Load Frequency Control Performance Research Articles

LFC performance advancement of two-area RES penetrated multi-source power system utilizing CES and a new CFOTID controller

This study addresses the challenges of load fluctuations and the assimilation of renewable energy sources (RES) causing undesirable frequency deviations. To avoid widespread deviations in frequency, an intelligent, competent and robust load frequency control (LFC) scheme and energy storage system are obligatory for disturbance rejection in RES penetrated realistic interconnected multi-source power system (IMSPS). Hence, a new cascade fractional order tilt integral derivative (CFOTID) controller is suggested with capacitive energy storage (CES) for a pragmatic RES based thermal-hydro-gas IMSPS having governor dead-band and generation rate constraint nonlinearities. The salp swarm algorithm (SSA) is utilized successfully for fine-tuning of the controller parameters. Scrutiny of the outcome results reveals the sovereignty of the CFOTID controller over several existing well proven controllers and SSA-based FOTID. It is observed that power generation due to CES further advance the system results in terms of minimized overshoot, undershoot, and settling time. Lastly, a sensitivity analysis is conducted to confirm the robustness of the controller.

Dynamic Performance of Load Frequency Control of Three Area System Using FOPID Controller with Transit Search Optimization

Dynamic Performance of Load Frequency Control of Three Area System Using FOPID Controller with Transit Search Optimization

Improving load frequency control performance in interconnected power systems with a new optimal high degree of freedom cascaded FOTPID-TIDF controller

Improving load frequency control performance in interconnected power systems with a new optimal high degree of freedom cascaded FOTPID-TIDF controller

Improved load frequency control performance by tuning parameters of PID controller and BESS using Bat algorithm

The oscillation of frequency can cause the generator to run out of sync in a power system. Therefore, load frequency control (LFC) is needed to reduce frequency oscillation and prevent out of sync operation. The LFC regulates the governor to balance the turbine speed with changes in the load of existing. In this paper, a proportional integral differential (PID) controller and a battery energy storage system (BESS) are added to an LFC to improve the frequency stability of a power system. The parameters of the PID controller and BESS are optimized using the Bat algorithm (BA) to attain a good coordination. The frequency performance analysis is done by introducing disturbance in the form of changes in load power. The simulation results show that the frequency deviation of the system with the PID controller and BESS, has a faster settling time and smaller overshoot value. The system performs better than those with only the PID controller or the BESS. In conclusion, the BA algorithm can be used to find optimal parameter values of the PID controller and BESS for a synchronized coordination of an LFC.

Age-of-information-aware PI controller for load frequency control

Open communication system in modern power systems brings concern about information staleness which may cause power system frequency instability. The information staleness is often characterized by communication delay. However, communication delay is a packet-centered metric and cannot reflect the requirement of information freshness for load frequency control (LFC). This paper introduces the age of information (AoI), which is more comprehensive and informative than the conventional communication delay modeling method. An LFC controller and communication are integrated into the design for LFC performance improvement. An AoI-aware LFC model is formulated first, and considering each allowable update period of the smart sensor, different AoI-aware PI controllers are then designed according to the exponential decay rate. The right AoI-aware controller and update period are selected according to the degree of frequency fluctuation of the power system. Case studies are carried out on one-area and two-area power systems. The results show the superior performance of the AoI-aware controllers in comparison to the delay-dependent controllers.

A Novel PSO-Based Modified SMC for Designing Robust Load-Frequency Control Strategies

In an electric power grid, Load-Frequency Control (LFC) plays a crucial role as it aims to maintain the system frequency at a nominal value, 50 or 60 Hz, by minimizing the effects of load changes. However, a modern power system is currently characterized by a huge number of nonlinearities and uncertainties, making control methodologies much more challenging. Among them, the nonlinear features of Governor Dead-Band (GDB) and Generation Rate Constraint (GRC) strongly affect the accuracy and performance of LFC applied to a power network. This study focused on designing an applicable and efficient LFC by proposing a novel Sliding-Mode Control (SMC) scheme. The traditional SMC can successfully solve several nonlinear control problems, and in case of having a reasonable adjustment, it is completely suitable to design the LFC strategy. The modified SMC, integrated with an effective optimization technique, i.e., Particle Swarm Optimization (PSO), can dramatically improve the performance of LFC. This paper presents numerical simulation results implemented in MATLAB/Simulink to demonstrate the feasibility and effectiveness of the proposed control strategy.

Event-Triggered Load Frequency Control Based on Age-of-Information

This paper investigates the role of age of information (AoI) in the load frequency control (LFC) of power systems. Smart grids employ an open information update system, which introduces pressing issues such as management of information freshness and limitation of communication bandwidth. Communication delay, though widely used to characterize the information packets, is not able to keep up the information freshness and often becomes the inherent source of information staleness. In this paper, AoI is used in the design of an event-triggered communication scheduler to manage the information updating process and alleviate the effect of limited communication bandwidth. AoI is also used in the design of an LQR (linear quadratic regulator) controller for the improvement of LFC performance. Case studies are presented to demonstrate that the proposed LFC-AoI model and the event-triggered LQR controller offer better LFC performance than the existing LFC methods.

Robust networked power system load frequency control against hybrid cyber attack

AbstractModern network and communication technologies are essential for the implementation and operation of load frequency control (LFC) systems. The measurements of crucial LFC system parameters will be compromised by attackers, rendering data received by the defence inaccurate and causing frequency fluctuations or even system collapse. To detect the potential attack on measured data and keep the LFC performance, an adaptive event‐triggered scheme with fractional order global sliding mode control scheme is proposed in this paper. Furthermore, Markov theory is employed for the modelling process with energy storage to present a multi‐area LFC power system considering renewable energy and hybrid cyber attacks. Stability and stabilisation criteria are built by employing improved Lyapunov stability theory and second‐order Bessel‐Legendre inequality. Finally, a two‐area LFC system under hybrid cyber attacks and a modified IEEE 39‐bus New England test power system with 3 wind farms are simulated to explore the efficacy of the proposed method.

Fractional order [formula omitted] controller for microgrid power system using cohort intelligence optimization

Microgrids are the future of energy management systems. Effective deployment and integration of microgrids can provide energy independence to many communities. The effectiveness of a microgrid primarily depends on its stability and reliability. Fractional regulators offer promising solutions to ensure stable microgrid performances. This article proposes a Fractional order Proportional–Integral–Derivative (FOPID) based secondary Load Frequency Control (LFC) for single area and dual-area AC microgrid systems. Cohort Intelligence (CI) based optimization was employed to tune the five major gain values of fractional PID regulators. The performance of the proposed CI tuned FOPID regulator was compared with that of the traditional PID controller tuned by Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on the same single and dual control area microgrid systems. Four different cases of AC microgrid simulations were explored for LFC performance assessments and effective comparisons among different controllers and tuning algorithms. The first case considered load changes in single area AC microgrid with and without distributed energy system. The second case considered load variations in the two area AC microgrid system. The third and fourth cases included variations in random step load and system parameters respectively. Primary results affirm the superiority of the CI tuned FOPID regulator over its GA and PSO tuned PID counterparts in terms of its better steady-state precision, expressed in terms of the Integral Time Absolute Error (ITAE) selected as the objective function.

Load Frequency Control Assessment of a PSO-PID Controller for a Standalone Multi-Source Power System

The performance of load frequency control (LFC) for isolated multiple sources of electric power-generating units with a proportional integral derivative (PID) controller is presented. A thermal, hydro, and gas power-generating unit are integrated into the studied system. The PID controller is proposed as a subordinate controller to stabilize system performance when there is a sudden demand on the power system. The particle swarm optimization (PSO) algorithm is used to obtain optimal gain values of the proposed PID controller. Various cost functions, mainly integral time absolute error (ITAE), integral absolute error (IAE), integral squared error (ISE), and integral time squared error (ITSE) were used to optimize controller gain parameters. Furthermore, the enhancement of the PSO technique is proven by the performance comparison of conventional, differential evolution (DE) algorithm- and genetic algorithm (GA)-based PID controllers for the same system. The results show the PSO-PID controller delivers a faster settled response and the percentage improvement of the proposed technique over the conventional method is 79%, over GA is 55%, and over DE is 24% in an emergency in a power system.

CPSOGSA Optimization Algorithm Driven Cascaded 3DOF-FOPID-FOPI Controller for Load Frequency Control of DFIG-Containing Interconnected Power System

This paper proposes a new cascaded fractional-order controller (CC-FOC) to solve the load frequency control (LFC) problem of an interconnected power system. The CC-FOC consists of a three-degree-of-freedom fractional-order proportional-integral-differential (3DOF-FOPID) controller and a fractional-order proportional-integral (FOPI) controller. Each area of the two-area interconnected power system in this study consists of a thermal unit, a hydro unit, a diesel unit, and a doubly-fed induction generator (DFIG). The enhanced particle swarm optimization (PSO) and gravitational search algorithm (GSA) under the chaotic map optimization (CPSOGSA) technique are used to optimize the controller gains and parameters to enhance the load frequency control performance of the cascade controller. Moreover, simulation experiments are conducted for the interconnected power system under load perturbation and random wind speed fluctuations. The simulation results demonstrate that the proposed cascaded fractional-order controller outperforms the traditional proportional-integral-differential (PID) controller and three other fractional-order controllers in terms of LFC performance. The suggested cascade controller displays strong dynamic control performance and the resilience of the cascade fractional-order controller by adjusting the load disturbance and analyzing the system characteristics.

Utilizing diverse mix of energy storage for LFC performance enhancement of a microgrid: A novel MPC approach

Integration of highly fluctuating renewable energy source (RES)-based distributed generation units (DGUs) and a low inertia of an islanded microgrid (MG), resulting from absence of direct coupling between the various DGUs and the MG, can stimulate severe frequency deviation problems. In such a scenario implementation of the energy storage units (ESUs) becomes imperative. Consequently, this paper examines the impact of diverse mix of two widely employed ESUs namely electric vehicle (EV) battery and superconducting magnetic energy storage (SMES) on the dynamic responses of an islanded MG. A novel discrete-time Laguerre functions-based intelligent model predictive control (LiMPC) approach is administered as a secondary controller. Realization of the Laguerre functions-based MPC approach, as compared to the classical MPC approach, reduces the number of parameters to be optimized and simultaneously downsizes the computational time of the control vector trajectory. Simulation results clearly validate the potency of the energy storage-based LiMPC approach, when both the ESUs are engaged, in improving the dynamic responses of the MG and simultaneously satisfying the load frequency control (LFC) requirements. A maximum percentage improvement of 41.91% in peak overshoot and 51.98% in peak undershoot of the dynamic responses, and 94.93% in the performance index value is observed. Furthermore, the superiority of the suggested control approach over a few widely used control approaches is shown. Finally, the robustness to parametric modifications of the proposed LiMPC approach is verified.

Load frequency control of multi area power system network with soft computing algorithm based controller and coordinated strategy of TCSC and battery energy storage

This work proposes the possible implementation of a water cycle algorithm (WCA) optimized controller of tilt-integral derivative plus filter (TIDF) for the load frequency control (LFC) of a multi-area multi-fuel (MAMF) interconnected power system network (IPSN). The MAMF network has two areas, and the dynamic analysis of the IPSN is assessed pertaining to the step load perturbation (SLP) of 10% in area 1. However, the performance efficacy of the TIDF is demonstrated and validated with other recent control techniques reported in the literature. The IPSN is considered with the communication time delays (CTDs) as the realistic constraint, and their subsequent impact on the LFC performance is demonstrated and vindicated. Further, the IPSN model of the MAMF system is enacted with the battery energy storage (BES) in areas 1 and 2, and the thyristor-controlled series compensator (TCSC) with the tie-line as the territory regulator. Simulation results showcased that there will be an improvement in the LFC of MAMF system performance with the employment of a territory control scheme.

An overview of load frequency control for grid using various control techniques

Nowadays Electrical power generation using renewable energy systems brings more novel ideas to satisfy the power demand worldwide. Microgrid is one of the ideas that ensures stability and reliability of the power system. Due to seasonal or climatic effect, the power generated by the renewable energy sources has issue in providing a constant power output and hence produces the power imbalance between the source to load. Furthermore, the system frequency cannot be kept constant in all cases. In this case, an electric vehicle (EV) can serve as a power generation source to support the power grid and meet the Load Frequency Control requirement (LFC). Due to their fast-regulating characteristics, electric vehicles provide frequency regulation services. Electric vehicles are used in both primary and secondary frequency management in power systems to quickly suppress frequency oscillations caused by changes in load. The control centre gathers and updates real-time Electric Vehicle data, such as information regarding state of charge and the state of each Electric Vehicle, in order to decide on load frequency regulation dispatch to Electric Vehicles. This paper compares and consolidate the performance of Load frequency control through Conventional plants and Renewable energy systems. The graphical abstract of power system grid is shown in Figure.1

Higher Order Degree of Freedom Controller for Load Frequency Control of Multi Area Interconnected Power System with Time Delays

In this paper, a seagull optimization algorithm (SOA) based 3-Degree-of-freedom (DOF) proportional-integral-derivative (3DOFPID) controller is suggested for load frequency control of multi-area interconnected power system (MAIPS). The considered MAIPS comprises of two areas with Thermal-Hydro-Nuclear generation units in each area. Analysis has been carried out by subjugating area-1 of MAIPS with a step load disturbance (SLD) of 10%. The sovereignty of presented SOA tuned 3DOFPID in regulating the stability of MAIPS is revealed upon comparing with the performances of 2DOFPID and conventional PID controllers. MIPS is analyzed dynamically without and with considering the nonlinear realistic constraint of communication time delays (CTDs) to demonstrate its impact on load frequency control performance. Simulation results disclosed that, MAIPS dynamical behavior is slightly more deviated up on considering CTDs and is justified.

Control Performance Standards-Oriented Event-Triggered Load Frequency Control for Power Systems Under Limited Communication Bandwidth

Load frequency control (LFC) of modern power systems tends to employ open communication networks to transmit measurement/control signals. Under a limited network bandwidth, the continuous and high-sampling-rate signal transmission will be prone to degradation of the LFC performance through network congestion. This brief proposes a decentralized control performance standards (CPSs)-oriented event-triggered (ET) LFC scheme for power systems under constrained communication bandwidth. The proposed scheme comprises the ET LFC scheme and the CPSs-oriented regulation scheme. In the CPSs-oriented regulation scheme, regulation rules are designed to adjust the threshold parameter of the ET LFC scheme based on the North American Electrical Reliability Council (NERC)’s CPS1 and CPS2. The rules generate a larger threshold parameter to lower the triggering frequency in order to reduce unnecessary transmission of measurement/control signals, while ensuring the frequency and tie-lie power of the power systems to meet the required CPS1 and CPS2 instead of the asymptotic stability requirement in the existing research. The reduced transmission of these signals lessens the communication burden. In addition, the decentralized control strategy is used to solve the problems of poor large scalability and computational dimension caused by the centralized control strategy. The effectiveness of the proposed scheme is evaluated on an IEEE 39-bus test system with renewable energy sources.

A multi-agent deep reinforcement learning-based “Octopus” cooperative load frequency control for an interconnected grid with various renewable units

With the aim of improving the frequency regulation performance of load frequency control (LFC) in an interconnected power system as well as reducing wastage of frequency regulation resources, an “octopus” cooperative load frequency control (OC-LFC) method is proposed. This method addresses the secondary frequency regulation coordination problem affecting different areas, as well as the coordination problem arising between the controller and power distributor within the same area. In addition, for this framework a tracking-explorer-based distributed multi-agent deep deterministic policy gradient (TED-MADDPG) algorithm is proposed. This algorithm imitates the edge-cloud cooperation framework, akin to an octopus. In the performance-based frequency regulation market, the controller and power distributor in each area are regarded as independent agents, which output the total generated power command and the generated power commands of different units. In the proposed method, centralized training and distributed implementation are employed so that a global optimal decision with higher robustness can be obtained. Furthermore, this algorithm also employs a tracking-explorer strategy which incorporate multiple learning techniques and in turn enhance the robustness of the proposed method. The effectiveness of the proposed method is demonstrated using a China Southern Grid (CSG) four-area load frequency control model.

Adaptive Neuro-Fuzzy Based Load Frequency Control in Presence of Energy Storage Devices

Energy storage technologies are utilized for improving the primary frequency control in complex electrical systems. In this paper, the modeling and simulation of a two-area power system is done to evaluate and compare the impact of three different energy storage applications on load frequency control performance. Capacitive energy storage (CES), battery energy storage (BES), and superconducting magnetic energy storage (SMES) are considered for the study. On the basis of peak overshoot and settling time, the performance of these energy storage devices is compared. The power system consists of thermal, wind, and solar resources. All nonlinearities are incorporated in the system model. Both conventional and artificial intelligence (AI) based controllers are tested for the case study. The simulations are carried out under multiple operating conditions and penetration levels of renewable sources. It is found that the primary frequency response of the test system is improved in the presence of the storage facilities and the neuro-fuzzy controller. The simulation results exhibit that the SMES displays the best performance indices. The prime contribution of the paper is to investigate and compare the response of the storage devices in a realistically modeled power system with renewable resources using both conventional and AI controllers.

Coordinated intelligent frequency control incorporating battery energy storage system, minimum variable contribution of demand response, and variable load damping coefficient in isolated power systems

Maintaining a generation-demand balance becomes more challenging nowadays due to the limited availability of traditional automatic generation control (AGC) and spinning reserves. In response to this concern, the purpose of the proposed paper is to bridge the gap by introducing battery energy storage system (BESS) control loop in the load frequency control (LFC) system. Furthermore, a minimum variable share of demand response (DR) is utilized as an additional mean to regulate the system frequency. The growing intermittency of controllable loads calls the awareness to incorporate frequency-sensitive loads by considering a variable load-damping coefficient in LFC problem. The presented control scheme demonstrates a stable power operation with the optimal sharing of BESS, dynamic DR, and the supplementary control loop. Moreover, because of the continuous and sudden variations in the demand, adaptive LFC controller is designed using fuzzy logic based on particle swarm optimization (PSO) tuning method. The proposed controller ensures a stable and robust frequency regulation of the system under load fluctuations. In this paper it is revealed that adding the BESS control loop is very effective in enhancing the performance of LFC as it can deliver fast power compensation. The obtained results prove the capability and effectiveness of the proposed method in an isolated power system.

Frequency stability of interconnected power systems using atom search optimization algorithm

This paper explores the dynamic controller design issue for load frequency control (LFC) of a practical interconnected power system model. In view of this, the three-degree-of-freedom proportional-integral-derivative (3DOF-PID) controller architecture and LFC performance analysis for the proposed atom search optimization (ASO) is presented. The consistency of the form and acceptability of the well-known PID controller’s responses ultimately enforces to use in this work. The proposed ASO algorithm efficiently combines search space discovery and exploitation that yield promising solutions at the termination condition. The test system investigated is a four-area model with each region consisting of identical thermal unit. The system is also connected in one area with an interline power flow controller. The simulation results presented showed the superiority of ASO based 3DOF-PID controller in terms of settling time, peak variance, and magnitude of oscillation.