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Robust control methodology for two-area load frequency control model is proposed in this paper. The paper presents a comparative study between the performance of model predictive controller (MPC) and optimized proportional–integral–derivative (PID) controller on different systems. An objective function derived from settling time, percentage overshoot and percentage undershoot is minimized to obtain the gains of the PID controller. Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) are used to tune the parameters of the PID controller through performance optimization of the system. System performance characteristics were compared to another controller designed based on MPC. Detailed comparison was performed between the performances of the MPC and optimized PID. The effectiveness and robustness of the proposed schemes were verified by the numerical simulation in MATLAB environment under different scenarios such as load and parameters variations. Moreover, the pole-zero map of each proposed approach is presented to investigate their stability.

The motivation for this paper is that most of the works on secondary frequency control are focused on conventional synchronous communication approaches. To extend this research, this paper investigates the sampled-data-based H∞ load frequency control (LFC) problem for fractional-order islanded microgrids under a multi-region communication scheme. In contrast to conventional synchronous communication approaches, the proposed scheme allows each regional sensor to operate asynchronously based on its own sampling interval. To model this multi-region communication mechanism, a unified sampling sequence is constructed by collecting all sampling instants from regional sensors. Accordingly, a closed-loop system model is established through the introduction of virtual state variables. Furthermore, a novel class of looped functionals is developed to fully exploit the sampling interval characteristics of each regional sensor. By employing inequality techniques and stability analysis, sufficient conditions are derived to achieve multi-region sampled-data-based H∞ LFC for fractional-order islanded microgrids. In addition, a co-design method is proposed to simultaneously determine the control gain and the maximum allowable sampling period. The simulations are conducted in MATLAB/Simulink (R2024a) and the LMI conditions are solved by using the LMI Toolbox and YALMIP. Finally, comprehensive simulations in MATLAB/Simulink validate the proposed scheme. For the two-region system, the method achieves a maximum sampling period of ζmax=0.106 s with an H∞ performance ratio of 2.87 (below γ=5) and settling times of 8.5 s and 9.2 s. Compared to synchronous sampling, it reduces the communication bandwidth by 50% for slower regions while maintaining comparable performance. For the single-region multi-rate case (0.104 s and 0.140 s sampling periods), the H∞ ratio is 3.12, also satisfying γ=5. The relationship between γ and ζmax is quantified: ζmax increases from 0.050 s to 0.106 s as γ increases from 3 to 5, confirming that relaxed disturbance attenuation allows larger sampling intervals.

This study explores the multi-rate sampling-based H∞ load frequency control (LFC) problem for networked power systems by using an area-information-fusion method. This problem is addressed for two reasons: (1) most of networked control methods for LFC are focused on the one-rate sampling scheme and (2) the previous looped function cannot be directly applied within the multi-rate sampling scheme. Here, the multi-rate sampling scheme involves each area sampling rate being reliant on its own sensor. Namely, all area sampling rates are different from each other. In the presence of a multi-rate sampling scheme, a new sampling instants sequence is established by using an area-information-fusion method. It contributes to constructing a corresponding closed-loop model by adding virtual state variables. In addition, a new looped-function approach is devised to capture the sampling information from diverse area sensors. Based on Lyapunov stability theory, less conservative LMI conditions are derived to guarantee the H∞ performance of the multi-rate LFC system. Additionally, a co-designed method for determining the control gain and maximum sampling frequency is established. Finally, simulation studies are conducted to validate the efficacy and features of the proposed control strategy.

Load frequency control is an important factor of supplying quality electricity in an interconnected power system. As a result, an optimally tuned Proportional-Integral-Derivative (PID) controller is proposed in this work to eliminate frequency errors caused by unexpected load changes while maintaining tie-line power exchange. The PID controller is tuned using several optimization techniques such as GA, PSO, SCA, and GWO. A two-area power system with Generation Rate Constraint is studied in the first instance, and a three-area thermal power system with both generation rate constraint and dead band effect is considered in the second case. In both scenarios, a PID controller is employed for each area. When compared to the results of other optimization approaches for the same integrated power system, such as Genetic Algorithm, Particle Swarm Optimization, and Sine Cosine Algorithm, the GWO-based PID controller outperforms them in both scenarios.  According to the simulation findings, the GWO technique gives better dynamic responses in terms of overshoot value, settling time, and Integral Time Absolute Error.  Finally, to evaluate the robustness of the suggested optimization strategies, sensitivity analysis is done by modifying the system parameters (turbine time constant, governor time constant, and both simultaneously) in the range of 25% from their nominal values.

Load‐frequency control (LFC) is essential for maintaining system stability and ensuring high power quality in microgrids (MGs), particularly those heavily reliant on renewable energy sources (RES) and operating independently of the main grid. This paper introduces a novel control strategy aimed at improving LFC performance in interconnected MGs by correcting the error signal. The proposed controller, denoted as TIDA+1, combines tilt, integrator, derivative, and acceleration operators in a parallel configuration to refine the incoming error signal. The controller parameters are optimized using a modified particle swarm optimization (PSO) algorithm with nonlinear time‐varying acceleration coefficients (NTVAC). The controller's effectiveness is validated through four distinct scenarios, including sudden load variations, system modeling uncertainties, fluctuations in RES outputs, and the impact of nonlinearities. Additionally, a lab‐scale evaluation of the controller has been conducted to further assess its practical applicability. Comparative results demonstrate that the TIDA+1 controller outperforms traditional controllers such as PID and FOPID, especially under complex operational conditions. The study highlights the TIDA+1 controller as a robust and viable solution for LFC in MGs, with potential for future scalability and application in larger systems. Numerical assessments and comparisons demonstrate that the stage‐by‐stage optimized TIDA+1 controller, despite its simple design, exhibits markedly superior performance under intricate working situations compared with traditional controllers of the same category, such as PID and FOPID. Consequently, it represents a viable choice for executing the LFC tasks in the MGs.

The deregulation of the electricity market made the open communication infrastructure an exigent need for future power system. In this scenario dedicated communication links are replaced by shared networks. These shared networks are characterized by random time delay and data loss. The random time delay and data loss may lead to system instability if they are not considered during the controller design stage. Load frequency control systems used to rely on dedicated communication links. To meet future power system challenges these dedicated networks are replaced by open communication links which makes the system stochastic. In this paper, the stochastic stabilization of load frequency control system under networked environment is investigated. The shared network is represented by three states which are governed by Markov chains. A controller synthesis method based on the stochastic stability criteria is presented in the paper. A one-area load frequency control system is chosen as case study. The effectiveness of the proposed method for the controller synthesis is tested through simulation. The derived proportion integration (PI) controller proves to be optimum where it is a compromise between compensating the random time delay effects and degrading the system dynamic performance. The range of the PI controller gains that guarantee the stochastic stability is determined. Also the range of the PI controller gains that achieve the robust stochastic stability is determined where the decay rate is used to measure the robustness of the system.

Power systems are the most complex systems that have been created by men in history. To operate such systems in a stable mode, several control loops are needed. Voltage frequency plays a vital role in power systems which need to be properly controlled. To this end, primary and secondary frequency control loops are used to control the frequency of the voltage in power systems. Secondary frequency control, which is called Load Frequency Control (LFC), is responsible for maintaining the frequency in a desirable level after a disturbance. Likewise, the power exchanges between different control areas are controlled by LFC approaches. In recent decades, many control approaches have been suggested for LFC in power systems. This paper presents a comprehensive literature survey on the topic of LFC. In this survey, the used LFC models for diverse configurations of power systems are firstly investigated and classified for both conventional and future smart power systems. Furthermore, the proposed control strategies for LFC are studied and categorized into different control groups. The paper concludes with highlighting the research gaps and presenting some new research directions in the field of LFC.

Due to the increased complexity and nonlinear nature of microgrid systems such as photovoltaic, wind-turbine fuel cell, and energy storage systems (PV/WT/FC/ESSs), load-frequency control has been a challenge. This paper employs a self-tuning controller based on the fuzzy logic to overcome parameter uncertainties of classic controllers, such as operation conditions, the change in the operating point of the microgrid, and the uncertainty of microgrid modeling. Furthermore, a combined fuzzy logic and fractional-order controller is used for load-frequency control of the off-grid microgrid with the influence of renewable resources because the latter controller benefits robust performance and enjoys a flexible structure. To reach a better operation for the proposed controller, a novel meta-heuristic whale algorithm has been used to optimally determine the input and output scale coefficients of the fuzzy controller and fractional orders of the fractional-order controller. The suggested approach is applied to a microgrid with a diesel generator, wind turbine, photovoltaic systems, and energy storage devices. The comparison made between the results of the proposed controller and those of the classic PID controller proves the superiority of the optimized fractional-order self-tuning fuzzy controller in terms of operation characteristics, response speed, and the reduction in frequency deviations against load variations. Graphical abstract

In our world of today developing incredibly fast, load frequency control (LFC) is an indispensable and vital element in increasing the standard of living of a country by providing a good quality of electric power. To this end, rapid and notable development has been recorded in LFC area. However, researchers worldwide need for the existence of not only effective but also computationally inexpensive control algorithm considering the limitations and difficulties in practice. Hence, this paper deals with the introduction of (1 + PD)-PID cascade controller to the relevant field. The controller is simple to implement and it connects the output of 1 + PD controller with the input of PID controller where the frequency and tie-line power deviation are applied to the latter controller as feedback signals also, which is the first attempt made in the literature. To discover the most optimistic results, controller gains are tuned concurrently by dragonfly search algorithm (DSA). For the certification purpose of the advocated approach, two-area thermal system with/without governor dead band nonlinearity is considered as test systems initially. Then single/multi-area multi-source power systems with/without a HVDC link are employed for the enriched validation purpose. The results of our proposal are analyzed in comparison with those of other prevalent works, which unveil that despite its simplicity, DSA optimized (1 + PD)-PID cascade strategy delivers better performance than others in terms of smaller values of the chosen objective function and settling time/undershoot/overshoot of the frequency and tie-line power deviations following a step load perturbation.

There is a global push to develop renewable energy to further a low-carbon society. However, the nature of variable renewable energy (VRE) sources such as wind power and solar photovoltaic (PV) systems may create problems because electricity grids require a stable power supply to match demand. To evaluate the potential capacity of VREs that may be installed, we develop an optimized model that balances power supply and demand and also considers grid balancing by battery storage and load frequency control. The model was applied to a case study of an isolated grid on a remote Japanese island. When set to optimize the grid in terms of lowest cost, the model suggested that, compared with the base case, the capacity of wind power should be increased by a factor of 1.7 and 15.8 for situations without and with battery storage, respectively. Since it was always considered to be more expensive than wind power, no change in solar PV capacity was observed. These approaches resulted in a decrease in the total power generation cost of 2% and 24%, respectively, while total CO2 emissions fell by 3% and 52%, primarily driven by decreased used of the existing fossil-fueled thermal plant.