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Ensuring stable power system performance is crucial for reliable grid operation. This study assesses various Load Frequency Control (LFC) strategies, including conventional PID, pole placement, Genetic Algorithm (GA)-optimized PID, Particle Swarm Optimization (PSO)-optimized PID, and an Artificial Neural Network (ANN)-based controller, in single and interconnected power grids. The results reveal that GA- and PSO-optimized PID outperform conventional methods, offering minimal overshoot and fast settling times. Pole placement strikes a balance between response time and stability, while the ANN controller demonstrates adaptability and quick rise times but exhibits higher overshoot and longer settling times compared to the optimization techniques. Tie-line bias control aids in frequency stabilization but presents challenges with overshoot and prolonged settling times. Notably, PSO-optimized PID emerges as a promising solution, effectively mitigating overshoot and achieving rapid frequency recovery. This study underscores the importance of tailored control strategies for optimal LFC, which are essential for enhancing power system stability and efficiency. Future research should explore the potential of advanced techniques, such as deep learning and reinforcement learning, to further improve control performance. [ABSTRACT FROM AUTHOR]

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Introduction: The increasing penetration of distributed generation (e.g., solar power and wind power) in the energy market has caused unpredictable disturbances in power systems and accelerated the application of intelligent control, such as reinforcement learning (RL), in automatic generation control (AGC). However, traditional RL cannot ensure constraint safety during training and frequently violates the constraints (e.g., frequency limitations), further threatening the safety and stability of grid operation. Methods: To address the safety issue, we propose a novel safe RL framework that combines expert experiences with the RL controller to achieve imitation-based RL. This method allows an initialized safe policy by imitating expert experiences to prevent random explorations at the beginning. Specifically, we first formulate the AGC problem mathematically as a Markov decision process. Then, the imitation mechanism is developed atop a soft actor-critic RL algorithm. Results and discussion: Finally, numerical studies are conducted with an IEEE 39-bus network, which show that the proposed method satisfies the frequency control performance standard better and improves the RL training efficiency. [ABSTRACT FROM AUTHOR]

To more reasonably and accurately control the output of new energy in large-scale wind farm clusters in the scenario of multiple nested cross sections， an optimization strategy for active control of wind farm clusters considering differences in their integration performance is proposed. This strategy considers the influence of new energy generation， the command power deviation of each wind farm and the deviation between the command power and the actual power. In cases of not meeting the network safety constraints， forced trigger strategy will be implemented based on the differences in integration performance of wind farms. The simulation example verifies that this strategy can accurately control the active power output of wind farm clusters and effectively improve the consumption rate of wind power.

Electricity network across the globe has witnessed an increase in penetration of distributed energy resources (DER) to the grid. A maiden attempt to integrate 50% loaded combined cycle gas turbine (CCGT) plant and doubly fed induction generator (DFIG) plant with DER is carried out for investigations to study frequency excursions in the hybrid system along with temperature excursions in the CCGT plant. A novel attempt is done to introduce modified beluga whale optimizer for tuning the controller gains and system parameters of the hybrid isolated system. This article emphasizes on the system behaviour of an isolated power system consisting of a CCGT plant and a DFIG plant integrated with distributed energy resources comprising of diesel engine generator, wind turbine generator and battery energy storage system. Among the classical secondary controllers, comparison of system responses is carried out to select the best among proportional, proportional–integral, and proportional–integral–derivative controllers. A modified beluga whale optimization algorithm is utilized to tune the controllers. Various investigations conducted with different turbo generators confirm the control strategy to the frequency of excursions. The hybrid CCGT—DFIG—DER system is also evaluated under random load demand, change in generation rate constraints and DFIG power output. [ABSTRACT FROM AUTHOR]

This paper presents a recent bio-inspired optimization algorithm for the critical topic of load frequency control (LFC) in an isolated multi-source power generating system. A specific model that emerges from the scientific literature and consists of reheated thermal, hydro and gas-turbine power sources is examined. The main goal is to tune the controller of each generating source i.e. find their optimal gains through bio-inspired optimization algorithms. Namely, the algorithms applied are the Whale Optimization Algorithm (WOA), Grey Wolf Optimizer (GWO), Particle Swarm Optimization (PSO), and the newly proposed Harris Hawks Optimization (HHO). It was observed that all optimization algorithms succeeded in obtaining controllers' gain values which ensure stable system operation. Also, the simulation results indicated the superiority of the proposed HHO algorithm over the other algorithms for all the considered scenarios. In this article, for the first time to the authors' knowledge extend an attempt has been made to optimize an isolated multi-source system using the bio-inspired HHO algorithm. The present study considered the LFC problem with a variety of different scenarios, taking into account both sub-cases of nonlinearities (e.g., generation rate constraint, boiler dynamics, etc.) and their combinations, as well as combinations of different controllers and load disturbances, which are not found in the literature. Last but not least, the robustness of the selected controller was further evaluated. The obtained results clearly demonstrated that the controller's gains established in normal conditions do not require retuning when critical system parameters undergo a significant variation. [ABSTRACT FROM AUTHOR]

This paper presents the use of a new hybrid optimization approach known as particle swarm optimization and grey wolf optimizer (PSO-GWO) for improving frequency stability load frequency control (LFC) in tow-area power systems. The approach consists in optimizing the fuzzy proportionalintegral-derivative (fuzzy-PID) controller parameters with meta-heuristic hybrid algorithm: PSO-GWO. This technique allows to have dynamic responses with the least possible frequency deviation in very short response times. The approach proposes to controls the tie-line power and the frequency deviation in the considered two-area power systems under variable perturbation in load and changing of system parameters in order to evaluate its effectiveness. The suggested hybrid algorithm-based fuzzy-PID controller is compared with various widely used control methods in the literature such as PID controller and algorithms such as PSO and GWO in order to evaluate its effectiveness and its robustness. Through the simulations carried out on MATLAB/Simulink, the proposed PSO-GWO fuzzy-PID and the objective function exhibit improved performance, achieving minimal objective values. The proposed technique proved to be quite powerful tool in the resolution of problems related to electrical power systems, particularly in load frequency control. [ABSTRACT FROM AUTHOR]

Currently, renewable energy sources (RESs) are gradually replacing traditional power sources that use fossil fuels. In some countries, such as Vietnam, RESs are developed on a massive scale and are concentrated in some key areas. This causes negative impacts on a power system when its transmission system is not deployed synchronously to release their capacity from these new renewable energy plants. An important challenge today is to ensure frequency stability in power systems with high uncertainty in RES output power. Additionally, the system requires solutions to prevent transmission line overloads during periods when RESs make a substantial contribution to the electricity generation capacity. Therefore, this paper builds an automatic generation control (AGC) system for a two-area power system with high penetration of RESs. This AGC system model aims to maintain system frequency stability amid unpredictable changes in RESs while also ensuring that tie-lines transmit the predetermined power levels to mitigate frequent congestion. By continuously monitoring and adjusting the system's frequency, the challenges posed by the inherent variability of RESs can be effectively mitigated. The AGC model is simulated on DIgSILENT PowerFactory software and tested with a 106-bus system. The simulation results of this study show that the AGC system operates effectively, ensuring that the frequency returns to the rated value and maintaining the exchange capacity on the tie-lines after occurrences of RES power decrease events. [ABSTRACT FROM AUTHOR]

Wind power is currently the most mature representative of sustainable energy generation technology, which has been developed and utilized on a large scale worldwide. The random and fluctuating nature of wind power output poses a threat to the secure and stable operation of the system. Consequently, the transmission of wind power has garnered considerable attention as a crucial factor in mitigating the challenges associated with wind power integration. In this paper, an artificial-intelligence-aided frequency coordination control strategy applicable to wind power transmission systems based on hybrid DC transmission technology is proposed. The line commutated converter (LCC) station at the sending end implements the strategy of auxiliary frequency control (AFC) and automatic generation control (AGC) to cooperate with each other in order to assist the system frequency regulation. The AFC controller is designed based on the variable forgetting factor recursive least squares (VFF-RLS) algorithm for system identification. First, the VFF-RLS algorithm is used to identify the open-loop transfer function of the system. Then, the AFC controller is designed based on the root locus method to achieve precise control of the system frequency. The DC line power modulation quantity is introduced in the AGC to automatically track the active power fluctuation and frequency deviation of the system. The AGC utilizes the classical proportional-integral (PI) control. By selecting the integrated time absolute error (ITAE) performance index to construct the reward function, and using a deep Q-network (DQN) for controller parameter optimization, it achieves improved regulation performance for the AGC. The voltage source converter (VSC) station at the receiving end implements an adaptive DC voltage droop control (ADC)strategy. Finally, the effectiveness and robustness of the proposed frequency control strategy are verified through simulation experiments. [ABSTRACT FROM AUTHOR]

As the proportion of renewable energy generation continues to increase, the participation of new energy stations with high-proportion energy storage in power system frequency regulation is of significant importance for stable and secure operation of the new power system. To address this issue, an energy storage control method based on quantum walks and model predictive control (MPC) has been proposed. First, historical frequency deviation signals and energy storage charge-discharge state signals are collected. Simulation data are generated through amplitude encoding and quantum walks, followed by quantum decoding. Subsequently, the decoded data are inputted into the MPC framework for real-time control, with parameters of the predictive model continuously adjusted through a feedback loop. Finally, a novel power system frequency regulation model with high-proportion new energy storage stations is constructed on the MATLAB/Simulink platform. Simulation verification is conducted with the proportional-integral-derivative (PID) and MPC methods as comparative approaches. Simulation results under step disturbances and random disturbances demonstrate that the proposed method exhibits stronger robustness and better control accuracy. [ABSTRACT FROM AUTHOR]

The primary goal of enhancing automatic generation control (AGC) in interconnected multi-area power systems is to ensure high-quality power generation and reliable distribution during emergencies. These systems still struggle with consistent stability and effective response under dynamic load conditions despite technological advancements. This research introduces a secondary controller designed for load frequency control (LFC) to maintain stability during unexpected load changes by optimally tuning the parameters of a Proportional–Integral–Derivative (PID) controller using pelican optimization algorithm (POA). An interconnected power system for ith multi-area is modeled in this study; meanwhile, for determining the optimal PID gain settings, a four-area interconnected power system is developed consisting of thermal, reheat thermal, hydroelectric, and gas turbine units based on the ith area model. A sensitivity analysis was conducted to validate the proposed controller's robustness under different load conditions (1%, 2%, and 10% step load perturbation) and adjusting nominal parameters (R, Tp, and Tij) within a range of ±25% and ±50%. The performance response indicates that the POA-optimized PID controller achieves superior performance in frequency stabilization and oscillation reduction, with the lowest integral time absolute error (ITAE) value showing improvements of 7.01%, 7.31%, 45.97%, and 50.57% over gray wolf optimization (GWO), Moth Flame Optimization Algorithm (MFOA), Particle Swarm Optimization (PSO), and Harris Hawks Optimization (HHO), respectively. [ABSTRACT FROM AUTHOR]

This work addresses the automatic-generation-control of multiple-area and sources under a restructured-situation. Sources within area-1 represent geothermal power plant, thermal, and gas, and area-2 sources represent thermal, hydro, and wind. An original endeavour is brought about to execute a controller with an admixture of proportional-derivative with filter (PDN) (integer-order) besides fractional-order proportional–integral (FOPI). Examination manifests excellence of PDN(FOPI) over integer order controllers likely integral, proportional–integral, proportional–integral–derivative-filter from perspective concerning depleted status of peak aberrations, extent-of-oscillations, and duration of settlement. To attain the controller’s attributes bioinspired meta-heuristic tunicate swarm algorithm is exercised. The occurrence of renewable sources makes arrangements meaningfully improved related to base thermal-gas-hydro arrangement. The action of hydrogen aqua electrolyzer-fuel cell and redox flow battery is examined using a PDN(FOPI) controller, providing noteworthy outcome in dynamic performance. The analysis is conducted under all the schemes of restructured situations.

This article investigates the impact of integrating Variable Renewable Energy (VRE), specifically solar energy from the Timimoun Photovoltaic Park, on the PIAT electrical grid stability in southern Algeria. The study focuses on how fluctuations in power demand and changes in weather conditions can affect grid frequency control, potentially leading to transient stability issues. To address these challenges, the research proposes the implementation of an Automatic Generation Control (AGC) system combined with the Particle Swarm Optimization (PSO) algorithm to optimize solar energy distribution. This approach effectively regulates real-time frequency deviations resulting from VRE integration, ensuring balanced supply and demand, and controllable power factor injection. The findings demonstrate that the integration of AGC and PSO stabilizes the frequency at the Timimoun Photovoltaic Park and reduces total active losses in the PIAT network by 13.88%. Additionally, strategic power factor control at the injection buses ensures optimal power quality and maximizes the utilization of the photovoltaic park, leading to a 4.84% reduction in the PIAT grid's reliance on gas turbines. This approach contributes to lowering operational costs, reducing carbon emissions, and supporting a transition to greener energy.

Automatic Generation Control (AGC) systems in smart grids are increasingly vulnerable to cyber-attacks, particularly False Data Injection (FDI) attacks, due to their reliance on information and communication technologies. These vulnerabilities pose significant threats to the reliable operation of power systems. To address this challenge, this research paper introduces the machine learning (ML) based cyberattack detection technique designed to identify FDI attacks with the highest accuracy proficiently. The study involves a comprehensive analysis of three features: the original discrete signal feature, the cycle-to-cycle-based feature, and the sample-to-sample-based feature. These features are utilized for detecting FDI attacks and distinguishing them from normal load variations. The research meticulously collects data by simulating diverse FDI attacks, including step attacks, pulse attacks, random attacks, and normal load variation cases on an AGC power system. Four ML classifiers are selected and compared for the classification task. The simulation results reveal that the sample-to-sample-based feature proves highly effective in distinguishing FDI attacks compared to the original signal and cycle-to-cycle-based features. Notably, the results indicate that the random subspace ensemble (RaSE) classifier, utilizing sample-to-sample-based features, effectively identifies and classifies all normal and FDI attacks. This research provides valuable insights into the potential of ML techniques for enhancing FDI attack detection in the AGC of power systems. It provides a potential pathway for overcoming the limitations associated with traditional model-based FDI detection methods.

Owing to unpredicted power demand, integration of distributed generators and parametric disparity in the system cause the power system more intricacy which in turn affects the frequency and power fluctuation critically. To mitigate these phenomena, an intelligent and efficient automatic generation control (AGC) is indispensable. Therefore, in this paper, an intelligent and robust controller named fuzzy sliding mode controller (FSMC) has been recommended to tackle the AGC problem in a deregulated power system in the presence of distributed generators effectively. Again, to manifest its dominance over SMC, fuzzy-PID and PID controllers, dynamic response of the deregulated system under Poolco, Bilateral and Contract violation conditions has been evaluated and compared. Further, to support the FSMC controller’s ability, another test model has been taken for the AGC study. The gains of these controllers have been enumerated by Gannet Optimization Algorithm (GOA) subjecting to a minimization problem. Exploration phase of GOA involves two steps, namely U- & V-shaped diving patterns of Gannets and exploitation phase again involves two steps namely sudden rotation and random walk of Gannets. These four steps ensures quick convergence of the objective function to its optimal value. Apart from transient response under different power transaction conditions, parametric uncertainties of the system, solar and wind power variation and abrupt load change have been considered to support the robustness & credibility of FSMC controller. In addition to this, the stability of the proposed model has been analyzed in the frequency domain. Finally, the MATLAB/SIMULINK based transient response has been validated by a real-time simulator using OPAL-RT-4510, Xilinx Kintex-7 FPGA software.

Abstract In the paper, the maiden application of Tilted Integral Fractional Derivative with Filter plus Fractional Derivative controller (TIDμ1NDμ2), called TIFDNFD controller, is proposed to achieve load frequency control (LFC) of two area‐multi source power system. In the study, both areas include thermal power, hydropower, and gas turbine power systems. In this study, generation rate constraint and governor dead‐band are integrated into the power system to evaluate the performance of the controller in a non‐linear environment. The wild horse optimizer (WHO) is used for controller parameter tuning in the LFC problem for the first time. The performance of the proposed controller is demonstrated by comparing it with the recently published studies including different controllers. In this study, to obtain more realistic results, solar photovoltaic (PV) power, wind power, and load demand with stochastic form have been added to the power system. In addition, the dynamic effect of the hydrogen storage system, consisting of an electrolyser and a fuel cell, is considered a grid supporter. The superiority of the WHO algorithm in optimization has been verified by comparing it with three optimization algorithms recently proposed. Finally, the resilience and robustness of the power system are evaluated in perturbed system parameters.

The modern era is witnessing a growing demand for sustainable and eco-friendly power sources. An interconnected power system capable of seamlessly integrating electric vehicles and renewable energy resources is being considered as a viable solution. However, this technology has some drawbacks, such as its lower system inertia, which limits its ability to respond to load capabilities. To overcome this issue, a Hybrid Energy Storage System (HESS) can be integrated with new techniques to enhance performance. This paper proposes a new Quasi Opposition Arithmetic Optimization Algorithm (QOAOA) optimized Fractional Order Proportional Integral Derivative with Filter (FOPIDN) controller cascaded One Plus Tilted Derivative (1 + TD) controller with HESS of super-capacitor (SC) and Redox Flow Battery (RFB) to mitigate frequency and tie-line power variations in a multi-area restructured smart-grid system. The proposed controller's performance is evaluated under various conditions, such as load fluctuations, wind speed variations, solar irradiation, Governor Dead Band (GDB), and generation rate limitations. For unsystematic load the improvement in the performance of the proposed controller indicated by maximum variation in frequency ( Δ f 1 , Δ f 2 , Δ f 3 ) and power change on the tie-lines ( Δ P tie 12 , Δ P tie 13 ) over FOPIDN controller is (98.65%, 48.78%, 52.52%) and (75.0%, 76.1%). The system performance is also analyzed when HESS devices are integrated into the three-area system, taking into account the varying nature of wind speed and solar irradiation. The improvement in performance indicated by maximum variation in frequency ( Δ f 1 , Δ f 2 , Δ f 3 ) and power change on the tie-lines Δ P tie 23 by incorporating HESS in the system is (45.21%, 33.33%, 29.59%) and 46.95% over without energy storage system. The proposed controller's outcomes are validated using real-time hardware-in-the-loop (HIL) simulation with OPAL-RT. [ABSTRACT FROM AUTHOR]

Human activities overwhelm our environment with CO2 and other global warming issues. The current electricity landscape necessitates a superior, continuous power supply and addressing such environmental concerns. These issues can be resolved by incorporating renewable energy sources (RESs) into the utility grid. Thus, this paper presents an optimized hybrid fuzzy logic self-tuning PID controller to control the automatic generation control (AGC) of various renewable sources. This controller regulates the frequency deviations of the power system and governs the change in the tie-line load of a multi-area hybrid energy system composed of wind, biomass, and photovoltaic energy sources. MATLAB Simulink software was applied to design and test the system. The PID controller has been tuned using four algorithms, namely, genetic algorithm (GA), pattern search (PS), simulated annealing (SA), and particle swarm optimization (PSO), and we compared the results with the proposed novel optimized PID controller (GA-fuzzy logic self-tuning technique) to validate it. The results show the superiority of the proposed hybrid GA-fuzzy logic self-tuning algorithm over the other algorithms in bringing the power system back to its regular operation. The paper also proposes an operation strategy to lower the utilization of biomass energy in the presence of other renewable energy sources. [ABSTRACT FROM AUTHOR]

This article presents a method of designing an optimal Proportional-Integral-Derivative (PID) controller for Automatic Generation Control (AGC) of a two-area interconnected thermal system. In addition, an application of Salp Swarm Algirithm (SSA) in order to design PID controllers is proposed. A method inspired by salp foraging behavior. The journey of the Salp group in the sea, by random the population of the salp and separated into leader salp and follower salp. The leader moves towards the food source and the rest of the followers follow the salp positioned in front of themselves. The parameters of PID were achieved by trial and error methods by designers. As an interconneted thermal power system with a Governor Deadband (GDB) is nonlinear and dynamic system, the Integral Squared Error (ISE) criterion has been used as objective function to find optimum controller gain. In order to solve this problem, SSA is proposed to concurrently tune PID gains of the PID controllers to minimize frequency deviations and tie-line power deviations by simulation the interconnected thermal power system. Compare of tuning PID controller from each optimization technique SSA and PSO. Simulation results clearly show that the performance of the system after taking SLP into the interconnected thermal power system that tuning by SSA and PSO and the robustness of the optimal PID controllers are superior to the conventional PID controllers in terms of settling time (-10.46 % in area 1, +0.87 % in area 2 and +0.57 % in power tie-line), overshoot (-10.61 % in area 1 and -10.87 % in area 2) that obtain by (SSA) method less value than the value that obtain by (PSO) method and the power tie-line. Integral Absolute Error (IAE) (-0.38 %) that obtain by (SSA) few errors. Comparisons to the tuned with Particle Swarm Optimization (PSO) approaches is also included. [ABSTRACT FROM AUTHOR]

This article introduces hybrid Arithmetic Optimization Algorithm (AOA) and Local Unimodal Sampling (hAOA-LUS)-based fractional order (FO) proportional derivative (PD) cascaded with one plus proportional integral (1+PI) controller for automatic generation control of power system with renewable energy sources (RES) and electric vehicle (EV). The control-area 1 has thermal, hydro, gas, and wind power generators and the same true for control area 2, which uses thermal, hydro, gas, and solar energy sources. Initially, Proportional-Integral-Derivative (PID) controllers are taken into consideration and it has been demonstrated that hAOA-LUS outperforms as compared to the AOA, Particle Swarm Optimization, and Generic Algorithm (GA). The assessing of overshoots, undershoots, and different integral errors of frequency and tie-line power deviations after step load perturbations in each area allows for performance comparison of the proposed power system. In the next stage, EVs are considered in each area and the controller parameters are optimized by hAOA-LUS techniques in the presence of RES and EV. A comparative analysis is carried out by hAOA-LUS-tuned FO PD(1+PI) controller with PID as well integer ordered PD(1+PI) for various cases so as to validate the superiority of the anticipated controller. The results from MATLAB and OPAL-RT are compared in order to verify the authenticate feasibility of method. [ABSTRACT FROM AUTHOR]

In the paper, the maiden application of Tilted Integral Fractional Derivative with Filter plus Fractional Derivative controller (TIDμ1NDμ2$TI{D^{{\mu _1}}}N{D^{{\mu _2}}}$), called TIFDNFD controller, is proposed to achieve load frequency control (LFC) of two area‐multi source power system. In the study, both areas include thermal power, hydropower, and gas turbine power systems. In this study, generation rate constraint and governor dead‐band are integrated into the power system to evaluate the performance of the controller in a non‐linear environment. The wild horse optimizer (WHO) is used for controller parameter tuning in the LFC problem for the first time. The performance of the proposed controller is demonstrated by comparing it with the recently published studies including different controllers. In this study, to obtain more realistic results, solar photovoltaic (PV) power, wind power, and load demand with stochastic form have been added to the power system. In addition, the dynamic effect of the hydrogen storage system, consisting of an electrolyser and a fuel cell, is considered a grid supporter. The superiority of the WHO algorithm in optimization has been verified by comparing it with three optimization algorithms recently proposed. Finally, the resilience and robustness of the power system are evaluated in perturbed system parameters. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering for Thermal Energy & Power / Reneng Dongli Gongcheng is the property of Journal of Engineering for Thermal Energy & Power and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The performance improvement of Automatic Generation Control (AGC) in a Multi Area Solar Thermal Power System (MASTPS) under a deregulated environment is considered as MIPS and their performance is compared using various controllers to improve the performance of AGC in the proposed MIPS. This study takes into account a three-area conventional thermal power system in conjunction with a solar thermal power plant in each area, along with the proper generation rate constraints (GRC). The performance of the different controllers, including the Adaptive Neuro Fuzzy Inference System (ANFIS) controller and the Droop Controller (DC), Fuzzy Logic Controller (FLC), Artificial Neural Network (ANN), and Fractional Order PID (FOPID) controllers, is compared in the proposed AGC deregulated power system. The proposed MIPS is designed and implemented to make the system as realistic as possible with real world scenarios. The recommended MIPS is being simulated in MALTAB Simulink environment and simulated for multiple times to investigate the performance improvement in AGC using the adopted controllers with 0.1 per unit of step load perturbation to verify its dynamic characteristics. According to the investigation, the suggested ANFIS controller improves the AGC performance in proposed MIPS and offers better dynamic performance than other controllers. [ABSTRACT FROM AUTHOR]

This article highlights the mitigation of automatic generation control (AGC) problem considering various FACTS devices and inertia emulation control (INEC) strategies based accurate model of HVDC links using various energy storage systems (ESS). The proposed system comprises a three-area hydro-thermal system integrated with renewable energy sources (RES) such as a realistic dish-Stirling solar thermal system (RDSTS) and a precise wind turbine system (PWTS). A maiden effort has been made to design a novel secondary controller called three degrees of freedom tilt-integral-derivative with filter coefficient (3DOF-TIDN) for the proposed system, and their parameters are successfully optimized by the bird swarm algorithm (BSA). The comparative analysis of system dynamics corresponding to the proposed 3DOF-TIDN controller exhibits betters dynamics over 3DOF-PID and 2DOF-PID controllers. The system dynamic performances are also evaluated considering RDSTS and PWTS in all the areas, and it has been evident that the incorporation of RDSTS and PWTS improves system dynamics. The system with AHVDC link shows quickly damped out the system oscillation. Moreover, the study on inertia emulation using various ESS such as redox flow battery (RFB), ultra-capacitor (UC) and battery energy storage (BES) systems inferred that the system's initial response with RFB is better than others. Furthermore, the integration of various FACTS devices such as thyristor control phase shifter (TCPS), static synchronous series capacitor (SSSC) and Gate-Controlled Series Capacitor (GCSC) revealed that the system dynamics marginally enhanced with GCSC than TCPS and SSSC. [ABSTRACT FROM AUTHOR]

In the modern era, the demand for an interconnected power system, which can integrate electric vehicles and renewable energy sources is increasing day by day. The primary goal of this demand is to make a sustainable and green power source, which can be fulfilled by utilizing Electric vehicles and renewable energy sources. However, there are some demerits of this technology, its lesser system inertia and hence it is not sufficient to respond to the required load capabilities. Further, the approach of electric vehicles with moving batteries can enable higher performance and resolve the issue. In this current analysis, the vehicles to grid idea (V2G) technique has been discussed, which can react as automatic generation control (AGC) in a three-area deregulated environment including hydro, thermal, and gas turbine units. In this type of power grid, all the sources such as Solar, wind, geothermal, and DEG power is also incorporated. To grip various ambiguities, the current study proposed a cascade combination of Interval type-2 fuzzy and Fractional Order Proportional-integral-derivative (FOPIDN) controllers. Further, the current work proposed a modified quasi-opposition Arithmetic Optimization Algorithm (QOAOA) to tune the scaling factor and membership function of an interval type-2 fuzzy FOPIDN controller. To discuss the significance of the anticipated controller, the estimated outputs have been compared with previously reported controllers and optimization techniques. The effect of constant and variations in DG, the penetration level of (PEVs) in various operating modes, subjected to step and random load disturbances have been focused on. Finally, to validate the outcomes of the proposed controller, a real-time (RT) hardware-in-the-loop (HIL) simulation has been adopted by using OPAL-RT. [ABSTRACT FROM AUTHOR]

The intensifying progression in the transformation of the power grid towards a new generation power system introduces frequency regulation challenges in operations which can be overcome with substantial benefits from the latest robust control approaches along with suitable smart optimizing tools. Additionally, with the adoption of smart and intelligent control schemes, the latest communication technologies with the extensive placement of smart devices, there will be significant growth in real-time system measurements. Under this trend, an adaptive fuzzy PID (A-FLC-PID) system will play a vital role in reducing transient time. In this article, a novel adaptive fuzzy control architecture intended for load frequency control with a supplementary hybrid moth flame optimization pattern search (h-MFO-PS) algorithm is presented. The proposed approach is designed to optimize the controller gains for the eventual fitness of the objective function. The hybrid algorithm smartly updates the scaling factors of the adaptive fuzzy controller by considering various proportional and integral thresholds. Simulations are performed on two areas hydrothermal system with the gas unit. To validate the research outcomes and measure the effectiveness of the proposed architecture, the outcomes are compared with recent research approaches and practicability is tested using an OPAL-RT setup. The sturdiness of the concerned approach is studied with a wide range of parameterization and loading conditions. It is observed that the h-MFO-PS scaled A-FLC-PID controller establishes superior improvement in frequency regulation. [ABSTRACT FROM AUTHOR]

This paper investigates the automatic generation control in a deregulated environment for three unequal interconnected power systems involving renewable energy sources and electric vehicles. In each area, there are reheat thermal plants combined with various renewable energy sources, including solar photovoltaic plant, wind turbine plant, hydropower plant, biodiesel engine generators, biogas turbine generators, and electric vehicles. Non-linearities, such as generation rate constraints and governor dead bands, are taken into consideration. This paper proposes a new cascade controller called the two degree of freedom Tilt integral Derivative with Filter cascaded with Fractional order Tilt Integral controller using a new Zebra optimization algorithm to optimize controller parameters. Several case studies are done to illustrate the robustness of the proposed controller and optimization. It has been observed that the deviation in frequency’s settling time, peak overshoots, and undershoots are better with including EV by 55%,71.4% and 44.4% in Area1, 55.6%, 62.5% and 14.2% in Area 2, 30.3% and 46.15% in Area 3 than the system without EV.

This study focuses on the automatic generation control (AGC) system, which is crucial for maintaining balance between power generation and demand in power systems. The implementation of the AGC system needs to be more precise due to the increasing uncertainty surrounding renewable energy sources (RESs) and changes in demand. The objective of this study is to investigate the AGC functions in a two-area hybrid power system that combines a PV system with a reheat thermal system. To improve system performance, we utilize a proportional-integral (PI) controller. We utilized a recently developed optimization method, RIME, for tuning controller parameters. This technique has not been studied before in AGC processes. Furthermore, the optimization procedure utilizes a modified version of the integral of time-multiplied absolute error (ITAE) objective function. The study compares the performance of the RIME-tuned PI controller under various scenarios, including changes in thermal system load, load variations in both areas, and robustness considerations, with well-known techniques in the literature, such as the black widow optimization algorithm (BWOA), the salp swarm algorithm (SSA), the shuffled frog leaping algorithm (SFLA), the firefly algorithm (FA) and the genetic algorithm (GA). Our comparative study demonstrates that the proposed controller outperforms state-of-the-art approaches in terms of overshoot values and damping durations for both system frequency and tie-line power changes. The study provides valuable information on the effectiveness of the RIME-tuned PI controller in controlling AGC processes in complex hybrid power systems.

This paper proposes the design of Discrete Centralized Optimal Quadratic Automatic Generation Control (COQAGC) based on the functional minimization method (FMM) and optimal control theory for interconnected power systems. The cost function and FMM design requirements are defined in terms of area control errors, integral area control errors, and control signals. FMM is an optimal method, an easy and systematic approach for constructing and selecting state and control weighting matrices. The performance of COQAGC on discrete two-area interconnected power systems with identical _non-reheat thermal turbines has been studied using 1% and 5% step load perturbations (SLPs) and sensitivity analysis. The study has been extended to investigate the performance of COQAGC on discrete multi-area multi-source interconnected power systems with wind turbines. The simulation results revealed that developed COQAGC-based FMM improves the power system dynamics in terms of the steady-state performance and robustness against SLPs and parameter variations in comparison with controllers from the literature. The developed method can be extended and implemented on large complex multi-area power systems.

The current task explores automatic generation control knowledge under old-style circumstances for a triple-arena scheme. Sources in area-1 are thermal-solar thermal (ST); thermalgeothermal power plant (GPP) in area-2 and thermal-hydro in area-3. An original endeavour has been set out to execute a new performance index named hybrid peak area integral squared error (HPA-ISE) and two-stage controller with amalgamation of proportional-integral and fractional order proportional-derivative, hence named as PI(FOPD). The performance of PI(FOPD) has been compared with varied controllers like proportional-integral (PI), proportional-integralderivative (PID). Various investigation express excellency of PI(FOPD) controller over other controller from outlook regarding lessened level of peak anomalies and time duration for settling. Thus, PI(FOPD) controller’s excellent performance is stated when comparison is undergone for a three-area basic thermal system. The above said controller’s gains and related parameters are developed by the aid of Artificial Rabbit Optimization (ARO). Also, studies with HPA-ISE enhances system dynamics over ISE. Moreover, a study on various area capacity ratios (ACR) suggests that high ACR shows better dynamics. The basic thermal system is united with renewable sources ST in area-1 also GPP in area-2. Also, hydro unit is installed in area-3. The performance of this new combination of system is compared with the basic thermal system using PI(FOPD) controller. It is detected that dynamic presentation of new system is improved. Action in existence of redox flow battery is also examined which provides with noteworthy outcome. PI(FOPD) parameters values at nominal condition are appropriate for higher value of disturbance without need for optimization.

To improve automatic generation control (AGC) performance and reduce the wastage of regulation resources in interconnected grids including highproportion renewable energy, a multi-area integrated AGC (MAI-AGC) framework is proposed to solve the coordination problem of secondary frequency regulation between different areas. In addition, a cocktail exploration multi-agent deep deterministic policy gradient (CE-MADDPG) algorithm is proposed as the framework algorithm. In this algorithm, the controller and power distributor of an area are combined into a single agent which can directly output the power generation command of different units. Moreover, the cocktail exploration strategy as well as various other techniques are introduced to improve the robustness of the framework. Through centralized training and decentralized execution, the proposed method can nonlinearly and adaptively derive the optimal coordinated control strategies for multiple agents and is verified on the two-area LFC model of southwest China and the four-area LFC model of the China Southern Grid (CSG). [ABSTRACT FROM AUTHOR]

Maintaining the reliability of the modern power system is becoming increasingly challenging due to the continuous growth in its size and complexity. One of the most significant challenges PS faces is frequency regulation, which is essential for maintaining a stable and reliable operation of PS. This article investigates the automatic generation control (AGC) of a multi-area PS considering partial loading scheduled in a hydro and thermal generating unit. A bird swarm algorithm (BSA) based two-degree of freedom tilt integral derivative (2DOF-TID) controller has been suggested. The superiority of the optimal 2DOF-TID controller has been selected by comparing the system dynamics with other conventional controllers. Also, the ascendancy of suggested BSA has been shown over the grasshopper algorithm and particle swarm optimization techniques. The investigation on partial loading schedules for the hydro-thermal system showed that the system's dynamic response deteriorates when loading on hydro units exceeds their nominal loading. Also, system dynamics are enhanced when thermal units are loaded beyond their capacity. The investigations are also conducted to analyse the effect of high voltage direct current (HVDC) link and virtual inertia emulation with an energy storage system. The studies with alternating current (AC)/HVDC and virtual inertia revealed that dynamic responses are better with the latter when compared with AC and HVDC links alone. • A novel 2DOF-TID controller has been proposed for a three-area hydro-thermal AGC system. • The controller gains and other parameters are optimized using a novel meta-heuristic algorithm called the BSA. • The supremacy of BSA has been analyzed over PSO and GHA. • The performance of the system, considering different loading schedules in thermal and hydro generating units is analyzed. • The effects of a parallel AC/HVDC link and virtual inertia with RFB on system dynamics are analyzed. [ABSTRACT FROM AUTHOR]

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To integrate large-scale variable renewable energy resources (RESs) in modern power grids, the coordinating control area (CA) operation is the most cost-effective method. This article reviews the technical aspects of CA cooperation. Firstly, a brief overview of the active balancing control within each CA is discussed. Secondly, three general control structures for CA cooperation are innovatively proposed, the corresponding implementation details are analyzed, and some representative technologies are also provided in the systematic analysis. Then, some future research directions such as large-scale power sharing by DC, active power control of RES bases, and the new structure for distributed energy resources in local power grids are prospected. Finally, the changes in power systems brought about by their evolution and importance for further promoting cooperation between CAs are summarized. [ABSTRACT FROM AUTHOR]

This article deals with the AGC design of diverse sources-integrated multi-area systems. Area 1 is the combination of solar PV, wind farm, and thermal units. Areas 2 and 3 combine wind farms, hydropower, and thermal units. Skill optimization algorithm (SOA) is used to simultaneously optimize secondary controllers' gains and other parameters. The various helpful performance indices, like ITSE, ISE, ITAE, and IAE, are compared, and it found that the ISE is the best performance indices. The proposed cascaded NF-PDF-PIDF controller outperforms PDF-PIDF and PIDF in terms of the system's dynamic performance when compared to those two. It is also found that integrating solar PV and energy storage, like SMES, proves dynamic responses. The impacts of combining HVDC and AC tie-lines on system response are also studied. It is found that parallel AC-HVDC tie-lines provide better dynamics than AC tie-lines alone. Finally, the resiliency of the SOA-optimized proposed cascaded (CNF-PDF-PIDF) has been applied for a broad change of system loading. It is revealed that the CNF-PDF-PIDF controller's performances at nominal conditions are resilient, and there is no need to reset the controller multiple times when the system loading varies. [ABSTRACT FROM AUTHOR]

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network's frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers' efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller. [ABSTRACT FROM AUTHOR]

The incorporation of intermittent and stochastic renewable energy into a microgrid creates frequent fluctuations, which provides new challenges in frequency control. This paper deals with the frequency control problem in the islanded AC microgrid (IACMG) via a model-free deep reinforcement learning (DRL) method, which includes offline learning and online control. Twin-delayed deep deterministic policy gradient is involved to improve the performance of the agent to minimise the frequency deviation. The advantage of the proposed method is self-adaptive to the uncertain IACMG model including renewable energy sources. Finally, the effectiveness and robustness of the proposed controller is demonstrated by four simulation scenarios. [ABSTRACT FROM AUTHOR]

The electric power system is rapidly transforming to address the urgent need for decarbonization and combat climate change. Integration of renewable energy sources into the power grid is accelerating, creating new challenges such as intermittency and uncertainty. To address these challenges, this paper proposes a new design of automatic generation control (AGC) ancillary service cost allocation based on the causer-pays rule. The proposed design treats reserves as inventory and aims to minimize them by allocating costs among consumers based on the causative factors for AGC operation. Two cost-allocation methods based on the causer-pays principle are introduced. The first method distributes costs according to the changes in loads causing ancillary service operation, while the second method considers opportunity costs. The case study on the IEEE 39 Bus System demonstrates that the proposed methods incentivize consumers to minimize volatility, resulting in reduced reserve requirements for system operation. In particular, the opportunity cost-based approach encourages loads and variable renewable energy (VRE) to actively reduce volatility, resulting in more efficient power system operation. In conclusion, the novel AGC ancillary service cost allocation methods offer a promising strategy for minimizing spinning reserves, increasing the power system's efficiency, and incentivizing consumers to actively participate in frequency regulation for a more sustainable and reliable electricity market. [ABSTRACT FROM AUTHOR]

In the upcoming epochs, conventional energy may deplete soon. Thus, the use of conventional energy in the power industries need to be supplemented by non‐conventional energy resources. This would result in loss of synchronisms in the power grids owing to the fact that solar and wind alternate their attributes expeditiously with change in atmospheric phenomenon. To ameliorate frequency deviation within a specific range automatic generation control (AGC) implements forced allowance on system operation. A three area thermal with photovoltaic (PV), electric vehicle (EV), wind system is considered under deregulated environment to develop and to judge the efficacy of newly developed cascade fractional order hybrid controller combination of (FOTID & 3DOF‐PID). Comparing the aforementioned controller to other controllers such as the three degree of freedom proportional‐integral‐derivative (3DOF‐PID), the fractional order tilt‐integral‐derivative (FOTID) and the proportional‐integral‐derivative (PID) justifies the system's effectiveness. This assessment has been accomplished by a trendy optimization technique such as hybrid whale optimization algorithm (HWOA). However, the main intent of this write‐up is to fabricate a cascade fractional order (CC‐FO) hybrid controller that would act as the new control mechanism for the proposed system under deregulated scenario. It has been found that the suggested CC‐FO hybrid controller stabilizes the system (i.e., Under step load disruptions, frequency deviation and tie‐line power become zero) in the shortest amount of time possible. Additionally, it is seen that the recommended controller can control a wide range of nominal loading circumstances and system characteristics, demonstrating its robustness. [ABSTRACT FROM AUTHOR]

With the increase of new energy power generation, the frequency fluctuation of the power grid intensifies. To accelerate the elimination of power grid frequency deviation, the automatic generation control(AGC) command optimal dispatching method was proposed based on the relationship between power output changes and maximum ramp rates of units. Firstly, data segments with continuous increasing and decreasing trends were obtained from historical data of units by the piecewise linear representation method. The amplitude change of each data segment and the corresponding ramp rate was calculated. The relationship between the unit output changes and the maximum ramp rates was established by polynomial fitting based on appropriate samples of amplitude change and ramp rates. Then, considering the constraints of output range and ramp rates under different output changes of units, the AGC demands of power generation units were determined by minimizing the AGC demand response time of the power grid. Finally, numerical and industrial examples including a comparison with existing dispatching methods were provided to illustrate the effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

Renewable energy has recently attracted a lot of interest due to its low cost and long-term sustainability. Integration of a photovoltaic (PV) system into an existing power system on a large scale is a challenging task. Due to irregular irradiance and an inertia-less system, frequency regulation is a cumbersome task. So, load frequency control is recommended in this article for multi-area power systems that include PV and thermal plant sources. The performance of four competing controllers, namely proportional integral derivative (PID), fractional-order PID, tilt-integral-derivative (TID), and fractional-order TID (FOTID), is investigated and compared. For obtaining the best-performing parameters, the honey badger algorithm (HBA) is implemented. Furthermore, uncertainty is taken into account by varying the system parameters from -20% to 20% in steps of 10%. The suggested competing controllers' performance is evaluated using a dynamic load. The performance of the proposed HBA-based FOTID controller is also studied against a randomly varying load applied in area-1. The work is further extended to a four-area system. Finally, it is observed that the HBA-based FOTID controller exhibits superior performance in maintaining the system's frequency and tie-line power when it is subjected to a disturbance. [ABSTRACT FROM AUTHOR]

This work addresses the automatic-generation-control of multiple-area and sources under a restructured-situation. Sources within area-1 represent geothermal power plant, thermal, and gas, and area-2 sources represent thermal, hydro, and wind. An original endeavour is brought about to execute a controller with an admixture of proportional-derivative with filter (PDN) (integer-order) besides fractional-order proportional–integral (FOPI). Examination manifests excellence of PDN(FOPI) over integer order controllers likely integral, proportional–integral, proportional–integral–derivative-filter from perspective concerning depleted status of peak aberrations, extent-of-oscillations, and duration of settlement. To attain the controller’s attributes bioinspired meta-heuristic tunicate swarm algorithm is exercised. The occurrence of renewable sources makes arrangements meaningfully improved related to base thermal-gas-hydro arrangement. The action of hydrogen aqua electrolyzer-fuel cell and redox flow battery is examined using a PDN(FOPI) controller, providing noteworthy outcome in dynamic performance. The analysis is conducted under all the schemes of restructured situations. [ABSTRACT FROM AUTHOR]

This paper introduces a new cost function to measure the power system frequency disturbances. This cost function helps to identify optimal parameter gains of the secondary controllers of automatic generation control mechanism to minimize the frequency and tie line power deviations during load perturbations. To study the application benefits, merits and the drawbacks of the new cost function, an interconnected two area power system with renewable and micro energy sources is selected. The study further extended to design of classical controllers with the proposed cost function to reduce frequency deviations. To enhance the frequency profile of the hybrid power system, integral-proportional-derivative secondary controller is applied whose optimal parameter gains are adjusted using recent population search-based technique known as cooperation search algorithm (CSA). New findings are listed by carrying simulations on the hybrid power system at different generation and demand loadings in presence of classical and I-PD controllers with proposed and existing cost function-based CSA tuned values. [ABSTRACT FROM AUTHOR]