Software and hardware platform for testing of Automatic Generation Control algorithms

A flexible automatic generation control (AGC) algorithm is described that has been developed for the Hellenic interconnected system. The algorithm includes the three well-known load frequency control (LFC) types (flat frequency, flat tie-line, and tie-line bias) and introduces four operating modes that depend on the magnitude of the measured area control error (ACE). These modes are the steady-state operating mode, the normal operating mode, and two distinct emergency modes. The LFC stability margins are calculated for the case of slow-acting, flat frequency control. The effects of integral feedback are also considered and analyzed. An approximate economic dispatch algorithm is presented that makes use of a predetermined table of the economic loading of units. Simulation results are reported that demonstrate satisfactory operation of the AGC system. Detailed transient stability program simulations demonstrate that a number of ACE measurements per measuring period are necessary to avoid dangerous false emergencies.< <ETX xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">&gt;</ETX>

This paper presents the analysis of Automatic generation control (AGC) of a two-area interconnected hydrothermal system under open market scenario. It also involves the optimization of integral controller employing ant colony system algorithm (ACS). Open transmission access and the evolving of more socialized companies for generation, transmission and distribution affects the formulation of AGC problem. So the traditional AGC two-area system is modified to take into account the effect of bilateral contracts on the dynamics. The behavior of real ants has inspired the development of the ACS algorithm, an improved version of the ant system (AS) algorithm, which reproduces the technique used by ants to construct their food recollection routes from their nest, and where a set of artificial ants cooperate to find the best solution through the interchange of the information contained in the pheromone deposits of the different trajectories. This metaheuristic approach has proven to be very robust when applied to global optimization problems and is favorably compared to other solution approaches such as genetic algorithms (GAs) and simulated annealing techniques. Gain setting of the integral controller is optimized using the ACS algorithm technique following a step load disturbance in either of the areas. The performance of the proposed approach outstands positively when compared to GAs, obtaining improved results with significant reductions in the solution time.

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.

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

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.

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.

With China's UHV network rapid development, three synchronizing national inter-connected grid: North China, East China and Central China have been established. The tie-line control utilizing automatic generation control (AGC) is becoming more complicated. With the alternation in power flow, the control mode for active power, the boundaries for each independent control area and the scope of the controlled generators are all affected. In order to adapt these changes, AGC system needs to reset the control parameters and optimize the control strategies. Real time operation and area control experiments are solutions for control strategy verification. This paper proposed an experimentation approach for AGC system control strategy verifications based on tie-line step disturbances. This approach has been successfully implemented on an independent control area for a provincial power grid, and the results have been used for AGC parameters and control strategies optimization.

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.

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.

In this paper, a high-voltage direct current (HVDC) tie-line has been accurately modeled and proposed for multi-area interconnected power system to enhance load frequency control and automatic generation control (AGC). HVDC tie-line has been modeled based on a simple first-order transfer function for AGC system. Time constant of this transfer function indicates the required time by HVDC to set DC current because of load perturbation in multi-area interconnected power systems. It is worth mentioning that the inexact and very simple model of HVDC-line cannot yield a reliable and correct response. Hence, an accurate and interpretable model is proposed for HVDC-link to control the AGC system. To verify the accuracy of the proposed model, it has been analytically compared with the conventional HVDC model. The integrated multi-area power system has also implemented the inertia emulation controller technique which enables AGC system to tackle the accumulated energy in HVDC-line’s capacitance.

ABSTRACTThe increasing scale and complexity of power systems, along with random fluctuations in power demand, modeling inaccuracies, structural changes in power networks, and time‐dependent parameter variations, have made the task of Automatic Generation Control (AGC) significantly challenging. A notable drop in system frequency can negatively impact the operation of electric clocks, magnetizing currents in transformers and induction motors, the constant speed of AC motors, continuous industrial processes, and the synchronization of various system components. Traditional control strategies struggle to manage these unpredictable dynamics in AGC systems. This study investigates AGC performance in a multi‐area, multi‐source hydrothermal interconnected power system using a two‐level‐Neuro‐Fuzzy Three‐Degrees of Freedom‐Proportional‐Derivative controller with a low filter coefficient (TLNF‐3DOFPDF) as a secondary control mechanism. The dynamic behavior of systems incorporating Super‐capacitor energy Storage (SCES) is evaluated and compared with systems lacking SCES across regions 1–3. Controller parameters are fine‐tuned using a novel metaheuristic method called the Skill Optimization Algorithm (SOA). The research assesses controller performance using multiple indices Integral of Time‐weighted Squared Error (ITSE), Integral of Squared Error (ISE), Integral of Time‐weighted Absolute Error (ITAE), and Integral of Absolute Error (IAE) and finds that ISE yields the most optimal results. Comprehensive evaluations indicate that the proposed TLNF‐3DOFPDF controller delivers superior performance compared to conventional controllers by achieving lower peak overshoots, reduced oscillations, and quicker settling times. Additionally, systems integrated with SCES demonstrate improved dynamic responses over those without storage. The study also examines the combined effect of high‐voltage direct current (HVDC) and alternating current (AC) tie‐lines on system dynamics, showing that this hybrid connection enhances system performance. Sensitivity analyses further confirm the robustness of the TLNF‐3DOFPDF controller, tuned via SOA, under varying load conditions. On average, the optimized controller reduces oscillation amplitudes by 65% relative to traditional methods, highlighting its effectiveness in modern AGC applications.

This paper reviews on the function of Automatic Generation Control (AGC) as an intelligent mechanism in enhancing electrical power systems dynamic performance at various perturbations; by sustaining the frequency and tie-line power within the scheduled limits. The review discloses the investigation on the flexible ac transmission system (FACTS), soft computing-based optimisation techniques, centralised approaches, decentralised methods, adaptive control approaches, and robust control techniques in AGC systems. Furthermore, the discussion focuses on applications of AGC with energy storage systems (ESS), high voltage direct current (HVDC) link, distributed generation, microgrids, and smart grids, in both traditional and liberalised power systems. Eventually, this paper concludes by highlighting the economic load dispatch methods, load forecasting methods, and digital control approach in AGC systems.

In the process of energy storage clusters participating in automatic generation control (AGC) of power systems, reasonable command power allocation is the key to effective execution of frequency regulation commands. Currently, the traditional AGC command tracking energy storage control strategy is to allocate according to the proportion of energy storage rated power. Due to the difference in power configuration of energy storage capacity in energy storage clusters, this will cause the state of charge (SOC) of small capacity energy storage power stations to reach the boundary first, resulting in limited output and command tracking errors, affecting the frequency regulation effect. This article proposes an energy storage cluster frequency modulation power optimization strategy that takes into account command tracking errors to address the above issues. Firstly, considering the basic process of automatic power generation control, a collaborative control architecture was designed for energy storage clusters to participate in AGC regulation; Then, a multi type energy storage regulation target considering the state of charge of energy storage was constructed. The model construction took into account the constraints of instruction power allocation and energy storage operation. Finally, the Lagrange multiplier method was used to solve the problem and obtain the output allocation of energy storage. The proposed strategy has been verified through simulation analysis to ensure the effective execution of AGC instructions and reduce the instruction tracking error of energy storage clusters participating in AGC regulation.

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.