Automatic Generation Control System Research Articles

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

Frequency Control of Interconnected Power System via SBO assisted Simultaneous Tuning Strategy in Presence of DFIG

To achieve the objectives of the automatic generation control (AGC) of wind integrated interconnected power system (WIIPS), better tuning and control strategies are required. This paper suggests simultaneous tuning strategy for the AGC and wind system controllers to improve the stability of the WIIPS. The bulk amount of wind is integrated into the grid using the doubly fed induction generator (DFIG) and its simplified model is used to simulate the WIIPS. Initially, classical controllers are opted in AGC mechanism to reduce the frequency disturbances caused by the load and wind generation uncertainties. Further, classical controllers of WIIPS are replaced with cascade controllers to enhance the system frequency profile during the disturbances. The optimal set of solutions of the classical and cascade controllers are examined by the satin bowerbird optimizer (SBO) with the help of integral square error as fitness function of the optimization problem. Comparisons are provided to show the merits of the WIIPS over normal interconnected power system, cascade controller over classical controller and simultaneous tuning strategy over regular tuning mechanism.

Performance Analysis of Automatic Generation Control for a Multi-Area Interconnected System Using Genetic Algorithm and Particle Swarm Optimization Technique

The primary focus of this paper is to assess an interconnected power system using different optimization techniques. The main purpose is to employ different optimization techniques, including genetic algorithms (GA) and particle swarm optimization (PSO), to systematically enhance the performance of a multi-area or two-area automatic generation control (AGC) system, aiming to optimize the three PID controllers gain values and improve system performance under diverse loading conditions. Two case studies are conducted exploring different loading conditions in the megawatt (MW) range, including increasing load demand and decreasing load demand. The analysis involves four scenarios, covering without any kind of controller, another with solely a proportional integral derivative (PID) controller, a PID controller enhanced through a genetic algorithm (GA), and lastly, a PID controller improved through particle swarm optimization (PSO). The optimization process utilizes the integral time absolute error (ITAE) as the objective function to evaluate the system's performance. The simulation outcomes for ITAE, settling time, overshoot, and undershoot for frequency deviation of area one, area two, and power deviation in the tie-line are compared with previous similar studies to assess the novelty of this work. The article highlights the importance of the multi-area AGC system and the significance of different optimization techniques in improving its performance.

Leveraging Deep Learning to Increase the Success Rate of DoS Attacks in PMU-Based Automatic Generation Control Systems

Leveraging Deep Learning to Increase the Success Rate of DoS Attacks in PMU-Based Automatic Generation Control Systems

Joint contribution of RTEM and AGC system for frequency stabilisation in renewable energy integrated power system

AbstractIncreasing penetration of variable renewable generations will diminish system inertia thereby degrading the conventional frequency regulation capability. As a result, maintaining frequency stability will be more and more challenging with traditional approaches. Even though renewable sources integration would jeopardise the grid stability, it also presents several opportunities as well. For example, converter‐interfaced generators can bid in real‐time electricity markets (RTEM) and provide short‐time dispatch to minimise load‐generation mismatch. In this paper, an integrated approach that accommodates discrete automatic generation control (AGC) system with a regulation mileage framework and RTEM model to balance generation and consumption is proposed. The RTEM model is assumed to have a five‐minute dispatch trading interval which is to some extent comparable to the discrete AGC system. Furthermore, a fractional order PID (FOPID) controller is equipped in the AGC system whose parameters are tuned using a novel metaheuristic‐based optimisation called Lichtenberg Algorithm (LA). The proposed framework is tested in a three‐area system under several operating conditions to reveal the improvement in the dynamic performance of the system. The objective function is also incorporated with mileage payment that allows a fair compensation rule for all the units.

Optimization-Based Ramping Reserve Allocation of BESS for AGC Enhancement

The transient behavior of Automatic Generation Control (AGC) systems is a critical aspect of power system operation. Therefore, fully extracting the potential of Battery Energy Storage Systems (BESSs) for AGC enhancement is of paramount importance. In light of the challenges posed by diverse resource interconnections and the variability associated, we propose an online optimization scheme that can adapt to changes in an unknown and variable environment. To leverage the synergy between BESSs and Conventional Generators (CGs), we devise a variant of the Area Injection Error (AIE) as a measure to quantify the ramping needs. Based on this measure, we develop a distributed optimization algorithm with adaptive learning rates for the allocation of the ramping reserve. The algorithm restores a larger learning rate for compliance with the ramping needs upon detecting a potentially destabilizing event. We demonstrate the effectiveness and scalability of the proposed scheme through comprehensive case studies. It is shown that the proposed scheme can improve the transient behavior of the AGC system by bridging the gap in ramping capability.

Design and analysis of optimal AGC regulator for multi‐area power systems with TCPS and energy storage unit in deregulated environment

AbstractIn today's electric power network, fast changes are the norm, and auxiliary services such as automatic generation control (AGC) play a crucial role in maintaining the quality of the power supply. AGC ensures a balance between power generation, demand, and losses to sustain frequency stability and variation in tie‐line power within a set limit, even when the load changes. To accomplish this, it is always beneficial to consider new approaches to controlling the situation. This paper introduces the design of new AGC regulators that consider deviation in DC tie‐line power as an extra control variable for the turbine controller. In this study, three different deregulated power systems (containing two control areas), namely three thermal generations, the combination of one thermal and two hydro generations, and three hydro generations, including two power distribution companies (DISCOs) in each control area. These optimal AGC regulators are implemented in the proposed thermal–thermal–thermal system to carry out the various power contracts. The results have shown that the dynamic outcomes meet the AGC standards. To improve further dynamic results and the proposed systems' stability margins by incorporating redox flow batteries (RFB). In actual operating conditions, the system parameters do not remain constant due to the aging effect, assumptions made in simplifying the mathematical model, etc. Thus, ±50% deviations in the nominal value of system parameters to assess how well the optimal AGC regulators perform in the system under investigation. The suggested realistic AGC system incorporates many parameter fluctuations and works well with the optimal AGC regulators developed for the proposed plans. This study expanded to include a two‐area, thermal‐hydro‐hydro (THH) system under a deregulated framework connected via an asynchronous transmission link with or without a thyristor‐controlled phase shifter (TCPS) and RFB. The genetic algorithm (GA) can solve many issues and has global search, flexibility to varied problem types, intrinsic parallelism, and the ability to handle massive, complex search spaces. Thus, a two‐area deregulated hydro‐hydro‐hydro system with parallel tie‐lines utilizes GA‐PID, GA‐FOPID, and GA‐(1 + PI)‐FOPID controllers. Moreover, a random load disturbance (RLD) is employed in each section of the offered approach to show the resilience and elite performance of the proposed control strategy. To produce various dynamic reactions in the plans, MATLAB software version R2013a is employed.

Load Frequency Control and Optimization of Load Allocation for Multi-Area Power System with Electric Vehicle Charging Load

The integration of electric vehicles (EVs) into power systems has introduced new challenges for load frequency control due to the additional charging load they impose. This research article investigates the design and analysis of automatic load frequency control in a two-area power system, considering the presence of EV charging load. The study employs Artificial Neural Network (ANN) based PI control to manage both traditional load demand and the dynamic charging requirements of EVs. Maintaining a stable power system frequency and balancing generation with the EV charging load have become crucial tasks. Automatic Generation Control (AGC) or Load Frequency Control (LFC) systems need to adapt and account for the variability and uncertainty associated with EV charging patterns. The integration of ANN-based PI control provides an intelligent and adaptive approach to address these challenges. Using MATLAB, a power system model is simulated to evaluate the effectiveness of the proposed control scheme. This investigation conducts a comparative analysis of the system's frequency responses under various scenarios, including different EV charging load profiles. It highlights the benefits and challenges of utilizing ANN-based PI control to manage the combined load of traditional demand and EV charging. Moreover, the load distribution among the distribution stations varied from 0.08% to 12.50% when compared between particle swarm optimization and genetic algorithm, respectively. By considering the dynamic interaction between power system operation and EV charging, this research aims to enhance the reliability, efficiency and sustainability of power systems in the context of evolving transportation trends and the increasing electrification of vehicles.

Enhancing Grid Operation with Electric Vehicle Integration in Automatic Generation Control

Wind energy has been recognized as a clean energy source with significant potential for reducing carbon emissions. However, its inherent variability poses substantial challenges for power system operators due to its unpredictable nature. As a result, there is an increased dependence on conventional generation sources to uphold the power system balance, resulting in elevated operational costs and an upsurge in carbon emissions. Hence, an urgent need exists for alternative solutions that can reduce the burden on traditional generating units and optimize the utilization of reserves from non-fossil fuel technologies. Meanwhile, vehicle-to-grid (V2G) technology integration has emerged as a remedial approach to rectify power capacity shortages during grid operations, enhancing stability and reliability. This research focuses on harnessing electric vehicle (EV) storage capacity to compensate for power deficiencies caused by forecasting errors in large-scale wind energy-based power systems. A real-time dynamic power dispatch strategy is developed for the automatic generation control (AGC) system to integrate EVs and utilize their reserves optimally to reduce reliance on conventional power plants and increase system security. The results obtained from this study emphasize the significant prospects associated with the fusion of EVs and traditional power plants, offering a highly effective solution for mitigating real-time power imbalances in large-scale wind energy-based power systems.

Real-time economic and safe operation diagnosis of hydropower units

In order to ensure the safety and economy of the operation of hydropower units under the big data technology application platform and the automatic generation control system, and to tap the maximum power generation potential of the units, an effective method of economic operation evaluation of hydropower units combined with state diagnosis is proposed. Taking Unit 1 of Longtan Hydropower Station as an example, the economic evaluation and status diagnosis of the daily operating conditions are carried out, the real-time efficiency of the turbine is queried through the unit operating conditions data, and the economic operation of the unit is comprehensively evaluated using the standard water consumption index. In addition, the real-time operation condition of the unit shall be diagnosed according to the working condition zoning diagram, and the economic operation condition shall be combined with the unit state operation condition for joint assessment. It is strictly forbidden to operate the unit in the vibration zone and the vortex zone and it is determined that the unit can maximize the power generation efficiency after operating in the safe operation zone.

Automatic Generation Control System: The Impact of Battery Energy Storage in Multi Area Network

Renewable energy sources (RES) are currently experiencing significant expansion, and the integration of these sources into power systems necessitates more complex auxiliary facilities. Battery energy storage systems (BESS) have been widely recognized in recent literature as an effective means of enhancing control capabilities. This study focuses on the implementation of an Automatic Generation Control (AGC) system with the integration of BESS in a multi-area network. Maintaining system frequency, especially during peak loads, poses challenges for AGC systems. The objective of this study is to investigate the utilization of BESS to enhance AGC for frequency control in power system networks. Additionally, the effectiveness of BESS in improving frequency control in multi-area networks is demonstrated through several case studies. The AGC and BESS simulations were conducted using MATLAB Simulink to evaluate the proposed frequency control method's effectiveness.

Real-time validation of an automatic generation control system considering HPA-ISE with crow search algorithm optimized cascade FOPDN-FOPIDN controller

Real-time validation of an automatic generation control system considering HPA-ISE with crow search algorithm optimized cascade FOPDN-FOPIDN controller

Inertia Emulation Control Strategy of an Accurate Model of HVDC Link in a Multi-area Automatic Generation Control System Integrated Precise Wind Turbine System

Inertia Emulation Control Strategy of an Accurate Model of HVDC Link in a Multi-area Automatic Generation Control System Integrated Precise Wind Turbine System

Design of a quasi‐oppositional FBI based fuzzy PIDF (1 + PI) controller for frequency regulation with renewable energy sources and energy storage elements

AbstractIntegration of renewable energy sources (RESs) in modern power system has put a great challenge on researchers and system engineers to satisfactorily operate the power system when it is subjected to various disturbances. Automatic generation control (AGC) plays a very essential role in modern power system to control the tie‐line power flows and system frequencies. This paper illustrates about optimal design and implementation of various controllers for AGC of a two‐area non‐reheat thermal power system with some supplementary generation from dish stirling solar thermal system and wind‐turbine system. In addition to the generating sources, some energy storage systems like capacitive energy storage and redox flow battery are integrated to improve the dynamic stability of the system. Various controllers like fuzzy logic based PID controller, PID controller with filter (PIDF) and fuzzy PIDF (1 + PI) controller are used as supplementary controllers of the AGC system. The proposed controllers are optimally designed by FBI algorithm and QO‐FBI algorithm. A step load of 1% (0.01 pu) is applied in area‐1 to analyze the performances of the proposed controllers. Investigation of the study includes undershoot, overshoot and settling time of frequency and tie‐line power deviations. The percentage improvement of QO‐FBI based FPIDF (1 + PI) controller with respect to ICA based FPIDF (1 + PI) controller is examined. It is noticed that, AGC system equipped with QO‐FBI based FPIDF (1 + PI) controller outperforms ICA‐FPIDF (1 + PI) controller in all aspects. The superiority of the QO‐FBI based FPIDF (1 + PI) controller is validated under parametric variation in system parameters. Sensitivity analysis is also carried out by varying the inputs to the intermittent renewable sources. Comparative performance analysis reveals that QO‐FBI based FPIDF (1 + PI) controller performs better in comparison with other proposed controllers and a recently proposed ICA based FPIDF (1 + PI) controller.

Resilient virtual synchronous generator approach using DC-link capacitor energy for frequency support of interconnected renewable power systems

Modern interconnected power systems with many renewable energy sources (RESs) are more susceptible to disturbances than conventional power systems because they lack the inertia constant. Therefore, this study presents an advanced frequency control strategy for interconnected power systems considering high RESs penetration relying on the virtual inertia control (VIC) strategy-based high voltage direct current (HVDC) link, thus improving the dynamic frequency performance of future power systems. The HVDC link has been used as a DC capacitor which stores the electrostatic energy. This stored energy can supply the earliest active power support in automatic generation control system operation. Moreover, this paper develops the conventional VIC strategy based on the HVDC link by considering the dynamic behavior of a virtual rotor that mimics the inertia and damping properties of actual synchronous machines, thus stabilizing future renewable power systems. Also, this paper proposes a novel frequency control scheme based on the inertia emulation-based HVDC link and a virtual synchronous generator, which mimics the virtual rotor and virtual frequency control loops, to improve further the frequency regulation of low-inertia modern interconnected power systems. The parameters of the virtual synchronous generator scheme are optimally adjusted utilizing particle swarm optimization. The utility and superiority of the proposed VIC strategies based on the HVDC link are confirmed by contrasting them with the conventional VIC strategy relying on the HVDC link in the studied interconnected power system against various load/renewables interruptions, uncertainties, and physical restrictions. MATLAB/Simulink® software was used to simulate the results of the investigated system.

Optimal False Data Injection Attacks Against Power System Frequency Stability

The automatic generation control (AGC) is one of the core control systems in power grids that regulate frequency within the permissible range. However, its dependence on communication makes it highly vulnerable to cyber-attacks. An arbitrary false data injection attack (FDIA) on AGC frequency and tie-line flow measurements will likely be detectable by bad data detection methods; however, if an attack can be launched optimally, it often becomes stealthy. In this regard, we develop a framework of optimal FDIAs (OFDIAs) to demonstrate the feasibility of such attacks in the power system frequency control loop. We propose a linearized formulation of discretized power systems’ dynamics in an optimization framework to model OFDIAs that compromise the AGC system by corrupting tie-line flow and generators’ frequency measurements. Using the proposed formal modeling, we study the effects of two types of FDIAs, continuous and time-limited, on the frequency behavior in power grids. The results demonstrate that continuous OFDIAs can lead to severe consequences on a power grid’s performance, such as frequency instability. In contrast, the time-limited FDIAs can cause the frequency to fluctuate beyond the acceptable range, which may lead to the triggering of the frequency-based protection relays.

Dominant mode identification and influence mechanism investigation of frequency oscillations as affected by automatic generation control

Several frequency oscillation events have recently been observed in practical power grids, which are strongly related with dynamic process of AGC (automatic generation control). This kind of oscillations are caused by the phase relation mismatching between mechanical powers and rotational speeds and the detailed dynamic performance and oscillation mechanism are not clear and deserve in-depth investigations. Motivated by this, this paper establishes the state-space model of multi-area AGC systems containing different kinds of generator units and time-scale dynamics. Without distinguishing AGC and PFR (primary frequency regulation) modes, Homotopy method is applied to identify the dominant mode and thus analyze the key influencing factors of frequency oscillations affected by AGC dynamics. The method can clearly establish the congruent relationship between eigenvalues and state variables and is more rapid and intuitive to identify the dominant mode for AGC oscillations. The correctness and feasibility of the proposed method are validated by time-domain simulations and results analysis. Finally, simulations are performed in single-area as well as four-area system based on real data to demonstrate the proposed model and method. And relevant parameter influence mechanism and oscillation performances are investigated for AGC system oscillations.

Application of Empirical Bode Analysis for Delay-Margin Evaluation of Fractional-Order PI Controller in a Renewable Distributed Hybrid System

For an uninterrupted power supply, renewable energy promises to be a suitable alternative compared to the conventional sources. System delays or communication delays may cause significant synchronization imbalances between various components in big electrical grids. Since the properties of solar and wind generation constantly change with climatic circumstances, engineers encounter many difficulties when substituting sustainable power with conventional electricity. The computation delay margin may be leveraged to handle a time-delayed automatic generation control (AGC) system. In order to regulate a distributed hybrid renewable energy system in a three-area AGC configuration, this paper investigates the influence of the fractional integral order on the stable system’s delay parameter region. By changing the fractional order range, the delay margin can be increased, potentially broadening the time-delayed system’s stability region. The controller’s stability region has dependency on the order of fraction and the time delay. For this purpose, the asymptotic Bode diagram of the time-delayed fractional proportional-integral controller is determined. The gain and phase margins are used to calculate the delay margin for the application in discussion. The Honey Badger algorithm helps to adjust the controller parameters. It is also confirmed that the suggested controller is resilient to random load perturbations, nonlinearities, and parameter variations.

Special issue on “Optimal design and operation of energy systems”

Special issue on “Optimal design and operation of energy systems”

Detection of False Data Injection Attack in AGC System Based on Random Forest

False data injection attacks change the control effect of automatic generation control systems, which may cause a destructive impact on power systems. In this paper, the data from the regular operation of a system and the data from false data injection attacks in the historical data are studied and classified. The normal operating parameters and abnormal operation parameters under various attack scenarios are collected as samples for training the detection model based on time series. The random forest algorithm model is selected for detection through the comparison of detection effects, and various data training models are accumulated during the operation process to improve the model’s accuracy. Finally, Simulink simulation experiments verify the consistency of the detection results of the simulated attack algorithm. This detection method can realize real-time attack detection and synchronize the detection results to the database with high timeliness. It can be used in systems with rich data samples and has broad applicability.