Search

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]

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]

One of the crucial quantities in the power system problem is frequency, which should never exceed its nominal value. In normal conditions of load changes, the frequency is adjusted by automatic generation control. Still, in the face of special conditions, such as logging of the downstream network from the main network or a significant amount of generated power leaving the circuit, the power imbalance between generation and consumption causes a rapid frequency drop. In this case, the automatic generation control cannot stop the frequency drop, so the frequency tends toward instability. In this paper, to automatically produce a water-thermal system in two zones of discrete and continuous modes, the design of a fractional controller is discussed and investigated in different functional conditions and uncertainties. The proposed NSGA-II multi-objective method is used in this article to optimize each area’s load frequency control parameters for various goals. The ideal response for the entire network will be obtained by modifying these responses using a multi-factor method. This paper defines a multi-zone frequency load control based on time objective functions and eigenvalues. Based on the speed changes for the proposed algorithm and the novel approach function method employed in the control design. The simulation performed and analyzed in different working conditions with different comparative criteria has been discussed and investigated. Compared to more contemporary techniques, the simulation results show that the system’s performance is guaranteed. The main successes of the suggested method in comparison to earlier methods have been the reduction of overshoot, undershoot, and settling time.

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 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]

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]

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]

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]

To boost power system reliability there must be a good scheme for the automatic generation control to maintain the generation-load balance. The development of this scheme started with the enhancement of the sparrow search algorithm, where the initial population and producer selection was targeted to improve the search quality. The enhancement made was used to optimize the gain parameters of the PID controller increasing the overall system performance. The proposed scheme was tested on the two-area power system in the MATLAB/Simulink environment and comparisons were made with recent publications. Integral time absolute error (ITAE) was used as the performance index. The proposed method shows improved performance with minimum settling time. This work presents the enhancement of the sparrow search algorithm for the automatic generation control of a two-area non-reheat thermal power system.

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.

This paper proposes a new model integrating a linear quadratic regulator (LQR) controller to mitigate frequency disturbances in the power system during cyber-attack, called as linear quadratic regulator to mitigate frequency disturbances (LQRMFD). As we know, most of the existing models have a common problem with achieving significant performances in mitigating dynamic response parameters, such as frequency deviation and settling time. However, the key aspect of LQRMFD is to mitigate the above issues with remarkable performance improvements. An uncommon and stable power system model has been considered in LQRMFD first to reach such a goal. A numerical problem has been solved to derive a certain characteristic equation, where the Routh-Hurwitz array criterion is applied for determining the stability of such a power system. After that, a state-space equation is developed from the power system to activate the LQR controller. Thus, achieving diversity and eliminating the redundancy of the power system considered can be obtained in LQRMFD. To evaluate the performance of LQRMFD, a series of experiments was conducted using the MATLABSimulink tool. Rigorous comparisons were also made among the results of LQRMFD, self-implemented and existing models. Furthermore, a detailed analysis was reported among those models to find the performance improvement of LQRMFD in percentage. [ABSTRACT FROM AUTHOR]

Load frequency control (LFC) serves as a crucial component of automatic generation control in renewable energy power systems. Its primary objective is to maintain a balance between the output power of generators and the load demand, thereby ensuring system frequency stability. However, integrating renewable energy sources into power systems brings forth several challenges, such as low power quality and poor system stability due to their uncontrollable nature. To enhance the response speed, stability, and disturbance rejection capabilities of LFC, a novel fractional-order active disturbance rejection controller (NFOADRC) based on an improved marine predator algorithm (IMPA) has been designed in this paper. By leveraging the wide frequency-response range and non-local memory of NFOADRC, a more precise prediction and compensation of rapid oscillations in the system can be achieved. Additionally, the IMPA can be utilized for efficient parameter tuning, enabling a more accurate adjustment of the controller. Subsequently, the combined application of these approaches can be applied to two-area interconnected power systems with a solar thermal power plant (STPP) and a five-area interconnected power system including a wind turbine generator (WTG), photovoltaic (PV) cells, hydro turbine, and gas turbine. The simulation results confirm that the proposed control strategy effectively minimizes the undershoot and overshoot of frequency deviation in the power system. It achieves a faster stabilization of the load frequency, leading to enhanced power quality. [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.

The reinforcement learning (RL)-based generation control strategies have been widely studied to address the limited adaptability of traditional automatic generation control (AGC) strategies to the load disturbance problem resulting from heterogeneous energy sources. To improve the control accuracy of the RL-based strategy in load frequency control (LFC), a double deep Q-network combined with an upper confidence bound (DDQN-UCB)-based strategy is designed to solve the problem of agent decision-making in a nonlinear environment. Firstly, the area control error (ACE) and control performance standard 1 (CPS1) of the LFC power system are considered in the design of the RL reward function. Secondly, the actual and estimated Q-values are calculated using the Q-network and the target Q-network combined with the reward value. Thirdly, the deviation loss of the two Q-values is calculated, and the network is updated based on the loss value using gradient descent. Finally, the UCB algorithm is introduced to equalize the frequency of being selected for each action during the random exploration of the actions, and the agent uses the greedy algorithm in combination with the UCB algorithm to select a power-compensated control action to send to the environment. In this paper, the IEEE multi-area LFC power system is used as an experimental validation model. A comparison of the proposed RL control algorithm with five other algorithms revealed that the pre-learning convergence accuracy was improved by 57.5%. Furthermore, the LFC effectiveness test demonstrated that the DDQN-UCB control strategy enhances LFC accuracy while simultaneously stabilizing the power exchange of the inter-area tie-line to within 1.8972 MW, thereby maintaining the stability of the power system. [ABSTRACT FROM AUTHOR]

One of the crucial quantities in the power system problem is frequency, which should never exceed its nominal value. In normal conditions of load changes, the frequency is adjusted by automatic generation control. Still, in the face of special conditions, such as logging of the downstream network from the main network or a significant amount of generated power leaving the circuit, the power imbalance between generation and consumption causes a rapid frequency drop. In this case, the automatic generation control cannot stop the frequency drop, so the frequency tends toward instability. In this paper, to automatically produce a water-thermal system in two zones of discrete and continuous modes, the design of a fractional controller is discussed and investigated in different functional conditions and uncertainties. The proposed NSGA-II multi-objective method is used in this article to optimize each area's load frequency control parameters for various goals. The ideal response for the entire network will be obtained by modifying these responses using a multi-factor method. This paper defines a multi-zone frequency load control based on time objective functions and eigenvalues. Based on the speed changes for the proposed algorithm and the novel approach function method employed in the control design. The simulation performed and analyzed in different working conditions with different comparative criteria has been discussed and investigated. Compared to more contemporary techniques, the simulation results show that the system's performance is guaranteed. The main successes of the suggested method in comparison to earlier methods have been the reduction of overshoot, undershoot, and settling time. [ABSTRACT FROM AUTHOR]

Automatic generation control (AGC) in modern power systems (PS) is difficult because the output power of many power resources is intermittent, and the load and system parameters vary widely. In this paper, a novel control scheme known as the wavelet based multiresolution proportional integral derivative (MRPID) controller for multiple interconnected hybrid power sources is presented. The discrete wavelet transform (DWT) is used in the proposed wavelet based MRPID controller to split the error between the actual and target responses into different frequency components at several stages. To ensure optimum system performance, the gains of the MRPID controller are fine-tuned using the Fox Optimizer Algorithm (FOA), a new powerful metaheuristic technique. The proposed MRPID controller is evaluated in a three-area hybrid system where each area contains a combination of conventional generation (gas, thermal reheat and hydro) and renewable generation sources (solar, and wind). The proposed controller also accounts for system non-linearities, including boiler dynamics, time delay, dead band, generation rate limitation, system uncertainties, and load changes. In the hybrid system studied, the proposed MRPID is compared with FOA-tuned PID and PI controllers. The proposed MRPID controller tuned with FOA algorithm effectively reducing the peak overshoot of 89.03%, 76.89 and 56.96% and undershoot of 69.52%,66.90 and 94.29% for ∆Ptie12, ∆Ptie23 and ∆Ptie13 respectively as compared to FOA based PI controller.

In this article, a fractional-order proportional-integral-differential (FOPID) controller and its modified structure, called a MFOPID controller, are presented. To guarantee optimal system performance, the gains of the proposed FOPID and MFOPID controllers are well-tuned, employing the Jellyfish Search Optimizer (JSO), a novel and highly effective bioinspired metaheuristic approach. The proposed controllers are assessed in a hybrid system with two domains, where each domain contains a hybrid of conventional (gas, reheat, and hydro) and renewable generation sources (solar and wind). For a more realistic analysis, the presented system model includes practical limitations with nonlinear characteristics, such as governor dead zone/band (GDZ/GDB), boiler dynamics, generation rate limitation/constraint (GRL/GRC), system uncertainties, communication time delay (CTD), and load changes. The suggested methodology outperforms some newly developed heuristic techniques, including fitness-dependent optimizer (FDO), sine-cosine algorithm (SCA), and firefly algorithm (FA), for the interconnected power system (PS) of two regions with multiple generating units. Furthermore, the proposed MFOPID controller is compared with JSO-tuned PID/FOPID and PI controllers to ascertain its superiority. The results signify that the presented control method and its parametric optimization significantly outperforms the other control strategies with respect to minimum undershoot and peak overshoot, settling times, and ITSE in the system's dynamic response. The sensitivity analysis outcomes imply that the proposed JSO-MFOPID control method is very reliable and can effectively stabilize the load frequency and interconnection line in a multi-area network with interconnected PS. [ABSTRACT FROM AUTHOR]

To boost power system reliability there must be a good scheme for the automatic generation control to maintain the generation-load balance. The development of this scheme started with the enhancement of the sparrow search algorithm, where the initial population and producer selection was targeted to improve the search quality. The enhancement made was used to optimize the gain parameters of the PID controller increasing the overall system performance. The proposed scheme was tested on the twoarea power system in the MATLAB/Simulink environment and comparisons were made with recent publications. Integral time absolute error (ITAE) was used as the performance index. The proposed method shows improved performance with minimum settling time. This work presents the enhancement of the sparrow search algorithm for the automatic generation control of a two-area non-reheat thermal power system. [ABSTRACT FROM AUTHOR]

In this paper, Levy flight and Fitness Distance Balance (FDB)-based coyote optimization algorithm (LRFDBCOA) is proposed to improve the performance of automatic generation control (AGC) of three different PV-based interconnected power systems. First, the achievement of the proposed algorithm is evaluated on the two-area PV-reheat thermal power system with PI controller, which is widely used in the literature, and the results are compared with the results obtained using genetic algorithm (GA), firefly algorithm (FA), and modified whale optimization algorithm (M-WOA) introduced in previous studies. Then, the control performance of the proposed algorithm is demonstrated on the newly constructed two-area PV-non-reheat thermal power system with governor dead band (GDB) and two-area PV-multi-source power system with electrolyser and fuel cell. As the costs of solar energy systems decrease, the microgrids containing these resources increase. Therefore, such a study will make an important contribution to the literature. For the AGC of these two systems, proportional–integral–derivative (PID), tilt-integral-derivative (TID) and fractional order PID (FOPID) controllers are used. Thus, both the performance of the proposed algorithm on AGC and the compatibility of the two newly tested systems with PID, TID and FOPID controllers are revealed. As a result, the proposed LRFDBCOA algorithm outperforms algorithms such as GA, FA, M-WOA and arithmetic optimization algorithm (AOA) in interconnected two-area power systems controlled by PID, TID and FOPID, and it shows superior control performance in terms of settling time, maximum/minimum peak value of Δ f 1 , Δ f 2 and Δ P tie under varying load conditions and perturbed system parameters. [ABSTRACT FROM AUTHOR]

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.

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.

In this article, a fractional-order proportional-integral-differential (FOPID) controller and its modified structure, called a MFOPID controller, are presented. To guarantee optimal system performance, the gains of the proposed FOPID and MFOPID controllers are well-tuned, employing the Jellyfish Search Optimizer (JSO), a novel and highly effective bioinspired metaheuristic approach. The proposed controllers are assessed in a hybrid system with two domains, where each domain contains a hybrid of conventional (gas, reheat, and hydro) and renewable generation sources (solar and wind). For a more realistic analysis, the presented system model includes practical limitations with nonlinear characteristics, such as governor dead zone/band (GDZ/GDB), boiler dynamics, generation rate limitation/constraint (GRL/GRC), system uncertainties, communication time delay (CTD), and load changes. The suggested methodology outperforms some newly developed heuristic techniques, including fitness-dependent optimizer (FDO), sine-cosine algorithm (SCA), and firefly algorithm (FA), for the interconnected power system (PS) of two regions with multiple generating units. Furthermore, the proposed MFOPID controller is compared with JSO-tuned PID/FOPID and PI controllers to ascertain its superiority. The results signify that the presented control method and its parametric optimization significantly outperforms the other control strategies with respect to minimum undershoot and peak overshoot, settling times, and ITSE in the system’s dynamic response. The sensitivity analysis outcomes imply that the proposed JSO-MFOPID control method is very reliable and can effectively stabilize the load frequency and interconnection line in a multi-area network with interconnected PS.

The frequency value should be kept constant to ensure and maintain synchronization in power systems. When the balance between generation and load is interrupted, the frequency value increases or decreases. This frequency deviation may lead to serious problems in the power system. Therefore, a design of a controller is required to keep the system frequency and tie-line power variations within specified limits, which is called automatic generation control (AGC) or load frequency control (LFC). This paper aims to determine the optimal controller parameters used in the LFC for a two-area non-reheat thermal power system integrated with various renewable energy sources (RES) such as photovoltaic (PV) and wind energy systems. The proposed controller is a PI–(1 + DD) controller which is a combination of proportional, integral, and double derivative controllers. The optimal gains of the proposed controller are determined by the Grey Wolf Optimization (GWO) algorithm. Moreover, the performance of the PI–(1 + DD) controller is tested under various scenarios such as different step load perturbations, random load changes, system parameters and RES variation. The results show that the PI–(1 + DD) controller provides an improvement of about 40% in system frequency overshoot and about 45% in settling time compared to other controllers. [ABSTRACT FROM AUTHOR]

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 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) %.

Nowadays, the restructuring of power systems is extremely urgent due to the depletion of fossil fuels on the one hand and the environmental impact on the other. In the restructured environment, the incorporation of renewable energy sources and storage devices is key as they have helped achieve a milestone in the form of microgrid technology. As the restructuring of the power system increases, there are several types of generation sources, and distribution companies express their interest in trading in a deregulated environment to operate economically. When considering the power system deregulation, the contract value deviates in some situations, resulting in an imbalance between the generation and the energy consumption, which can bring the system into a power outage condition. In particular, load frequency control has been a great challenge over the past few decades to ensure the stable operation of power systems. This study considers two generation sources: mini-hydro in GENCO-1 and 3 and microgrid (combination of wind, fuel cell, battery storage, and diesel engine) in GENCO-2 and 4. It is two equal-area networks; in area-1, GENCO-1 and 2, and in area-2, GENCO-3 and 4 are considered, respectively. In addition, a FOPID controller and two ancillary devices, such as a unified power flow controller and a superconducting magnetic energy storage system, have been incorporated. Three different test networks have been formed according to the contract value, such as unilateral, bilateral, and agreement violations. The simulation results show that ancillary devices and controller participation significantly enhance the system response by reducing the frequency and tie-line power fluctuation. To validate the efficacy of the proposed method, respective performance indices and percentages of improvement have been obtained. Finally, this study demonstrated the effectiveness of the proposed restructured power system in a deregulated environment.

Copyright of Przegląd Elektrotechniczny is the property of Przeglad Elektrotechniczny 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.)

This article discusses a Whale Optimization (WO) controller for design analysis, stability management, application, and performance analysis of a deregulated two-area multi-source energy system. Within the power grid, the Automatic Generation Control (AGC) maintains the power balance and strengthens the sudden frequency disturbance of the grid. Various researchers are targeted many kinds of fast-acting energy-storing devices to dampen space frequency oscillations and diversion of the tie line because of unexpected load variation. To regulate the power flow between multi-areas, load frequency control (LFC) is used by keeping frequency constant. But, to design a controller for minimizing the issues of LFC remains a challenge within the deregulated atmosphere. The main objective of the proposed methodology is to design a controller for minimizing the issues in LFC within the deregulated atmosphere. A two-area deregulated hydrothermal power system is employed to analyze many LFC problems in the deregulated power grid. During this modeling, the Thyristor-Controlled Phase Shifters (TCPS) and Capacitive Energy Storage unit (CES) are placed in the tie line and its control space. To mitigate the various issues of LFC in a two-area deregulated hydrothermal power system, the CES–TCPS combination is placed between the two control areas and the transient performance of constant is evaluated. The WO controller will increase the system power size to focus on the better performance of low-frequency management tasks. In addition, the effectiveness of the WO algorithm in tuning the proportional integral controller is also compared in a different contract (unilateral, bilateral and contract violation cases) scenarios of the proposed deregulated power system. Finally, the simulation is carried out in MATLAB/Simulink platform and the results obtained for the WO controller is compared with the normal and fuzzy PI controllers. [ABSTRACT FROM AUTHOR]

In a unified power system the Load Frequency Control (LFC) is taken for review looking at all aspects with various optimization techniques for optimizing the parameters of PI, PID and Fuzzy controllers. The power system with multi area consists of conventional, renewable and combination of both with some energy devices like SMES, battery sources is analyzed specifically in this manuscript. The controllers are designed for a deregulated environment of a power system for LFC. The Model Predictive Control (MPC) and some other control techniques are used to control LFC under various disturbances like GRC and dead band control. For the readers ease of understanding the time response comparison graph of the controller for single and multiple areas of power system is depicted.

Automatic Generation Control (AGC) delivers a high quality electrical energy to energy consumers using efficient and intelligent control systems ensuring nominal operating frequency and organized tie-line power deviation. Subsequently, for the AGC analysis of a two-area interconnected hydro-gas-thermal-wind generating unit, a novel Fractional Order Integral-Tilt Derivative (FOI-TD) controller is proposed that is fine-tuned by a powerful meta-heuristic optimization technique referred as Improved-Fitness Dependent Optimizer (I-FDO) algorithm. For more realistic analysis, various constraints, such as Boiler Dynamics (BD), Time Delay (TD), Generation Rate Constraint (GRC), and Governor Dead Zone (GDZ) having non-linear features are incorporated in the specified system model. Moreover, a comparative analysis of I-FDO algorithm is performed with state-of-the-art approaches, such as FDO, teaching learning based optimization, and particle swarm optimization algorithms. Further, the proposed I-FDO tuned controller is compared with Fractional Order Tilt Integral Derivative (FOTID), PID, and Integral-Tilt Derivative (I-TD) controllers. The performance analysis demonstrates that proposed FOI-TD controller provides better performance and show strong robustness by changing system parameters and load condition in the range of  ± 50%, compared to other controllers.

The load frequency control (LFC) and tie-line power are the key deciding factors to evaluate the performance of a multiarea power system. In this paper, the performance analysis of a two-area power system is presented. This analysis is based on two performance metrics: LFC and tie-line power. The power system consists of a thermal plant generation system and a hydro plant generation system. The performance is evaluated by designing a proportional plus integral (PI) controller. The hybrid gravitational search with firefly algorithm (hGFA) has been devised to achieve proper tuning of the controller parameter. The designed algorithm involves integral time absolute error (ITAE) as an objective function. For two-area hydrothermal power systems, the load frequency and tie-line power are correlated with the system generation capacity and the load. Any deviation in the generation and in the load capacity causes variations in the load frequencies, as well as in the tie-line power. Variations from the nominal value may hamper the operation of the power system with adverse consequences. Hence, performance of the hydrothermal power system is analyzed using the simulations based on the step load change. To elucidate the efficacy of the hGFA, the performance is compared with some of the well-known optimization techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), gravitational search algorithm (GSA) and the firefly algorithm (FA).

The automatic load frequency control for multi-area power systems has been a challenging task for power system engineers. The complexity of this task further increases with the incorporation of multiple sources of power generation. For multi-source power system, this paper presents a new heuristic-based hybrid optimization technique to achieve the objective of automatic load frequency control. In particular, the proposed optimization technique regulates the frequency deviation and the tie-line power in multi-source power system. The proposed optimization technique uses the main features of three different optimization techniques, namely, the Firefly Algorithm (FA), the Particle Swarm Optimization (PSO), and the Gravitational Search Algorithm (GSA). The proposed algorithm was used to tune the parameters of a Proportional Integral Derivative (PID) controller to achieve the automatic load frequency control of the multi-source power system. The integral time absolute error was used as the objective function. Moreover, the controller was also tuned to ensure that the tie-line power and the frequency of the multi-source power system were within the acceptable limits. A two-area power system was designed using MATLAB-Simulink tool, consisting of three types of power sources, viz., thermal power plant, hydro power plant, and gas-turbine power plant. The overall efficacy of the proposed algorithm was tested for two different case studies. In the first case study, both the areas were subjected to a load increment of 0.01 p.u. In the second case, the two areas were subjected to different load increments of 0.03 p.u and 0.02 p.u, respectively. Furthermore, the settling time and the peak overshoot were considered to measure the effect on the frequency deviation and on the tie-line response. For the first case study, the settling times for the frequency deviation in area-1, the frequency deviation in area-2, and the tie-line power flow were 8.5 s, 5.5 s, and 3.0 s, respectively. In comparison, these values were 8.7 s, 6.1 s, and 5.5 s, using PSO; 8.7 s, 7.2 s, and 6.5 s, using FA; and 9.0 s, 8.0 s, and 11.0 s using GSA. Similarly, for case study II, these values were: 5.5 s, 5.6 s, and 5.1 s, using the proposed algorithm; 6.2 s, 6.3 s, and 5.3 s, using PSO; 7.0 s, 6.5 s, and 10.0 s, using FA; and 8.5 s, 7.5 s, and 12.0 s, using GSA. Thus, the proposed algorithm performed better than the other techniques.

Industrial equipment such as electric arc furnaces and steel mills are often associated with rapid and high load disturbances, so their power systems require additional control equipment to limit the frequency. However, proper ancillary service fees are not paid in these cases with extreme and variable load demands. The frequency regulation reserve equipment adds to power generation costs. Therefore, variable power generation loads lead to increases in the cost of energy production. We propose a load frequency control method that is applied on the customer end instead of the power supply end to reduce the operating reserve required to improve the energy efficiency of the power system. We analyzed the load fluctuation of steel mill customers using real data sampled at two-second intervals from the energy management system in Korea. We developed an automatic generation control program to simulate the power system’s frequency characteristics. We also propose compensation techniques for mitigation of the system’s frequency deviation at the customer end based on an energy storage system, pump storage hydro generator, customer generator, and plant process adjustment. To recover the frequency deviation, we calculated the compensation facility capacity and analyzed static characteristics, and we proved the feasibility via simulations.

In this study, the authors represent a modelling to analyse and simulate renewable power generation for two area power systems in the presence of high penetrated wind farm. The performance of assumed power systems may hazard without appropriate frequency amelioration methodologies. To complete the LFC model for two area power systems, the combination of automatic generation control and automatic voltage regulation of thermal units is considered. Due to the decline in the total inertia of power system associated with wind farm contribution, the self‐tuning and adaptive fuzzy‐based PID droop can be proposed in the structure of wind turbines instead of the fixed/traditional PID droop in de‐loaded area to ameliorate the frequency excursions. Besides, the artificial bee colony algorithm can tune the parameters of membership functions for input and output signals based on a multi‐objective function (MOF). The proposed strategy control is proved to be accurately stable under various load changes and yields more satisfactory performance in comparison to the conventional PID droop. This research generally includes wind farm collaboration in the frequency control by inertia, primary and secondary frequency control. [ABSTRACT FROM AUTHOR]

In this paper, a novel optimal combined fuzzy PID controller employing Dragonfly Algorithm (DA) was proposed for solving Automatic Generation Control (AGC) problem in interconnected power systems. The DA algorithm was employed in this work to optimize the controller parameters including the scaling factors of fuzzy logic and the PID gains. To show the potential of the proposed approach, initially, a two equal areas non-reheat thermal power system was investigated, and then the proposed approach was extended to a typical AC/DC two-area multisource power system connected via an AC tie-line as well as High Voltage Direct Current (HVDC) transmission link. The superiority of the proposed controller was proven by comparing the obtained results with the previous AGC techniques reported in the literature. Moreover, sensitivity analysis was done. Further, the proposed control strategy was also extended to a nonlinear three-area thermal power system model by considering the effect of appropriate Generation Rate Constraint (GRC) with nonlinearity in each area. Finally, the results were tabulated in terms of settling time and peak overshoot. The comparative study demonstrates the effectiveness of the proposed control strategy and shows its robustness to enhance frequency regulation loop of linear and nonlinear interconnected power systems. [ABSTRACT FROM AUTHOR]

Wind farms can contribute to ancillary services to the power system, by advancing and adopting new control techniques in existing, and also in new, wind turbine generator systems. One of the most important aspects of ancillary service related to wind farms is frequency regulation, which is partitioned into inertial response, primary control, and supplementary control or automatic generation control (AGC). The contribution of wind farms for the first two is well addressed in literature; however, the AGC and its associated controls require more attention. In this paper, in the first step, the contribution of wind farms in supplementary/load frequency control of AGC is overviewed. As second step, a fractional order proportional-integral-differential (FOPID) controller is proposed to control the governor speed of wind turbine to contribute to the AGC. The performance of FOPID controller is compared with classic proportional-integral-differential (PID) controller, to demonstrate the efficacy of the proposed control method in the frequency regulation of a two-area power system. Furthermore, the effect of penetration level of wind farms on the load frequency control is analyzed. [ABSTRACT FROM AUTHOR]