Load Frequency Regulation Research Articles

Resilient Event-Triggered MPC for Load Frequency Regulation With Wind Turbines Under False Data Injection Attacks

Resilient Event-Triggered MPC for Load Frequency Regulation With Wind Turbines Under False Data Injection Attacks

Exponential PID controller for effective load frequency regulation of electric power systems

Load frequency regulation (LFR) is an indispensable scheme in planning electrical power production to provide consumers with stable, reliable and uninterrupted power. In the face of complicated power system (PS) structures with increasing and intricate power demand, new controllers that offer not only good performance, but also easy commissioning in practice are required. To this end, this research introduces an exponential PID (EXP-PID) controller as a new control scheme to ameliorate the LFR performance of PSs. This controller is simple to design and has a nonlinear feature inherited from two tunable exponential functions, which are placed in front of the PID controller and act on the error signal and its time derivative individually. To achieve the utmost performance, the EXP-PID controller’s parameters are procured by a corrected variant of the snake optimizer (co-SO). To validate the proposed control scheme, various single-/multi-area single-/multi-source PSs favored in the area are considered as test benches. A thorough comparison with the state-of-the-art approaches is performed to disclose the true efficacy of our proposal. Among the rivals, co-SO tuned EXP-PID controller, despite its simplicity, is found to render credible and promising performance in mitigating frequency and tie-line power deviations effectively.

A Comparative Study of Load Frequency Regulation for Multi-Area Interconnected Grids Using Integral Controller

The present paper provides an optimal design for load frequency control (LFC) in the interconnected power system. To obtain an adequate LFC response alongside shortening implementation time and minimizing costs, an integral (I) controller is used. A deep analysis of the I controller-based LFC is presented. At first, a two-area interconnected power system is used, and to enhance the LFC response, the I controller and frequency bias parameters are optimized using three novel optimization algorithms, which are the incomprehensible but intelligible-in-time logic algorithm (ILA), the coati optimization algorithm (COA), and the brown-bear optimization algorithm (BOA). Also, five well-known techniques, namely, particle swarm optimization (PSO), genetic algorithm (GA), simulated annealing (SA), pattern search (PS), and nonlinear programming (NP), are used. A new objective function utilizing the integral of squared error (ISE), settling time, settling-max, and settling-min of the dynamic response is used to increase the efficacy of estimating the parameters. The presented results in this paper showed that the optimized I controller outperforms the classic I controller. After considering a load change in one area by 18.75%, the optimized I controller achieved the lowest ISE values. ISE values were: 0.00582, 0.00179, 0.00176, 0.00178, 0.00321, 0.00304, 0.00179, 0.00185, and 0.00181, for classic I, PSO-I, GA-I, SA-I, PS-I, NP-I, ILA-I, COA-I, and BOA-I. Then, the proposed method is applied to a nonlinear two-area system, demonstrating that the proposed strategies can deal with nonlinearity. As the purpose of the hybrid power system is to create a robust energy infrastructure that adheres to sustainability standards, the proposed algorithms are analyzed in a three-area multi-source power system comprising renewable energy sources (RESs) such as photovoltaic (PV) and wind turbine (WT), a battery energy storage system (BESS), and an electric vehicle (EV).

Cascaded Fractional-Order Controller-Based Load Frequency Regulation for Diverse Multigeneration Sources Incorporated with Nuclear Power Plant

To sustain a system frequency within acceptable limits, it is widely conceded that retaining a power balance between generation and demand is necessary. In order to regulate the frequency of power systems (PSs) this article proposes a novel cascaded based fractional-order controller termed as fractional-order integer- (FOI-) fractional-order proportional integral with double derivative (FOPIDD2). In addition to redox flow batteries and capacitive energy storage, the recommended control strategy has been validated with gas, thermal reheat, hydro, and nuclear power systems. Additionally, a newly designed algorithm known as squid game optimizer (SGO) optimizes the gains of the new FOI-FOPIDD2 controller. The squid game optimizer technique is inspired by the fundamental principles of a conventional Korean sport. It employs a population of candidate solutions and iteratively adjusts the control parameters to discover the optimal set that reduces frequency abnormalities and improves system stability. A comparison is also made between the controller’s performance and benchmarks, including the jellyfish search algorithm, the firefly algorithm, the grey wolf optimizer, and the particle swarm algorithm. The proposed algorithms reduced peak overshoot as compared to grey wolf optimizer algorithm by 35.34%, 46.78%, and 76.89%; jellyfish search optimization algorithm by 34.76%, 77.22%, and 82.56%; and firefly algorithm by 82.67%, 89.23%, and 29.67% for frequency variations in area 1, area 2 and tie line power, respectively. Furthermore, SGO-FOI-FOPIDD2 controllers under different loading circumstances and conditions were evaluated and endorsed for their ability to withstand uncertainties in power system parameters.

A Systematic Load Frequency Regulation in Two Area Renewable Micro-grid Cluster Utilizing Bald Eagle Search Algorithm

This paper works on the systematic load frequency and tie-lie power transfer regulation within the prescribed limit. The proposed two area renewable energy based micro-grid cluster comprises of dish stirling solar generator, micro hydro turbine, biogas generator as energy generators in area 1. Also, wind turbine, tidal generator, biogas generator as energy generators in area 2. The Flywheel and battery act as mechanical and electrical energy storage devices in their respective areas. Moreover, to make the frequency oscillation die out quickly super magnetic storage devices are connected in the system. A comparison of performance of system with respect to objective function error is made to determine superior controller with minimum error and most stability. The controller parameters is adjusted with Bald eagle search optimizer, Black widow optimizer algorithm, Giza pyramid construction, Teaching learning based optimizer and Genetic algorithm. Lastly, best controller-algorithm i.e. (tilt integral tilt derivative with filter - Bald eagle search optimizer) combination with minimized objective function integral square error is examined under different scenarios.

A Novel Handling Method to Intermittent Feedback in Load Frequency Regulation for Renewable Energy-Dominated Microgrids

A Novel Handling Method to Intermittent Feedback in Load Frequency Regulation for Renewable Energy-Dominated Microgrids

Virtual Inertia Controller Design Based on Mixed Sensitivity Constraint H∞ Approach for Load Frequency Regulation of Islanded AC Microgrid

Virtual Inertia Controller Design Based on Mixed Sensitivity Constraint H∞ Approach for Load Frequency Regulation of Islanded AC Microgrid

Performance improvement of DC servo motor using sliding mode controller

<p>Sliding mode control has emerged as a valuable technique for enhancing dynamic response in various fields, including load frequency regulation and remote vehicle applications. While the widely adopted PID controller has proven effective for optimizing control tasks in industries, sliding mode control offers distinct advantages. By controlling the slope of the dynamical trends of state variable behavior, it enables rapid dynamic response with minimal or no overshoot, as well as negligible steady-state error. The robustness of sliding mode control, which makes it highly resilient to changes in plant parameters and outside disturbances, is one of its main advantages. A digital computer simulation was run using Simulink in the MATLAB software, concentrating on a position control system using an armature voltage-controlled D.C. servo motor to assess how well it performed. To learn more about the operation of sliding mode control, several control laws were used and state trajectories were examined. When compared to the conventional tuned PID control, the findings and discussion conclusively show sliding mode control to be more successful. The sliding mode technique has exceptional effectiveness, including enhanced dynamic response, less overshoot, and almost no steady-state error. Furthermore, its robust nature ensures consistent operation even in the face of parameter fluctuations and external disturbances. This study underscores the immense potential of sliding mode control as a powerful alternative to conventional control methods. Its ability to enhance system performance, coupled with its inherent robustness, makes it a compelling choice for various industrial applications where precise control and resilient operation are crucial.</p>

Study on the dynamic characteristics, control strategies and load variation rates of the concentrated solar power plant

As more and more wind power and photovoltaic power are connected to the electrical power system, it brings great challenges to the stability of power grid. Concentrated solar power (CSP) plant with thermal energy storage (TES) can undertake the task of load regulation and frequency regulation in power grid by balancing the electricity demand and generation. However, the maximum load variation rates of the CSP plant are not known, which restricts sufficient utilization of its advantages. In this study, a dynamic simulation model of CSP plant was built by using the APROS, and the dynamic characteristics were investigated by applying the step disturbances to molten salt flowrate, molten salt temperature, and the opening of steam regulating valve. Based on the results of dynamic characteristics, the control strategies were developed with the consideration of thermal storage in system and the feedforward signal of setting load and load variation rate. Then, the maximum load increase and reduction rates within different load ranges were studied. The results indicated that steam regulating valve, molten salt flowrate and molten salt temperature could be used to control turbine power, steam pressure and steam temperature, respectively. The designed control system could effectively follow the load variation with load deviation less than 0.3 MW and steam temperature deviation less than 1.2 °C in the condition of load variation rate at 3% Pe·min−1, which was superior to the conventional control strategy not considering the thermal storage in system. The maximum load increase and reduction rates were 11.57% Pe·min−1 and 8.94% Pe·min−1 within the load range of 75%THA-100%THA, and they were 10.42% Pe·min−1 and 7.46% Pe·min−1 within the load range of 50%THA-75%THA, respectively. The above load variation rates were first demonstrated for CSP plant, which could provide important guidance for plant operation in load regulation task and the load dispatching on grid side.

Regulation of Load Frequency for a Hybrid Wind-Ocean Wave Energy-Based Microgrid via Advanced Fuzzy Logic-Based Control Approach

In this article, a model of wind and ocean wave power generation-based microgrid (MG) system with diesel engine generator and battery storage system is developed. To replicate the more practical scenarios, various system non-linearities, as well as measurement time delay, are also incorporated in the designing of the considered MG system. The stochastic nature of wind and ocean, and load demands create an imbalance among sources and loads, which may lead to severe frequency fluctuation. To mitigate the effect of frequency fluctuations, an advanced fuzzy logic (FL)-based control method is proposed for load frequency regulation (LFR). The various variants of FL-based control schemes, such as fuzzy P, fuzzy P + I, fuzzy PI, cascaded fuzzy PI-PD, and fuzzy PID controllers, are designed and implemented for the LFR of the considered hybrid MG system (HMGS). A comparative analysis of HMGS with all designed and implemented controllers is performed for the identification of the suitable controller to alleviate the effect of frequency fluctuations. The obtained results show the effectiveness of the cascaded fuzzy PI-PD controller over the other designed controllers. Finally, the stability analysis of HMGS with proposed cascaded fuzzy PI-PD control approach is also performed.

Sliding mode controller design via delay-dependent H_{infty } stabilization criterion for load frequency regulation

This work presents a control approach based on sliding-mode-control (SMC) to design robust H_{infty } state feedback controllers for load frequency regulation of delayed interconnected power system (IPS) with parametric uncertainties. Considering both state feedback control strategy and delayed feedback control strategy, two SMC laws are proposed. The proposed control laws are designed to improve the stability and disturbance rejection performance of delayed IPS, while stabilization criteria in the form of linear matrix inequality are derived by choosing a Lyapunov–Krasovskii functional. An artificial time-delay is incorporated in the control law design of the delayed feedback control structure to enhance the controller performance. A numerical example is considered to study the control performance of the proposed controllers and simulation results are provided to observe the dynamic response of the IPS.

Multi-area load frequency regulation of a stochastic renewable energy-based power system with SMES using enhanced-WOA-tuned PID controller

This paper presents a novel nature-inspired meta-heuristic optimization algorithm known as the Enhanced Whale Optimization Algorithm (EWOA), which imitates humpback whales' social behavior to solve the optimization of multi-area automatic load frequency control (LFC) problems of a stochastic renewable energy-based power system with superconducting magnetic energy storage (SMES). An EWOA algorithm is presented in response to the limitations of the conventional WOA algorithm, including its sluggish convergence time, low accuracy, and propensity to easily enter local optimum. The system model investigated includes some physical constraints such as the time delay (TD), generation rate constraint (GRC), reheat turbine (RT), and the dead band (DB). The impacts of these physical constraints on the dynamic performance of the proposed controller were investigated. The EWOA algorithm is utilized to dynamically optimize the parameters of the PID controller for optimal system performance. The effectiveness and dynamic performance of the proposed controller are compared with the conventional WOA using some performance metrics. The system model also includes superconducting magnetic energy storage (SMES) units in both areas and their impacts on the system performances are also investigated. The effects of the changes of two different parameters of the system (frequency bias parameter, B, and the governor speed regulation, R) on the frequency deviation responses and the controller's robustness are examined. It is evident from the results that the dynamic performance of the proposed controller is better than that of the conventional WOA and it is more robust and stable to changes in system loading, parameters, and step load perturbation.

Synthesis of State/Output Feedback Event-Triggered Controllers for Load Frequency Regulation in Hybrid Wind–Diesel Power Systems

Hybrid power systems based on renewable energy sources and diesel generators are efficient solutions for supplying electricity to remote and off-grid locations. One of the most crucial problems in hybrid power systems is frequency regulation, which is established by balancing the supplied power with the load demand using the load frequency control approach. Since most feedback signals are analog and the control setups are digital, the resulting control system is a sampled-data system, which requires careful designs for both the control law and the sampling frequency to guarantee closed-loop stability. This paper is concerned with the state-feedback load frequency regulation for hybrid wind–diesel power systems under event-triggered implementation. It is assumed that the full state measurement is available for feedback and that sensors and controllers communicate over a shared digital network. To mitigate the communication load on the network, an event-triggering mechanism is constructed by emulation, based on the time-regularization principle in the sense that each consecutive triggering instant is speared by a specified minimum dwell time. The closed-loop system is described as a hybrid dynamical system to account for mixed dynamical behaviors naturally arising in networked control systems. By means of appropriate Lyapunov functions, the closed-loop stability is ensured under the proposed triggering rule. Moreover, the enforced dwell time between transmissions ensures that the accumulation of sampling times is prevented, which is crucial for the event-triggering condition to be implementable in practice. The required conditions to apply this technique are derived in terms of a linear matrix inequality. Numerical simulations on an isolated hybrid power system were implemented to demonstrate the efficiency of the proposed method. Comparative simulations with relevant techniques in the literature were carried out, which showed that the proposed approach can produce fewer transmission numbers over the network.

Finite-time economic model predictive control for optimal load dispatch and frequency regulation in interconnected power systems

This paper presents a finite-time economic model predictive control (MPC) algorithm that can be used for frequency regulation and optimal load dispatch in multi-area power systems. Economic MPC can be used in a power system to ensure frequency stability, real-time economic optimization, control of the system and optimal load dispatch from it. A generalized terminal penalty term was used, and the finite-time convergence of the system was guaranteed. The effectiveness of the proposed model predictive control algorithm was verified by simulating a power system, which had two areas connected by an AC tie line. The simulation results demonstrated the effectiveness of the algorithm.

Load frequency regulation of multi-area power systems with communication delay via cascaded improved ADRC

Load frequency regulation (LFR) is regarded as a significant method for the stable operation of modern power grids. However, the LFR is facing many challenges such as communication delays in the area control error, and external disturbances caused by renewable energy integrating into power grids. This paper presents a cascaded improved active disturbance rejection control (ADRC) to handle these control challenges, where the inner and outer controllers are the improved ADRC and regular ADRC, respectively. The advantages of the improved ADRC to reject external disturbances and system uncurtains are discussed by simulations. Then a practical parameter tuning method is provided to guide the selection of controller parameters. Finally, comparative simulations are implemented to illustrate the abilities of the proposed control scheme in different cases. The proposed control structure can handle the communication delay well and have a strong ability to reject the disturbances of uncertain consumed power and wind power. The simulation results imply that the cascaded improved ADRC may have broad application prospects for the LFR.

Multi-area automatic generation control of a renewable energy-based hybrid power systems using JAYA optimized model predictive control

This study uses JAYA optimized model predictive control to address a three-area load frequency regulation problem that actively involves stochastic renewable energy sources. Some physical constraints employed in the system model, such as the generation rate constraint, dead band, and time delay by governor-turbine, negatively impact the control performance of the classical MPC technique. As such, a more robust control scheme is required to get rid of this flaw. The JAYA algorithm is adopted to dynamically tune the parameters of MPC, which is a parameter-driven controller for optimal system performance. The performance indices such as the integral of square error (ISE), integral of time multiplied by square error (ITSE), integral of the absolute value of error (IAE), and integral of time multiplied by the absolute value of error (ITAE) have been used to compare the effectiveness of the proposed controller with the classical MPC with constant tuning parameters. The suggested controller is superior to the conventional MPC, as demonstrated by the results from the MATLAB/Simulink environment.

Proportional-Integral-Derivative Controller Based-Artificial Rabbits Algorithm for Load Frequency Control in Multi-Area Power Systems

A major problem in power systems is achieving a match between the load demand and generation demand, where security, dependability, and quality are critical factors that need to be provided to power producers. This paper proposes a proportional–integral–derivative (PID) controller that is optimally designed using a novel artificial rabbits algorithm (ARA) for load frequency control (LFC) in multi-area power systems (MAPSs) of two-area non-reheat thermal systems. The PID controller incorporates a filter with such a derivative coefficient to reduce the effects of the accompanied noise. In this regard, single objective function is assessed based on time-domain simulation to minimize the integral time-multiplied absolute error (ITAE). The proposed ARA adjusts the PID settings to their best potential considering three dissimilar test cases with different sets of disturbances, and the results from the designed PID controller based on the ARA are compared with various published techniques, including particle swarm optimization (PSO), differential evolution (DE), JAYA optimizer, and self-adaptive multi-population elitist (SAMPE) JAYA. The comparisons show that the PID controller’s design, which is based on the ARA, handles the load frequency regulation in MAPSs for the ITAE minimizations with significant effectiveness and success where the statistical analysis confirms its superiority. Considering the load change in area 1, the proposed ARA can acquire significant percentage improvements in the ITAE values of 1.949%, 3.455%, 2.077% and 1.949%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering the load change in area 2, the proposed ARA can acquire significant percentage improvements in the ITAE values of 7.587%, 8.038%, 3.322% and 2.066%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA. Considering simultaneous load changes in areas 1 and 2, the proposed ARA can acquire significant improvements in the ITAE values of 60.89%, 38.13%, 55.29% and 17.97%, respectively, with regard to PSO, DE, JAYA and SAMPE-JAYA.

Establishment of Performance Metrics for Batteries in Large‐Scale Energy Storage Systems from Perspective of Technique, Economics, Environment, and Safety

The battery is the core of large‐scale battery energy storage systems (LBESS). It is important to develop high‐performance batteries that can meet the requirements of LBESS for different application scenarios. However, large gaps exist between studies and practical applications because there are no uniform metrics for evaluating the performance of batteries. Herein, based on the fundamental requirements of LBESS, this perspective establishes the performance metrics of batteries for scenarios of load leveling, frequency regulation, and reserve application, respectively. Performance metrics include the technical metrics (e.g., the energy density, cycling performance, rate performance), economic metrics (levelized cost of energy), environmental metrics (sustainability of the material, recyclability of batteries, etc.), and safety metrics. The relationship between the proposed metrics and the application of the LBESS are discussed to guide the future studies.

PSO-Based Model Predictive Control for Load Frequency Regulation with Wind Turbines

With the high penetration of wind turbines, many issues need to be addressed in relation to load frequency control (LFC) to ensure the stable operation of power grids. The particle swarm optimization-based model predictive control (PSO-MPC) approach is presented to address this issue in the context of LFC with the participation of wind turbines. The classical MPC model was modified to incorporate the particle swarm optimization algorithm for the power generation model to regulate the system frequency. In addition to addressing the unpredictability of wind turbine generation, the presented PSO-MPC strategy not only addresses the randomness of wind turbine generation, but also reduces the computation burden of traditional MPC. The simulation results validate the effectiveness and feasibility of the PSO-MPC approach as compared with other state-of-the-art strategies.

Frequency Regulation of Power Systems With a Wind Farm by Sliding-Mode-Based Design

Load frequency regulation is an essential auxiliary service used in dealing with the challenge of frequency stability in power systems that utilize an increasing proportion of wind power. We investigate a load frequency control method for multiarea interconnected power systems integrated with wind farms, aimed to eliminate the frequency deviation in each area and the tie-line power deviation between different areas. The method explores the derivative and integral terminal sliding mode control technology to solve the problem of load frequency regulation. Such technology employs the concept of relative degrees. However, the subsystems of wind-integrated interconnected power systems have different relative degrees, complicating the control design. This study develops the derivative and integral terminal sliding-mode-based controllers for these subsystems, realizing the load frequency regulation. Meanwhile, closed-loop stability is guaranteed with the theory of Lyapunov stability. Moreover, both a thermal power system and a wind power system are applied to provide frequency support in this study. Considering both constant and variable external disturbances, several numerical simulations were carried out in a two-area thermal power system with a wind farm. The results demonstrate the validity and feasibility of the developed method.