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The increasing expansion of the industrial and domestic sectors, combined with the integration of renewable energy sources, has led to an overload of the existing interconnected power systems, which in turn has led to significant bottlenecks. This confluence of factors manifests itself in severe frequency, voltage and tie-line power issues, highlighting the urgent need for intelligent control mechanisms. Automatic voltage regulation (AVR) and load frequency control (LFC) are key components that ensure the supply of high-quality energy to consumers while maintaining the nominal frequency, voltage and power deviations in the grid. These measures are essential to ensure the stability and safety of the IPS under these challenging conditions. This research addresses the control strategies for a four-area sophisticated IPS. The complexity of the system, which includes five generation units in each area, including gas, thermal reheating, hydropower and two renewable energy sources (wind and photovoltaic), requires a careful study of the control methods. In particular, the inclusion of various non-linear factors, such as the governor dead band (GDB), generation rate constraint (GRC) and boiler dynamics (BD) in each of the four areas, increases the realism of the study. In this context, a recently introduced meta-heuristic algorithm, the One-to-One-based Optimizer (OOBO), was used to determine the optimal parameters of the proportional-integral-proportional-derivative (PI-PD) controller. The tuning process involves using the integral of time multiplied by the squared error value (ITSE) as the error criterion for evaluating the fitness function. In the study, the voltage, frequency and power performance of the OOBO PI-PD controller is evaluated and compared with alternative control methods, namely Proportional-Integral-Derivative (PID), Integral-Proportional (I-P) and Integral-Proportional-Derivative (I-PD) controllers, all of which are tuned with OOBO. Under the influence of a 5% load step load perturbation (SLP), comprehensive comparisons show the superior performance of the OOBO PI-PD controller, which shows better responses in all areas. Furthermore, the reliability and effectiveness of the OOBO PI-PD method are validated by a sensitivity analysis that considers simultaneous variations of the turbine time constant and speed control within a range of ±25%. The results highlight the robustness of the OOBO-PI-PD control

This paper proposes the mixed sensitivity constraint H $\infty $ based virtual inertia controller (VIC) design for load frequency control (LFC) of islanded AC microgrid (MG). Owing to the increasing integration of renewable energy sources (RESs) in the MG, the power electronic converters required to interface RESs into grid, does not support and maintain required inertia for MG reliable operation. So, system frequency deviates from its nominal value. Virtual synchronous generator (VSG) concept has been introduced to support system inertia in the inverter-based MG. This work introduces the robust control technique based on H $\infty $ approach for design of VIC for the frequency regulation of MG. The mixed-sensitivity concept is utilized for H $\infty $ control-based VIC design. The proposed design aims to strike a balance between achieving high performance in terms of control objectives, such as frequency regulation, while ensuring robust stability in the face of uncertainties and disturbances. The effectiveness and robustness of the suggested VIC for frequency control are assessed under communication time delay, step load demand, parametric uncertainty, and various generation scenarios. Further, the efficacy of the suggested control design is verified in comparison to the existing control approach.

This paper presents a new approach to approximate the AC optimal power flow (ACOPF). By eliminating the need to solve the ACOPF every few minutes, the paper showcases how a realtime feedback controller can be utilized in lieu of ACOPF and its variants. By i) forming the grid dynamics as a system of differential-algebraic equations (DAE) that naturally encode the non-convex OPF power flow constraints, ii) utilizing DAE-Lyapunov theory, and iii) designing a feedback controller that captures realtime uncertainty while being uncertainty-unaware, the presented approach demonstrates promises of obtaining solutions that are close to the OPF ones without needing to solve the OPF. The proposed controller responds in realtime to deviations in renewables generation and loads, guaranteeing improvements in system transient stability, while always yielding approximate solutions of the ACOPF with no constraint violations. As the studied approach herein yields slightly more expensive realtime generator controls, the corresponding price of realtime control and regulation is examined. Cost comparisons with the traditional ACOPF are also showcased—all via case studies on standard power networks.

This paper proposes an optimal $\alpha $ -variable model-free adaptive barrier-function fractional-order nonlinear sliding mode control ( $\alpha (t)$ -MF-ABFFONSMC) for the load frequency control (LFC) problem of a four-area interconnected hybrid power system with boiler dynamics and physical constraints. The proposed $\alpha (t)$ -MF-ABFFONSMC is comprised of the ultra-local model (ULM)-based sliding mode disturbance observer (SMDO), proportional-differential (PD) controller, and adaptive barrier-function fractional-order nonlinear sliding mode control (ABFFONSMC). The ULM mechanism is utilized to re-formulate the complex four-area interconnected hybrid power system so as to reduce the controller’s design complexity, wherein SMDO is utilized to observe and eliminate the uncertain dynamics or lumped disturbance. Then, the SMDO based-iPD controller is designed. However, there always exists non-null estimation error under the SMDO method and the control performance cannot be ensured. Therefore, the ABFFONSMC is proposed and inserted into the SMDO-iPD controller to avoid the impact of estimation error and improve the control performance. In addition, an adaptive gain based on barrier function is formulated to approximate the upper bound of SMDO’s estimation error and thus decrease the undesired chattering on the sliding surface. Correspondingly, the $\alpha (t)$ -MF-ABFFONSMC is established. Moreover, the parameter optimizer based on the Marine Predator Algorithm (MPA) is proposed to tune the parameters of the proposed $\alpha (t)$ -MF-ABFFONSMC controller. Furthermore, using the Lyapunov theorem, the stability of $\alpha (t)$ -MF-ABFFONSMC via a closed-loop system is verified. To validate the performance of the proposed controller, the numerical simulation on a four-area interconnected hybrid power system is carried out in a Matlab/Simulink environment. The corresponding simulation results are presented to show the superiority and effectiveness of the proposed technique.

This paper introduces a robust approach, integrating a Virtual Inertia Controller (VIC) with a modified demand response controller for an islanded Multi-Microgrid (MMG) system, accommodating high levels of Renewable Energy Sources (RESs). In these MGs, the low inertia in the system has an undesirable impact on the stability of MG frequency. As a result, it leads to a weakening of the MGs overall performance. A novel fractional derivative virtual inertia is integrated into the VIC loop to address this issue. This enhancement aims to fortify the MG’s stability and robust performance, particularly when facing contingencies. Furthermore, a modified demand response controller has been incorporated into the proposed inertia control technique to mitigate the frequency fluctuations and reduce stress on the energy storage system (ESS). Fractional Order Proportional Integral Derivative (FOPID) controllers have been employed to regulate the active power output of the biodiesel generators and the Geothermal station in the MG. The hybrid sparrow search and mountain gazelle optimizer algorithm (SSAMGO) optimizes the parameters for the three-loop controller. Time-domain simulations assess the effectiveness of proposed controllers in enhancing system frequency stability. SSAMGO’s performance was comprehensively evaluated, comparing it to various optimization algorithms in diverse scenarios. The results obtained from the MMG system demonstrate that utilizing the proposed controller technique, optimized with hybrid SSAMGO parameters, yields notable improvements in settling time by 24.68%, 46.20%, 7.52%, and 61.01%, steady-state error values by 72.56%, 98.18%, 98.73%, and 6.67%, undershoot by 105.76%, 144.23%, 19.23%, and 7.69% compared to other state-of-the-art algorithms presented in the literature. Finally, the proposed control technique’s effectiveness and robustness are assessed in comparison to conventional inertia control across various system scenarios. These scenarios encompass random load demand fluctuations, real-time changes in RES, and a wide spectrum of system operations, including situations with reduced damping and inertia and high levels of load variation.

whiteThis paper presents a whitenovel Nested Identical Control (NIC) whitemethod for load frequency control of multi-area microgrid systems employing two novel variants of the Chameleon Swarm Algorithm (CSA). Load frequency control, a common strategy to counter power system instabilities from load fluctuations, poses a significant challenge. Maintaining system stability amidst substantial load shifts becomes particularly challenging in complex power networks spanning multiple areas with several generators. To address this, we first designed two novel variants of CSA called Quasi-oppositional CSA (QCSA) and Quasi-Levy-oppositional CSA (QLCSA). In QCSA, a quasi-oppositional-based learning operator is used to improve the diversification and intensification of CSA. The QLCSA incorporates the Levy flight operator into the QCSA to avoid stagnation and local minimal entrapment. We evaluated the efficiency of these variants against the original CSA and five other highly performing algorithms on the IEEE Congress on Evolutionary Computation benchmark functions. whiteSubsequently, we employed the new CSA variants to design a NIC for load frequency control of a two-area microgrid system, considering different cases of uncertainties. The NIC strategy is designed such that the controller parameters of each loop in one area are inherited by the controller of the corresponding loop in the second area, with a target to achieve $\mathrm {10 sec}$ of settling time. Simulated results on the benchmark functions prove the superiority of QLCSA and QCSA over CSA and the selected highly-performing algorithms. The dynamic responses of the NIC-controlled microgrid system whitein numerical time-domain simulations show the effectiveness of the proposed control approaches when compared with PID control tuned using MATLAB’s control system tuner.

In this paper, the stability problem of load frequency control (LFC) based power systems with two additive interval time-varying delays is studied. Firstly, considering the time delays of transmission from control center to regulator and sensor to control center, the mathematical model of two-area LFC power system based on proportional integral control is established. Secondly, under the condition that the lower bounds of interval time-varying delays are non-zero, an augmented Lyaounov-Krasovskii functional (LKF) is constructed, and a new delay-dependent stability criterion is derived. Since the extension of LKF introduces the nonlinear term of time-varying delay square, a new negative definite integral inequality transformation lemma is used to transform the nonlinear matrix inequality in the stability criterion into linear matrix inequality (LMI) equally-without introducing additional conservatism. Finally, the maximum stability margin of the LFC power systems is obtained by using MATLAB LMI-toolbox, and simulation results based on Simulink-toolbox show the effectiveness of the stability criterion.

Load frequency control of power systems is inherently a disturbance rejection issue. This paper proposes a disturbance observer-based frequency regulation approach for a shipboard microgrid system with communication delay. The proposed control method consists of the design of both disturbance observer and load frequency controller. The design of disturbance observer involves the acquisition of nominal model and the tuning of Q-filter. The nominal model is obtained via the complex curve fitting technique. Whereas the load frequency controller is designed by the frequency response model matching approach. The maximum sensitivity index is utilized as a robustness specification to tune both Q-filter and load frequency controller. The advantage of the proposed method is that inverse response process dynamics are considered into disturbance observer design to obtain better modeling accuracy and frequency regulation performance. Simulation examples including comparison with classical methods are presented to demonstrate the effectiveness of the proposed method.

The reliability of a power system depends on its ability to handle fluctuations and varying load demands, as uncontrolled frequency deviations can lead to load-shedding and blackouts. Optimally tuned controllers are essential for Load Frequency Control (LFC) applications to efficiently stabilize the power system by minimizing frequency undershoots, overshoots, and settling time. This paper proposed the application of novel Golden Eagle Optimization (GEO) algorithm for the optimal tuning of the LFC controller, which has not been previously employed in any LFC applications. Moreover, this paper presents the first-ever implementation of a hybrid energy storage system consisting of Vanadium Redox Flow Battery (VRFB) and Super Magnetic Energy Storage System (SMES) coupled with AC/HVDC transmission lines in a multi-area power system. A GEO optimized Proportional-Integrative-Derivative (GEO-PID) robust controller is designed with the Integral Time Absolute Error (ITAE) objective function to enhance the power system’s stability. The proposed controller is tested on two and four areas power systems considering the sensitivity and nonlinearity of the power systems. A robustness test is also performed to verify the stability of the system under randomly chosen loading conditions. In comparison with particle swarm optimization, dragonfly algorithm, sine cosine algorithm, ant lion optimization, and whale optimization algorithm, the GEO-PID controller significantly reduced the settling time up to 80% for different area’s frequencies. Simulation results indicate that the proposed controller outperforms other recent optimization algorithms by effectively dampening the frequency and tie-line deviations with less settling times, as well as reduced frequency undershoots and overshoots.

The load frequency control (LFC) in modern power system like microgrid has turned out to be significantly challenging due to the high penetration of renewable energy sources (RESs) and the consequent reduction of overall system inertia. The inverter-equipped RESs like solar and wind power generation units, besides the load variations can prompt sustained frequency fluctuations in microgrid and further lead to system instability, power outages, and even complete system blackout in the worst case. As a solution to the concerns of intermittent power source integration and resulting microgrid frequency instability, in this work, two robust LFC schemes using conventional linear quadratic gaussian (LQG) and modified LQG with linear quadratic integral (LQI) control schemes are proposed for secondary/battery energy storage system (BESS)-based auxiliary (virtual inertia (VI)) control of islanded/non-linear microgrid. The efficacy of the suggested control strategy is confirmed through MATLAB/SIMULINK simulations and by comparing with other different control schemes under various scenarios of distinct load and RES disturbance input profiles. The simulation results exhibited superior frequency regulation performance for the proposed control mechanisms over other types of control schemes. The proposed control schemes also ensure fast settling of frequency transients and help improve frequency stability under stochastic loads and random RES output power. In addition to the development of an effective robust controller, the vulnerability of the microgrid LFC system towards the denial of service (DoS) attack is analyzed for different control schemes. The vulnerability analysis is performed in the presence and absence of local auxiliary control loop and the remote secondary measurement communication channel is considered as the DoS attack point. The simulation results indicate that the local auxiliary control mechanism can not only help to improve frequency stability but also helps to add cyber-attack resilience to an extent when the secondary control loop is under attack.

In this paper, a decentralized load frequency control (LFC) is proposed for the frequency regulation of multi-area power systems applying optimized integral sliding mode control (OISMC) scheme. A modified particle swarm optimization (MPSO) algorithm is utilised to optimize the control variables of the proposed controller for improving the performance of frequency response. A comprehensive model of a multi-area power system is developed where each area is considered to power from conventional sources, renewable energy resources (RERs), and electrical vehicle (EV) aggregators. Different types of interconnectors have been considered to tie different power regions. This system is subjected to random generation and loading conditions, as well as model uncertainties, which cause its frequency and area control error (ACE) to deviate from their nominal values. A decentralised OISMC is implemented for the proposed model to keep the frequency variation within the nominal range. Further, a validation study investigates the performance of the proposed control technique in MATLAB simulation environment to mimic the real power system operation. This study includes different test scenarios considering the dynamic events such as variations in load demand, amount of generated power, participation factor of EV reserve, system parameters, disturbances, and time delay. The simulated results support better frequency regulation with the suggested OISMC compared to other alternative control approaches.

This article aims at designing a dynamic event-triggered $\mathcal {H}_{\infty }$ load frequency controller for multi-area power systems affected by false data-injection attacks and denial-of-service attacks. A dynamic event-triggered scheme, whose threshold parameter varies with objective system states, is employed to make rational use of limited network bandwidth resources and improve the efficiency of the data utilization. Then, taking the impacts of the aforementioned hybrid cyber attacks into consideration, an attractive system model is established. Whereafter, several sufficient conditions, which can guarantee the exponential mean-square stability with a preset $\mathcal {H} _{\infty }$ performance index of the studied system, are obtained through utilizing Lyapunov stability theory. Additionally, the desired controller is designed via handling convex optimization problems. Finally, a simulation example is displayed to explain the validity of the proposed method. [ABSTRACT FROM AUTHOR]

This brief studies the stabilization of load frequency control (LFC) system. The event-triggered intermittent control (ETIC) is designed for the multi-area LFC system. The stabilization of the LFC system is achieved under the designed ETIC. Moreover, the scheme of ETIC is improved via state-based control width (SCW) and an ETIC-SCW is proposed. Two performance indices: the rate of control and the cost of control, are defined for comparisons. Specific cases are studied to verify the effectiveness of the proposed ETIC schemes. From the numerical simulations, it is shown that the designed ETIC, especially ETIC-SCW, achieves clearly lower rate of control and cost of control than the time-based intermittent control and the event-triggered control for LFC system in the literature. [ABSTRACT FROM AUTHOR]

Grid-connected large-scale power converter-based intermittent renewable energy sources (RES) reduce system inertia, increase frequency fluctuation, and increase the rate of change of frequency (RoCoF). An energy storage system (ESS) is an indispensable component of a smart grid, and is used to overcome low-inertia problems. However, the capital and maintenance costs of ESS are high and high RoCoF events are less frequent in power systems. Therefore, the introduction of a virtual energy storage system (VESS) to provide the function of a conventional ESS for power system ancillary services is an innovative and cost-effective method. This study investigated a VESS using photovoltaic (PV) generators and inverter air conditioners (IACs) to provide virtual inertia and frequency regulation for a low-inertia microgrid. A model predictive control (MPC)-based VESS regulates indoor temperature, microgrid frequency, and RoCoF. The impact of parameter variation, that is, the microgrid frequency weight, indoor temperature weight, virtual inertia gain, and number of IACs, was studied and selected by considering the ability of the parameters to provide virtual inertia and frequency regulation. Finally, the efficiency and robustness of the proposed MPC-based VESS technique are compared with those of a conventional VESS. Simulation results revealed that the proposed MPC-based VESS can improve the virtual inertia, reduce the frequency deviation, and reduce the RoCoF of the studied microgrid. In addition, the proposed method is robust to variations in the system parameters.

False data injection attacks (FDIAs) against the load frequency control (LFC) system can lead to unstable operation of power systems. In this paper, the problems of detecting and estimating the FDIAs for the LFC system in the presence of external disturbances are investigated. First, the LFC system model with FDIAs against frequency and tie-line power measurements is established. Then, a design procedure for the unknown input observer (UIO) is presented and the residual signal is generated to detect the FDIAs. The UIO is designed to decouple the effect of the unknown external disturbance on the residual signal. After that, an attack estimation method based on a robust adaptive observer (RAO) is proposed to estimate the state and the FDIAs simultaneously. In order to improve the performance of attack estimation, the $H_{\infty}$ technique is employed to minimize the effect of external disturbance on estimation errors, and the uniform boundedness of the state and attack estimation errors is proven using Lyapunov stability theory. Finally, a two-area interconnected power system is simulated to demonstrate the effectiveness of the proposed attack detection and estimation algorithms.

One of the challenging issues in a hybrid power system (HPS) is to provide a stable power supply with minimum frequency deviation, which can be accomplished by ensuring a coordinated operation among intermittent type distributed generators (DGs). In this study, the coordinated operation of a tidal turbine generator (TTG) (an emerging and less explored DG), diesel engine generator (DEG) and plug-in hybrid electric vehicles (PHEVs) based an autonomous HPS (a less explored HPS of this kind) for load frequency control (LFC) is investigated. The coordinated operation of different control loops such as the blade pitch control loop of TTG, supplementary control loop of DEG, and power control loop of PHEVs are realized through the maiden application of the proposed quasi-oppositional harmony search algorithm (QOHSA) based model predictive control (MPC) strategy. To establish the superiority of the proposed QOHSA tuned MPC method in mitigating frequency deviation following disturbance, its performance is compared with that of the coordinated performance obtained using QOHSA tuned conventional controllers and other existing optimization algorithms. The results conclude that the suggested method can significantly reduce frequency fluctuation under different load disturbances. The proposed method can handle different variances of the disturbance signals or noise entering the system as well as the model mismatch.

The interconnection of renewable energy systems, which are complex nonlinear systems, often results in power fluctuations in the interconnection line and high system frequency due to insufficient damping in extreme and dynamic loading situations. To solve this problem, load frequency control ensures nominal operating frequency and orderly fluctuation of grid interconnection power by delivering high-quality electric power to energy consumers through efficient and intelligent control systems. To introduce the frequency control of power systems, this paper presents a novel control technique of Fractional Order Integral-Tilt Derivative with Filter (FOI-TDN) controller optimized by the current soft computing technique of hybrid Sine-Cosine algorithm with Fitness Dependent Optimizer (hSC-FDO). For more realistic analysis, practical constraints with nonlinear features, such as controller dead band, communication time delay, boiler dynamics, and generation rate constraint are embedded in the given system model. The proposed approach outperforms some recently developed heuristic approaches such as fitness dependent optimizer, firefly algorithm, and particle swarm optimization for the interconnected power system of two areas with multiple generating units in terms of minimum undershoot, overshoot, and settling time. To improve the system performance, capacitive energy storage devices are used in each area and thyristor control phase shifter is used in the interconnection line of the power system. The potential of the hSC-FDO-based FOI-TDN is demonstrated by comparing it with conventional FOTID/FOPID/PID controllers for two areas with multiple power generators IPS. Finally, a robustness analysis is performed to determine the robustness of the presented control system by varying the system loads and system parameters.

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.

A frequency regulation of interconnected power system comprising thermal reheat system and photovoltaic panel systems is disclosed in this research article with Proportional Integral Derivative (PID) controller optimized using Black Widow Optimization Algorithm (BWOA) based on distinct feature of cannibalism of black widow spiders. With rising demand for perfectly optimized power systems, a best optimal solution for controller gains besides merit of faster convergence and avoidance of trapping in local minimal are becoming key requirements for system engineers. In tune with these requirements, we have developed an optimized solution based on BWOA in the power system realm to minimize the fitness function which is Integral Time Absolute Error composing of frequency and tie-line power variations. Superiority of BWOA optimized controller in power system realm is established by comparing and analyzing its simulation results with some other known tuned controllers which uses different optimization algorithms namely firefly algorithm, genetic algorithm, whale optimization algorithm and modified whale optimization algorithm. Results in this paper lays out that BWOA optimized PID controller outperforms other reported controllers in terms of fitness function value, settling time, undershoot/overshoot of the incremental variations in frequency and tie-line power of the interconnected power system. Further, the potency and robustness of the proposed tuned controller are also proven by considering large variation in load demand, real-time constraints namely generation rate constraint and governor dead band in the system. Additionally, the sensitivity analysis of the optimized controlled system is performed to conclude that the proposed BWOA optimized PID controller optimized controller is insensitive to changes in system parameters and eliminates the necessity of resetting of controller parameters. The performance of the proposed control technique is also tested on two-area non-reheat thermal power system with non-linear constraints namely time delay, generation rate constraint and governor deadband.

This paper presents an accomplishment of Load Frequency Control (LFC) under deregulated environment using Imperialistic Competition Algorithm (ICA) based Dual Mode Controller (DMC). The Capacitive Energy Storage (CES) and Static Synchronous Series Compensator (SSSC) have been successfully used to improve the performance of the system dynamics. The optimization techniques are beneficial to the design of the Dual Mode Controller and overcome the drawbacks identified by the conventional controllers. The traditional Load Frequency Control system is adapted to interpret bilateral contracts on the system. The performance index is calculated by using the integral of the square of error technique to optimize the controllers of the Load Frequency Control system. The simulation analysis reveals that due to the presence of the Imperialistic Competition Algorithm-based Dual-Mode Controller, the performance of the system improves in terms of peak time, settling time, and overshoot, and also performance index is lesser than the system without the Imperialistic Competition Algorithm based Dual Mode Controller following a load disturbance in either of the areas.

Power systems have been evolving dynamically due to the integration of renewable energy sources, making it more challenging for power grids to control the frequency and tie-line power variations. In this context, this paper proposes an efficient automatic load frequency control of hybrid power system based on deep reinforcement learning. By incorporating intermittent renewable energy sources, variable loads and electric vehicles, the complexity of the interconnected power system is escalated for a more realistic approach. The proposed method tunes the proportional-integral-derivative (PID) controller parameters using an improved twin delayed deep deterministic policy gradient (TD3) based reinforcement learning agent, where a non-negative fully connected layer is added with absolute function to avoid negative gain values. Multi deep reinforcement learning agents are trained to obtain the optimal controller gains for the given two-area interconnected system, and each agent uses the local area control error information to minimize the deviations in frequency and tie-line power. The integral absolute error of area control error is used as a reward function to derive the controller gains. The proposed approach is tested under random load-generation disturbances along with nonlinear generation behaviors. The simulation results demonstrate the superiority of the proposed approach compared to other techniques presented in the literature and show that it can effectively cope with nonlinearities caused by load-generation variations.

Unpredictable high renewable shares in a standalone microgrid (MG) system with stochastic load demands introduces an unavoidable mismatch among loads and sources. This mismatch directly impacts the system frequency, which can be mitigated via applying a suitable load frequency control (LFC) scheme. This brief proposes a maiden attempt of marine predator algorithm (MPA) assisted one plus proportional derivative with filter-fractional order proportional-integral ((1+PDF)-FOPI) controller to obtain the proper power flow management among loads and sources. The investigated MG system consists of a photovoltaic (PV) system, a wind turbine (WT) generator (WTG), and a diesel engine generator (DG) as the distributed energy sources, and an ultracapacitor (UC) and a flywheel are chosen as the energy storage elements (ESEs). Various system nonlinearities, such as governor dead-band (GDB) and generation rate constraint (GRC) are also considered to reflect the practical scenario. Five state-of-the-art optimization techniques and three traditional controllers, PID, FOPID, and PI-PD, are vividly compared to assess the proposed scheme’s performance. The parametric uncertainties are considered obtaining the robust performance of the proposed control scheme. An eigenvalues-based stability evaluation of the considered plant employing the proposed LFC scheme is also included in this work. In the worst situation, the maximum frequency deviation is obtained as −0.016 Hz, which is entirely satisfactory and under the range of the IEEE standard. Finally, a modified New England IEEE-39 test bus system is chosen to perform the real power system validation via MATLAB/Simulink.

Egypt aims to diversify electricity generation sources and targets 42% of its power capacity through different renewable energy sources (RESs) by 2035. Such an increased share of RESs will make grid operation and control more tedious and may have a negative impact on power system frequency stability. Therefore, this paper is the first study that studies the automatic generation control of future multi-source power systems (e.g., Egyptian Power System (EPS) in a future 2035 scenario) considering high RESs penetrations and electric vehicles (EVs). The RESs in this future scenario include photovoltaic plants, wind generation plants, concentrated solar power plants, and hydropower plants. The EPS also contains other traditional power plants based on fossil fuels (i.e., coal, oil, and natural gas) and nuclear power plants. Moreover, this study investigates the effect of participation of EVs in enhancing the frequency stability of the future-scenario EPS. Furthermore, this study proposes a fractional-order proportional integral derivative (FOPID) controller for load frequency control (LFC), and its parameters are tuned using RUNge Kutta optimizer (RUN), which is a new optimization algorithm. To assess the FOPID controller performance, it was compared with a proportional-integral-derivative controller, proportional-integral controller, and integral controller. The results showed the positive effect of EVs’ participation in frequency regulation of the future-scenario EPS, the effectiveness of RUN optimizer in LFC application, and the superior performance of the FOPID controller over its peers of controllers used in the literature in improving frequency stability through reducing frequency deviations caused by different disturbances and under various power system conditions.

The load frequency control (LFC) problem in interconnected multiarea power systems is facing more challenges due to increasing uncertainties caused by the penetration of intermittent renewable energy resources, random changes in load patterns, uncertainties in system parameters and unmodeled system dynamics, leading to a compromised reliability of power systems and increasing the risk of power outages. In responding to this problem, this paper proposes a decentralized disturbance observer-based sliding mode LFC scheme for multiarea interlinked power systems with external disturbances. First, a reduced power system order is constructed by lumping disturbances from tie-line power deviations, load variations and the output power from renewable energy resources. The disturbance observer is then designed to estimate the lumped disturbance, which is further utilized to construct a novel integral-based sliding surface. The necessary and sufficient conditions to determine the tuning parameters of the sliding surface are then formulated in terms of linear matrix inequalities (LMIs), thus guaranteeing that the resultant sliding mode dynamics meet the ${H_\infty }$ performance requirements. The sliding mode controller is then synthesized to drive the system trajectories onto the predesigned sliding surface in finite time in the presence of a lumped disturbance. From a practical perspective, the merit of the proposed control method is to minimize the impact of the lumped disturbance on the system frequency, which has not been considered to date in sliding mode LFC design. Numerical simulations are illustrated to validate the effectiveness of the proposed LFC strategy and verify its advantages over other approaches.

This paper studies the decentralized $H_{\infty }$ secure load frequency control issue and circuital realization of multi-area networked power systems subject to random transmission delays and deception attacks. To make full use of the stochastic feature of the network-induced transmission delay, its distribution described by the probability density function is utilized. Due to this feature, the normal control signal and the injected deceptive attack signal transmitted over the network can be formed as two distributed delay terms. Then, the $i$ th load frequency control area is established as a new distributed delay system, in which the delay probability density is treated as the distributed kernel. By utilizing an integral inequality dependent on the kernel, new sufficient controller design conditions are derived to guarantee the system stability with given $H_{\infty }$ performance. Moreover, a physical execution approach is addressed to transfer the load frequency control systems into electrical analogy circuits. The effectiveness of the proposed approach is illustrated via the professional simcape toolbox in Simulink/MATLAB for circuit simulations. [ABSTRACT FROM AUTHOR]

Most of the existing control schemes for power systems ensure stability only in the presence of constant loads and renewable energy sources. Motivated then by the inadequacy of the existing control strategies for power systems affected by time-varying loads and renewable energy sources, this article proposes two control schemes based on the well-known output regulation control methodology. The first one is designed based on the classical output regulation theory and addresses the so-called load frequency control (LFC) problem in the presence of time-varying uncontrolled power injections. Then, in order to also minimize the generation costs, we use an approximate output regulation method that solves numerically only the partial differential equation of the regulator equation and propose a controller based on this solution, minimizing an appropriate penalty function. An extensive case study shows the excellent performance of the proposed control schemes in realistic and critical scenarios. [ABSTRACT FROM AUTHOR]

This paper proposes a co-estimation and resilient compensation control framework for multi-area load frequency control (LFC) systems in the presence of false data injection (FDI) attacks and denial-of-service (DoS) attacks. To relax the requirement on existing DoS attack models, which are usually considered under the assumption that both its frequency and duration are constrained, a new DoS model within a known bounds of the sleeping and active period of the attack signal is proposed. The injected FDI attack signal is generated by an external dynamic systems. A switching impulsive observer and a switching state observer are constructed to estimate the unknown FDI attack signal and the system state, respectively. Moreover, a new exponential stability criteria of the output feedback multi-area LFC systems under DoS and FDI attacks is first derived by using attack parameter dependent time varying Lyapunov function method. Then, a co-design method of a resilient attack compensation controller, state estimator, and attack estimator is developed. Finally, case studies are considered for verification of the effectiveness of the proposed theoretical results. [ABSTRACT FROM AUTHOR]

This brief proposes a novel chaotic fractional-order beetle swarm optimization (CFBSO) algorithm that combines chaos concepts and a fractional derivative structure with the beetle swarm algorithm. The proposed CFBSO was compared with other advanced meta-heuristic algorithms on 23 benchmark functions with single-peak, multi-peak, and fixed-dimensional multi-peak optimization problems, and the effectiveness and superiority of the proposed algorithm were verified. The proposed CFBSO was then used to optimize the parameters of the active disturbance rejection controller (ADRC). The CFBSO-based ADRC was further applied for the load frequency control (LFC) system of a four-area interconnected power system consisting of a hydraulic turbine with a non-minimum phase (NMP) and three reheat turbines. The results showed that the proposed method had a smaller undershoot and shorter settling time than those obtained by a linear ADRC and a proportional–integral–derivative controller. Thus, it can meet the high-performance requirements of LFC. [ABSTRACT FROM AUTHOR]

Several issues have been risen due to the recent vast installations of renewable energy sources (RESs) instead of fossil fuel sources in addition to the replacement of electric vehicles (EVs) for fuel-powered vehicles. Mitigating frequency deviations and tie-line power fluctuations has become driving challenge for the control design of interconnected power systems. RESs represent continuously varying power generators due to their nature and dependency on the environmental conditions. In this context, this article presents a new modified hybrid fractional order controller for load frequency and EVs control in interconnected power systems. The new controller combines the benefits of two widely employed fractional order controllers, including the FOPID and TID controllers. In addition, a new practical application of recent artificial ecosystem optimization (AEO) method has been proposed in this article for determining simultaneously the optimum controller parameters. The proposed controller and optimization method are validated on two areas interconnected power system with different types of RESs and with considering the natural characteristics of sources, EVs and load variations. Obtained simulation results verify the superior performance of the proposed controller and optimization method for achieving high mitigation of frequency fluctuations and tie-line power deviations, increased robustness, enhanced system stability over a wide range of parameters uncertainty and fast response during transients.

This paper presents a fully optimized fuzzy proportional-integral-derivative with filter (FPIDF) load frequency controller (LFC) for enhancing the performance of a hybrid microgrid system. The Marine Predator Algorithm (MPA), a recent optimization algorithm, is used to optimize the gains as well as the input scaling factors and membership functions of the proposed fuzzy PIDF controller. The controller performance is tested on a two-area hybrid microgrid system containing various renewable energy sources and energy storage devices. The effectiveness of the MPA based FPIDF controller is compared with conventional PIDF and FPIDF controllers based on other heuristic techniques presented in literatures. Moreover, different scenarios are implemented in this study to verify the robustness and sensitivity of the proposed controller to different step load perturbations, system parameters variations, wind speed fluctuation and solar irradiance variation. The dynamic response of the system is compared using different controllers in terms of settling time, maximum overshoot and undershoot values. The results are presented in the form of time domain simulations conducted via MATLAB/SIMULINK.

In multi-interconnected power system, keeping the changes in frequencies and tie-line powers at their specified values is vital process especially during operation under load disturbance. This target can be achieved by installing load frequency control (LFC), the main object of LFC is damping the deviations of frequencies and tie-line powers to zero. This paper proposes a new approach incorporated recent optimizer of movable damped wave algorithm (MDVA) to identify the unknown parameters of LFC represented by fractional-order proportional integral derivative (FOPID). FOPID controller is selected as it has better and robust performance, the controller is installed in multi-interconnected system with multi-sources considering renewable energy-based plants. Minimizing the integral time absolute error (ITAE) of the change in frequencies and tie-line powers is the main target. Two power systems are considered in this work, the first one comprises photovoltaic (PV) and thermal generating units while the second system includes four plants of PV, wind turbine (WT), and two thermal based plants. Moreover, the generation rate constraints and governor dead-band of thermal plant are considered. Different load disturbances in both studied systems are investigated and the obtained results via the proposed DMVA are compared to coronavirus herd immunity optimizer (CHIO), antlion optimizer (ALO), sooty tern optimization algorithm (STOA), manta ray foraging optimizer (MRFO), and sin-cos algorithm (SCA). Regarding the two-interconnected system, the proposed DMVA succeeded in achieving the best ITAE of 6.3911 during 10% disturbance applied on PV plant. Regarding to the multi-interconnected system, the best (minimum) fitness function is 0.015029 achieved via the proposed DMVA at 1% load disturbance on the first area. The results confirmed the robustness of the proposed algorithm in solving the LFC parameter estimation.

The congest in an open communication system may cause the failure of the load frequency control (LFC) scheme in the power system. Furthermore, the increase of wind power penetration degrades the stability of the LFC scheme. To address these two issues, this paper analysis the stability of the LFC schemes with both the Bernoulli model and the Gilbert-Elliott model packet losses based on the Markov jump system. The effect of wind penetration on the stability of the LFC schemes also discussed as inertia reduction. Moreover, the combined action of packet losses and inertia reduction is discussed both in theoretical. Then, a resilient event-triggered LFC scheme is proposed to deal with packet losses and wind penetration. Simulation results with different scale of power systems are given to validate the proposed LFC scheme.

The penetration rate of renewable energy source generation in power systems continues to increase in recent years. However, due to the intermittent nature of renewable energy source (RES) generation, more frequency regulation resources with faster response time and larger capacities are required in the system in order to maintain the system frequency stability. Electric vehicles (EVs) can provide frequency regulation capacities to the system with their batteries when idle, but first they need to be aggregated to enter the ancillary service market for frequency regulation. In this paper, a model predictive control scheme is proposed for the EV aggregators. With the proposed control scheme, an EV aggregator can receive more payment through participation in system frequency regulation while not violating the EV users’ convenience. A prediction method based on a seasonal-autoregressive-integral-moving-average (SARIMA) model on the regulation capacity price is also implemented to further boost the EV aggregator’s payment. Simulation based on actual frequency regulation market price is conducted to examine the performance of the proposed control scheme. Compared with the simple prediction method on the regulation capacity price used in the existing literature, the proposed MPC scheme with SARIMA prediction increases the payments from the ancillary service market by 4.3% for the EV aggregator.

Load frequency control (LFC) is necessary to guarantee the safe operation of power systems. Aiming at the frequency and power stability problems caused by load disturbances in interconnected power systems, active disturbance rejection control (ADRC) was designed. There are eight parameters that need to be adjusted for an ADRC, which are challenging to adjust manually, thus limiting the development of this approach in industrial applications. Regardless of the theory or application, there is still no unified and efficient parameter optimization method. The traditional particle swarm optimization (PSO) algorithm suffers from premature convergence and a high computational cost. Therefore, in this paper, we utilize an improved PSO algorithm, a reinforcement-learning-based memetic particle swarm optimization (RLMPSO), for the parameter tuning of ADRC to obtain better control performance for the controlled system. Finally, to highlight the advantages of the proposed RLMPSO-ADRC method and to prove its superiority, the results were compared with other control algorithms in both a traditional non-reheat two-area thermal power system and a non-linear power system with a governor dead band (GDB) and a generation rate constraint (GRC). Moreover, the robustness of the proposed method was tested by simulations with parameter perturbations and different working conditions. The simulation results showed that the proposed method can meet the demand for the frequency deviation to stabilize to 0 in LFC with higher performance, and it is worthy of popularization and application.

In this paper, the load frequency control problem of multi-area interconnected power systems containing PV and energy storage system is considered. First, based on the traditional load frequency control model, the model of photovoltaic and energy storage system is established. Then, when the upper and lower bounds of disturbances are known, a fast terminal sliding mode controller is designed, which compensates the influence of disturbances effectively. Further, by introducing an adaptive law to estimate the bound of the unknown disturbances, an adaptive fast terminal sliding mode controller is designed, which reduces the dependence on the disturbance boundary. By designing the controller, the stability of the system is analyzed. Finally, simulations are performed with two area power systems to verify the effectiveness of the designed controller. The simulation results show that the adaptive fast terminal sliding mode controller can overcome the effects of disturbances. In addition, when the energy storage system and the controller participate in frequency regulation, the load frequency deviation fluctuation is further reduced, which enhances the performance of system.

This paper proposes a cascaded Proportional Integral-Fractional Order Proportional-Integral-Derivative (PI-FOPID) controller to improve the frequency response of a hybrid microgrid system. The optimum gains of the proposed controller are fine-tuned using Gorilla Troops Optimizer (GTO) which is a recent metaheuristic optimization algorithm. The case study is a two-area microgrid system that contains diesel generators, various renewable energy sources such as photovoltaic and wind generation systems, as well as different energy storage devices. Moreover, real wind speed and solar irradiance measurements have been collected for proper system modeling. The performance of the proposed cascaded PI-FOPID controller is compared to the single structure fractional order PID (FOPID) controller based on GTO and numerous other optimization techniques presented in the previous literature such as Genetic Algorithm and Particle Swarm Optimization. The robustness of the proposed cascaded PI-FOPID controller is investigated under different scenarios such as different step load perturbations, random load disturbances, and renewable energy sources variation. The simulation results are carried out using MATLAB/Simulink. The results show that the proposed controller provides an improvement in the maximum overshoot/undershoot and settling time of 99.8% and 75.9%, respectively, compared to other competing techniques.

The low system inertia and the high sensitivity to load and generation fluctuations represent the main challenges for future ambitious plans of modern power systems accompanied by high penetrations levels of the renewable energy sources (RESs). Therefore, this article presents a new approach for solving the load frequency control (LFC) in addition to the virtual inertia control (VIC) in interconnected RESs penetrated power systems using cooperative tilt-based controllers and a hybrid modified particle swarm optimization with genetic algorithm (MPSOGA). The VIC system is adopted using superconducting magnetic energy storage (SMES) to provide sufficient inertial energy for system stability. Two tilt-based controllers are employed in each area using the tilt-integral-derivative (TID) controller for the SMES and TID with filter (TIDF) for the LFC function. The cooperative optimum design of the TID/TIDF controllers leads to the enhancement of frequency stability in studied two-area power systems. The formulated optimization process aims to minimize the frequency nadir settling time during abrupt changes of RESs and/or load changes, considering the cooperative control of LFC and VIC. The proposed approach has been applied to a case study consisting of two-area power systems, connected via hybrid high voltage DC/AC (hybrid HVAC/HVDC) tie-line, integrated with distributed conventional generations, photovoltaic (PV), and wind generation systems. Performance analysis has been conducted to demonstrate the effectiveness of the proposed method is compared to the genetic algorithm (GA) and particle-swarm optimization (PSO) using high fluctuations of renewable generations under extreme changes in loading conditions and physical parameters variation. The obtained results show the superiority of MPSOGA approach on the other competitive optimization techniques.

In this study, a bandwidth allocation-based distributed event-triggering load frequency control (LFC) has been developed for smart grids to deal with hybrid cyber-attacks, for example, denial-of-service (DoS) attacks and false data injection (FDI) attacks. Firstly, to prevent hybrid cyber-attacks from causing open-loop unstable operation of the LFC systems, we propose a distributed event-triggering communication (ETC) strategy. To attain the maximum usage of bandwidth, a dynamic bandwidth allocation mechanism is integrated with the ETC approach on the basis of resource availability and error between the current state and equilibrium state. This bandwidth reservation and allocation approach aim at detecting attacks and assigning bandwidth to the different channels of distribution networks. Then, by virtue of the Lyapunov approach, the exponential stability criteria are established. Further, the exclusion of Zeno behavior of the designed systems is proved during the control process. Finally, comprehensive case studies show that the proposed method can improve the utilization rate of the network resource. [ABSTRACT FROM AUTHOR]

The goal of this paper is to introduce an adaptive load frequency control (LFC) technique for power systems. The concept of the proposed adaptive technique is built on the on-line tuning of the gain of an integral controller using Electro-Search optimization (ESO) supported by a modification called the `balloon effect'. The main target is to regulate the frequency of isolated and interconnected power systems. The balloon effect is designed to obtain the input/output signals of the power system plant at any moment; then, these signals are utilized to calculate the value of the open loop transfer function of the plant at the same time. This will emphatically affect the specified objective function of the ESO approach and increase its sensitivity to and effect on system difficulties such as system step load changes, parameter uncertainties and the effect of the penetrations resulting from renewable sources. Delayed time resulting from the communication process between the area and control center is considered in the dynamic model of interconnected MGs, and nonlinearities, such as governor dead bands (GDBs) and generation rate constraints (GRCs), are included in the simulation model. The simulation results demonstrate the effectiveness and success of the proposed controller tuned by the ESO approach with a balloon effect and provide better performance of frequency regulation than a fixed controller, as well as other methods such as standard Jaya and ESO methods.

This paper suggests an adaptive control approach using water cycle algorithm optimization method (WCA) modified by a new technique called “Balloon Effect”. The main concept of Balloon Effect is to increase the ability of the WCA to tune individually the gains of the controller online. The proposed technique has been used to tune the gains of PID controller and applied to control both of the load frequency control in micro-grid power system (which minimize the fundamental frequency variations) and the position of armature-controlled DC motor. Digital simulations have been carried out to prove the efficiency of the proposed adaptive controller-based WCA with Balloon Effect. The obtained results showed that the proposed adaptive controller maintains robust performance, and effectively reduces the impact of identified disturbances and uncertainties.

This paper proposed an improved structure of Proportional Integral Derivative (PID) controller called as Integral Proportional Derivative (I-PD), applied for Automatic Generation Control (AGC) of Multi-Source Interconnected Power System (IPS). The parameters of the proposed controller are optimized with a newly developed, powerful, nature-inspired meta-heuristic technique known as Fitness Dependent Optimizer (FDO). To show the efficacy of the proposed controller and the technique used, they have been tested on three different system models. Initially, a two-equal area of diverse source generation including reheat-thermal, gas, and hydro power system is considered. In the second scenario, the same power system model is used with addition of two non-linearities; Generation Rate Constraint (GRC) and Governor Dead Band (GDB). Lastly, multiple non-linearities including Governor Dead Band (GDB), Time Delay (TD), Generation Rate Constraint (GRC), and Boiler Dynamics (BD) have been considered to make the initial system more realistic and practical. The outcome from the proposed techniques is also compared with some recently meta-heuristic algorithms such as Teaching Learning Based Optimization (TLBO), Particle Swarm Optimization (PSO) and Firefly Algorithm (FA). From the results, it has been perceived that the proposed technique shows superior performance in respect of Overshoot (Osh), Undershoot (Ush) and Settling Time (Ts) of the system frequency.

This paper presents a recent metaheuristic optimization approach of multi-verse optimizer (MVO) to design load frequency control (LFC) based model predictive control (MPC) incorporated in large multi-interconnected system. The constructed system comprises six plants with renewable energy sources (RESs). MVO is employed to determine the optimal parameters of MPC-LFC to achieve the desired output of the interconnected system in case of load disturbances. The presented system comprises reheat thermal, hydro, photovoltaic (PV) model with maximum power point tracker (MPPT), wind turbine (WT), diesel generation (DG), and superconducting magnetic energy storage (SMES). The integral time absolute error (ITAE) of the frequencies and tie-line powers deviations is proposed as objective function. The effects of governor dead zone and generation rate constraint (GRC) of thermal plants are considered. The performance of the proposed MPC optimized via MVO is compared with the other designed via intelligent water drops (IWD) and genetic algorithm (GA). Additionally, the robustness of the proposed MPC-LFC based MVO with variation of the system parameters is presented. The obtained results confirmed the superiority and reliability of the proposed controller compared to the others.

This paper investigates the stability of time-delayed load frequency control (LFC) systems with electric vehicles (EV) aggregators based on Lyapunov theory and linear matrix inequality (LMI) technology. A novel Lyapunov-Krasovskii functional (LKF) is constructed. Then, based on Bessel-Legendre (B-L) inequality and model reconstruction technique, two stability criteria are derived, respectively. Some simulations are carried out to verify the effectiveness of the proposed methods. And the interaction of time-delays between areas and the effect of gains of EVs on delay margins are discussed.

The robust stability and stabilization of adaptive event-triggered load frequency control (LFC) with sliding mode control (SMC) for multi-area power systems under a networked environment are investigated in this paper. The adaptive event-triggered scheme is proposed to maximize network bandwidth utilization, and it can be adaptively modified according to circumstances. Furthermore, to provide stronger robustness performance which is against the frequency deviation induced by power unbalance or transmission time delays, the SMC is developed. Then, the LFC scheme for multi-area power systems under the networked environment is modeled as a Markov jump linear system model, to describe the uncertainty parameters and external disturbances better in this system. Additionally, by employing Wirtinger-based inequality and Lyapunov theory, the robust stability and stabilization criteria with less conservatism are derived. Finally, simulations are performed to demonstrate the efficacy and superiority of the developed approach.

Load frequency control (LFC) is one of the essential process in interconnected power systems. To provide high quality, reliable and stable electrical power, designed controller should perform satisfactorily, i.e. suppress area frequency and tie-line power deviations. Within this scope, in this study, a high order differential feedback controller (HODFC) and a developed fractional high order differential feedback controller (FHODFC) are proposed for LFC problem in multi-area power systems for the first time. The gains of the HODFC and FHODFC are optimally tuned by particle swarm optimization (PSO) algorithm aiming to minimize integral of time weighted absolute error (ITAE) performance index. The superiority of the developed FHODFC are verified by comparing reported controller structures in the recent state-of-the-art literature and HODFC for two identical non-reheat thermal power system and two-area multi-source power system consisting of gas, thermal and hydro generation units with/without consideration of HVDC link. To test the robustness of the designed controllers, varying system parameters and loading conditions are investigated. The governor dead band (GDB) and generation rate constraint (GRC) limitations are also considered for the system under study to examine non-linearity handling success of the proposed controllers. Performance results indicate that the developed FHODFC provides better dynamic response and robustness than other published techniques under nonlinearities, random load pattern, and variations in system parameters and loading conditions.

In this paper, a cyber-physical digital signature creation method and a resilient load frequency control approach are proposed to improve the security of the existing SCADA protocols used in the load frequency control of interconnected power systems. In specific, the dynamic model of the power system is used to predict the future states of the system and the statistics of a number of future states are calculated and used as the dynamic hash to determine the integrity of the data communicated between remote telemetry units and the power system SCADA control center. The proposed algorithm has inherently the cyber-physical characteristics and enhanced robustness to collisions without adding a larger overhead to the message frame of the packets in current SCADA protocols. To compensate for the possible performance degradation and instability of the power system caused by corrupted data, a model-based resilient load frequency controller is designed for the smart grid. The maximum interval between two consecutive feedback updates in the model-based control scheme is obtained for securing the stability of the power system. The investigation of three types of integrity attacks on a two-area power system shows that the proposed model-based digital signature attack detection scheme is able to detect even a little inconsistency of the communicated data. By using the designed resilient load frequency controller, the stability and dynamic performance of the smart grid are guaranteed.

Multi-area power systems inhere complicated nonlinear response, which results in degraded performance due to the insufficient damping. The main causes of the damping problems are the stochastic behavior of the renewable energy sources, loading conditions, and the variations of system parameters. The load frequency control (LFC) represents an essential element for controlling multi-area power systems. Therefore, the proper design of the controllers is mandatory for preserving reliable, stable and high-quality electrical power. The controller has to suppress the deviations of the area frequency in addition to the tie-line power. Therefore, this paper proposes a new frequency regulation method based on employing the hybrid fractional order controller for the LFC side in coordination with the fractional order proportional integral derivative (FOPID) controller for the superconducting energy storage system (SMES) side. The hybrid controller is designed based on combining the FOPID and the tilt integral derivative (TID) controllers. In addition, the controller parameters are optimized through a new application of the manta ray foraging optimization algorithm (MRFO) for determining the optimum parameters of the LFC system and the SMES controllers. The optimally-designed controllers have operated cooperatively and hence the deviations of the area frequency and tie-line power are efficiently suppressed. The robustness of the proposed controllers is investigated against the variation of the power system parameters in addition to the location and/or magnitude of random/step load disturbances.

Power systems are moving towards hybrid AC/DC grids with the integration of HVDC links, renewable resources and energy storage modules. The load frequency control (LFC) of tomorrow has to consider the complex interactions between these components. Meanwhile, more attention should be paid to cyber security concerns as the LFC loop highly depends on data communications which may be exposed to cyber attacks. In this regard, this article aims to analyze the false data injection (FDI) attacks on the AC/DC interconnected LFC system with inertia emulation and develop advanced diagnosis tools to reveal their occurrence. We build an optimization-based framework for the purpose of vulnerability analysis. Considering the attack impact on frequency stability, it is shown that the multi-area LFC system with parallel AC/DC links and emulated inertia by storage devices is more vulnerable to FDI attacks, compared to the one without inertia emulation and the normal AC system. We then propose a detection approach to detect and isolate each FDI intrusion with a sufficient fast response, and even recover the attack value. In addition to theoretical results, the effectiveness of the proposed method is validated through simulations on the two-area AC/DC interconnected LFC system with inertia emulation capabilities.

This paper presents a Fractional Order Integral-Proportional Derivative (FOI-PD) controller for Automatic Generation Control (AGC) of two-area Interconnected Power System (IPS) with six multiple generations units in a restructured environment. Further, the two-area IPS is composed of multiple non-linearities with Time Delay (TD), Boiler Dynamic (BD), Governor Dead Zone/Band (GDZ/GDB) and Generation Rate Constraint (GRC). The gains of the proposed controller are optimized by a most recent powerful meta-heuristic algorithm known as Improved-Fitness Dependent Optimizer (I-FDO). The efficiency of the proposed approach is compared with other techniques such as Firefly Algorithm (FA), Fitness Dependent Optimizer (FDO) and Teaching Learning Based Optimization (TLBO) algorithms. Further, to enhance the performance of the system, Redox Flow Batteries (RFB) is incorporated in each area and Thyristor Controlled Series Compensator (TCSC) in the tie-line of the power system. Results reveal that our proposed approach performs superior in terms of less Overshoot (Os), Settling time (Ts) and Undershoot (Us). Robustness of the proposed controller is verified by changing system parameters within a range of ± (25) %.

The significant penetration of renewable energy resources and ever-rising load demand has gradually aggravated the safe operation of the interconnected power system. A generalized extended state observer and fractional-order theory based integral sliding mode control strategy is introduced in the interconnected power system to minimize the frequency deviation by controlling the governor of the generation unit. The generalized extended state observer is designed to estimate the disturbance from system uncertainty, load demand, and wind power fluctuation to improve the robustness of the interconnected power system. The fractional-order integral sliding mode control relieves the chattering of frequency deviation and tie-line power deviation. Besides, the superconducting magnetic energy storage and the wind model, as the ancillary frequency regulation devices, are implemented in the interconnected power system. The novel sliding mode controller based on generalized extended state observer is designed for the interconnected power system with the addition of the superconducting magnetic energy storage and the wind model. Stability proof and simulation comparison with the integer-order sliding mode control and other three control schemes are carried out as well. [ABSTRACT FROM AUTHOR]