivity of AGC. The corresponding optimization models were established to perform conventional and advanced control strategies. To meet performance requirements imposed by variable energy sources, the machine learning (ML) model, namely the densely connected neural network, was designed for power flow calculations. Besides, Lasso regression method was proposed to select relevant features for the considered control tasks and improve the performance of the machine learning based power flow model. Finally, the software tool was developed to implement the proposed approach and tested on a model of real 60 GW interconnection containing 464 nodes and 742 branches. The results of the software testing confirmed its feasibility and easy integration into existing AGC systems.

In the upcoming era, when conventional sources will mostly be replaced by renewable energy sources, engineers may face huge problem during controller design. For this large interconnected hybrid automatic generation control (AGC) system, high proficient and intelligent control techniques are needed. Hence, a newly developed fractional order tilt integral derivative double integral filter plus (FOTIDF-II) controller has been proposed in this research work. To overcome slow convergence feature of moth swarm algorithm (MSA), chaotic-based moth swarm algorithm (CMSA) is introduced by incorporating chaos into the MSA algorithm. Afterward, to upgrade the performance of AGC, the newly proposed controller is incorporated to the said system. The gains and fractional order parameters such as order of integrator, differentiator and filter coefficients of the proposed controller are optimized using CMSA optimizing technique. It is observed that the dynamic performance of the proposed CMSA optimized FOTIDF-II controller is better than the CMSA optimized PID. Furthermore, the performance of CMSA in the presence of different flexible AC transmission system (FACTs) and storage devices such as distributed power flow controller (DPFC), redox flow battery (RFB), and Tesla power wall (TPWA) battery (i.e Storage devices) along with proposed controller is compared with MSA-based PID controller with same combination of FACTs controller. Afterwards, superiority of the proposed controller has been judged in three area renewable-based AGC system under realistic environment.

In this paper, we propose a coordination scheme between balancing authority (BA) areas in an interconnected power system that decreases the regulation amount needed as well as the associated costs. Our approach aims at mimicking the behavior of the automatic generation control (AGC) system in a scenario where the whole interconnected system is assumed to be operated by a single BA area. To this end, we modify the area control error (ACE), which is fed into the AGC system of each BA area, and determine the AGC allocation based on a distributed algorithm that identifies the least expensive generators, with the mismatch of the total regulation needed being the only information exchanged between BA areas. We demonstrate the proposed ideas with the 3-machine 9-bus Western Electricity Coordinating Council (WECC) system, and compare the performance of our method with other three existing coordination approaches.