Frequency Control Of Power System Research Articles

A dynamics-constrained method for distributed frequency regulation in low-inertia power systems

An increasing number of power electronics-interfaced renewable energy sources are integrated into the power grids. Such power systems suffer from poor frequency dynamic behaviors due to the decreasing rotational inertia and governor damping, thus are vulnerable to disturbances. A widely investigated approach to improve the frequency dynamics of the system is to imitate the synchronous generator responses by power converter control. In this paper, a dynamics-constrained frequency control method is proposed to bridge the gap between frequency dynamics optimization and secondary frequency control in low-inertia power systems. An intraday optimization model is formulated for the optimal tuning of the converter control parameters with guaranteed frequency security, and a distributed model predictive controller is developed for recovering the frequency economically. Case studies demonstrate the practicability of the proposed dynamic constrained frequency regulation approach and further comparison illustrates the computational efficiency and scalability of the proposed distributed algorithm.

Dynamic memory event-triggered proportional-integral-based [formula omitted] load frequency control for multi-area wind power systems

Aiming at ensuring the stability of multi-area doubly fed induction generator (DFIG)-integrated wind power systems, a dynamic memory event-triggered H∞ load frequency control (LFC) scheme is presented in this article. Firstly, a unified LFC model of multi-area DFIG-integrated wind power systems is constructed considering the grid connection of the DFIG-based wind turbine. Secondly, a dynamic memory event-triggered scheme (DMETS) is proposed to reduce the utilization of communication bandwidth by introducing a dynamic threshold parameter and historical data. Furthermore, the designed multi-area LFC model is reconstructed as a time-delay system under DMETS and proportional-integral (PI) controller with transmission delays introduced by open communication network. Then, asymptotic stability criteria meeting the H∞ attenuation performance index and an H∞ controller design criterion are obtained according to a selected Lyapunov–Krasovskii functional. Finally, numerical examples are presented to demonstrate the availability of the designed control scheme and the advantages of the proposed DMETS in reducing communication burden.

An optimal frequency regulation in interconnected power system through differential evolution and firefly algorithm

Automatic generation control is extensively used to regulate power plants in a modern area of the power system network. In this paper, automatic generation and frequency control in interconnected power system is presented. A multisource such as thermal, hydro, and gas-based power plant is considered in this study, which is carried out by incorporating nonlinearity like generation rate constants, HVDC link, and the conventional PID controller design. Further, an optimal setting of the PID controller is performed by employing evolutionary, and metaheuristic algorithm-based approaches such as differential evolution and firefly algorithm, respectively. With these algorithms, the proposed model has been tested with their performance evaluation and comparison characteristics are discoursed. The robustness of the proposed controllers is assessed based on comparative analyses to regulate the interconnected power network’s frequency profile under different loading conditions. The stability analysis is performed using the Eigen and Nyquist plots to assess the proposed controllers’ efficacy. Besides, the frequency control study is summarized with comparative assessment through various performance indices such as settling time, peak overshoot and undershoots under different operating conditions. Finally, the proposed control scheme, in the interconnected power system, is validated through a real-time digital simulation platform, i.e., OPAL-RT 5142. The comparison of simulation and real-time results demonstrates the effectiveness of the FA-optimized PID controller in comparison with that of the DE-optimized PID controller.

Resilient Load Frequency Control of Power Systems to Compensate Random Time Delays and Time-Delay Attacks

Load frequency control (LFC) of modern power systems tends to employ open communication networks to transmit measurement/control signals, which makes the LFC scheme more vulnerable to random time delays and time delay attacks. In this paper, a resilient and active time-delay-compensation-based LFC scheme is proposed to compensate the random time delays and time delay attacks. At first, a state observer is employed to estimate the state of the LFC system. Then a networked predictive control method is used to predict the control signals of the system at the future moments. Next, an evaluation and compensation scheme for random time delays and time delay attacks is constructed in the actuator side of the LFC scheme based on the updating period of the actuator and the timestamp technique. Due to the stochastic characteristics of the random time delays or TDAs, the stability condition of the proposed scheme is developed with the aid of the mean square stability theory. Moreover, a dual-loop open communication is employed in the proposed scheme to improve the reliability and resilience. At last, simulation and experiment tests are undertaken to demonstrate the effectiveness of the proposed scheme.

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.

Load Frequency Control of One and Two Areas Power System Using Grasshopper Optimization Based Fractional Order PID Controller

In this paper, grasshopper optimization based fractional order PID controller for load frequency control of single and multi-area power system is presented. It is paramount to minimize large frequency deviation in power system control. Large frequency deviation occurs when the parameter values of the various generating units of the power system like generators, turbine and governors keeps changing due to numerous on/off witching in the load side. As such, for a more realistic study, nonlinearities and boiler dynamics has been introduced into the power system design and measures also put in place to mitigate large frequency deviation. This is because large frequency deviation can damage equipment at the generation level and the consumer devices at the distribution level. Anti-windup control will then be used to compensate the effects of the nonlinearities. The gain of the FOPID controller will be optimized using grasshopper optimization algorithm and objective function for minimization is the Integral Square Error (ISE). The Errors to be minimized are the summation of frequency deviation, tie-line power deviation and the area control errors. Simulations were carried out on the designed power system using MATLAB/Simulink and results obtained show a significant improvement in mitigating frequency deviation. However, the proposed method took a longer time to balance the generated power and load demand. This is because the proposed method considers boiler dynamics and nonlinearities in the power system designed and results were compared with other designed method for power system without nonlinearities.

Frequency control by the PV station in electric power systems with hydrogen energy storage

The rapid growth of renewable energy capacity, in particular photovoltaic systems, is creating challenges associated with changing the rate of transient processes in the power system. This is due to the approach PV systems are connected to the grid using power converters and the absence of a rotating mass in the PV power plant. One of the most pressing challenge is the participation of PV stations in the process of frequency control in power systems, including in emergency modes. Simultaneously with PV power plants, it is efficient to use energy storage systems, including hydrogen ones. This is due to the fact that it is possible to obtain hydrogen for such energy storage systems using excess energy from PV power plants. The article proposes to solve the problem of frequency regulation in the power system by using an algorithm that allows to control the frequency in the power system using a synthetic inertia block of PV station, including at different levels of insolation and temperature of PV panels. The robustness of the proposed algorithm allows it to be used at different levels of power generated by the PV station, as well as in emergency modes.

Optimal frequency control for wind power-integrated power system based on parameter identification

Frequency control based on wind power is required by the grid code for maintaining system inertia level as well as frequency stability. However, synchronous generator (SG)-emulated frequency control of wind power will result in a nadir and second dip of frequency trajectory after a disturbance, which may threaten system safety. Optimizing the frequency trajectory of system to improve the nadir and avoid second dip usually relies on grid-wide information. But the information is hardly perfectly acquired in advance for real power system since it may change anytime. To address these problems, an optimal frequency control method for wind power-integrated power system based on parameter identification is proposed. Frequency characteristics of wind power-integrated are firstly analyzed based on the common frequency theory. Then the control framework of proposed method to eliminate the nadir and second dip of frequency trajectory is introduced. To realize this framework, system equivalent parameters like inertia and damp coefficient are identified by the extended Kalman smoother algorithm after each disturbance. These parameters are furtherly utilized to shape the transfer function of wind power-integrated power system as a first-order system. Finally, the implementation steps of proposed method are presented. The effectiveness of proposed method is verified by case studies.

Effective participation of wind turbines in frequency control of a two-area power system using coot optimization

In this paper, load frequency control is performed for a two-area power system incorporating a high penetration of renewable energy sources. A droop controller for a type 3 wind turbine is used to extract the stored kinetic energy from the rotating masses during sudden load disturbances. An auxiliary storage controller is applied to achieve effective frequency response. The coot optimization algorithm (COA) is applied to allocate the optimum parameters of the fractional-order proportional integral derivative (FOPID), droop and auxiliary storage controllers. The fitness function is represented by the summation of integral square deviations in tie line power, and Areas 1 and 2 frequency errors. The robustness of the COA is proven by comparing the results with benchmarked optimizers including: atomic orbital search, honey badger algorithm, water cycle algorithm and particle swarm optimization. Performance assessment is confirmed in the following four scenarios: (i) optimization while including PID controllers; (ii) optimization while including FOPID controllers; (iii) validation of COA results under various load disturbances; and (iv) validation of the proposed controllers under varying weather conditions.

Load Frequency Control of Hydro-Hydro Power System using Fuzzy-PSO-PID with Application of UC and RFB

Load frequency control (LFC) plays a crucial rule in matching the power generation with variable power demand, and hence, maintains the system frequency and tie-line power to its esteemed value. Further, most of the countries are dependent on thermal power plants to meet the electrical energy requirement, and hence, LFC strategies are available for these types of power plants only. As the world is moving to generate electrical energy via cleaner sources to reduce harmful environmental pollutants, and hence, hydro power are one of the well-developed and cleanest sources of electrical energy. Subsequently, this article shows up a novel LFC design for hydro-hydro system based on joint endeavors of fuzzy logic with PID viably optimized through particle swarm optimization (PSO) coming into a new and robust Fuzzy-PSO-PID for LFC. At to beginning with, the result of Fuzzy-PSO-PID is evaluated for step load alteration, and the outcomes of Fuzzy-PSO-PID are matched with recently published outcomes of LFC with regards to values of PID, error minimization, and graphical results. In any case, still, there may be a scope of LFC upgrade in Fuzzy-PSO-PID due to higher responding time of turbines used in hydro-hydro plants and subsequently, the combinations of storage devices such as ultra-capacitor (UC) in each zone and UC with redox flow battery combination are used to improve the LFC and the application outcomes are analyzed again and uncover considering the step load alteration, non-linearity, load pattern, and parametric modification to see the benefits of the proposed work for hydro-hydro LFC.

Quasi-Oppositional African Vultures Optimization-Based PIλDn Plus PIλ Controller for Frequency Control of an Interlinked Hybrid Power System

One of the challenging tasks in a hybrid power system is to provide an effective control strategy to attenuate frequency and power deviations that occur due to the intermittency of renewable energy resources (RERs) and load demand. To address this issue, we present a novel proportional fractional-order integral derivative filter plus proportional fractional-order integral (PIλDn plus PIλ) controller, which is applied for the load frequency control (LFC) operation of an interlinked hybrid thermal power system. Using quasi-oppositional learning, we also propose the maiden quasi-oppositional African vultures optimization algorithm (QOAVOA), which is used to tune the proposed controller by minimizing the objective function. First, the capability of the proposed QOAVOA tuned PIλDn plus PIλ controller is examined with a two-area thermal power system under load uncertainty and the performance is compared with the results of various optimization-based classical/fuzzy controllers. The sensitivity study is also performed to validate the robustness of the proposed control approach for handling systems’ parametric uncertainties. The suggested LFC mechanism is further validated on a two-area-hybrid thermal power system (2A-hTPS) comprising RERs and distributed generation sources to authenticate the tuning ability of the proposed optimization approach. The dynamic performance of considered 2A-hTPS with the suggested control approach is examined considering various scenarios such as changing load demand, uncertain wind perturbation, and random multi-step load perturbations of the wind turbine generator. The comparative dynamic performance of the suggested controller reveals its superiority over other controllers in terms of transient and steady-state responses. Finally, a few non-linearities are introduced to the test system, and the simulation study with non-linearities validates the potency of the proposed controller.

Load frequency composite control for multi-region interconnected power systems

This paper investigates a composite controller for load frequency control (LFC) in multi-region interconnected power systems via sliding mode observer design. State observers (SOs) and disturbance observers (DOs) are implied for the LFC based on the load variations with communication delays and quantization output measurements. A nonlinear integral sliding surface combined with a composite controller is developed to optimize control performance. Moreover, a three-area power system model is used to demonstrate the effectiveness of the proposed scheme in the illustrative example, confirming that frequency deviations can be rejected despite delays, uncertainties, and quantization during transmission.

Leveraging blockchain technology for resilient and robust frequency control in a renewable-based hybrid power system with hydrogen and battery storage integration

This paper introduces the blockchain-assisted frequency regulation mechanism for achieving resiliency and robustness in a renewable-based hybrid power system (HPS) powered by multiple sources, including wind turbine generators (WTG), hydrogen fuel cells (FC) with aqua electrolyzer (AE), diesel engine generators (DEG) and battery energy storage systems (BESS). Frequency fluctuations are exacerbated by load uncertainties paired with energy-generating source uncertainties resulting from integrating multiple distributed energy resources (DERs) into the national electric power grid. In the event of energy generation loss, efficient load frequency control (LFC) techniques are considered essential for frequency restoration in DER-integrated HPS. In addition, active power regulation data in LFC need secure data storage mechanisms for ensuring cyber-resilience, a transparent cash payout mechanism for the stakeholders in LFC market operation, and related sustainability services. A novel resilient blockchain framework for realizing LFC is proposed for addressing the power resilience and the false-data injection issue. Moreover, a parameter sensitivity algorithm (PSA) constrained optimization methodology using the genetic algorithm (GA) withstands the energy generation loss and uncertainties. A consensus algorithm-based blockchain methodology acts as an enabler for strategic decision-making and secures the data flow to the market operator. The proposed framework-driven methodology is implemented using a Blockchain network developed in python for securing LFC transactions by developing a case study for HPS. Numerous simulation scenarios demonstrate the proposed methodology's effectiveness in establishing a synergistic operative framework for the resilient and robust operation of the hybrid energy system with multiple sources, even in the event of uncertainties.

Coordinated load frequency control of a smart hybrid power system using the DEMA-TD3 algorithm

In this paper, a novel deep reinforcement learning (DRL) algorithm is proposed for the coordination of controllers in a multi-source hybrid power system (HPS) for load frequency control (LFC). Decentralized controllers regulate the distributed sources with uncertainties in load and source power deviations to maintain the power system frequency. Coordination of decentralized controllers becomes challenging in the presence of dynamic disturbances. A novel LFC framework is introduced via a dynamic environment-based multi-agent twin delayed deep deterministic policy gradient (DEMA-TD3) algorithm to coordinate the decentralized controllers. Moreover, system stability is a concern in systems with renewable integration during islanding operations. To achieve successful islanding, a smart home inverter (SHI) based coordinated control is proposed. Virtual inertia (VI) is added to provide additional frequency support for robust control. Various simulation test scenarios are simulated to analyze the effectiveness of the proposed novel LFC architecture in different modes of operation.

Optimal design of PI/PD dual mode controller based on quasi opposition based learning for power system frequency control

Due to uncertain load deviations in the unified power system, it becomes more challenging to uphold the system frequency at a constant desired value. The secondary controller has a significant role in damping the oscillations of frequency and interline power at a faster rate. Here a novel PI/PD dual mode controller (DMC) is suggested to regulate the frequency of unified system comprising renewable and conventional sources of power generation. The various sources of generation instigated in the dual-area system are hydro, diesel, thermal, PV, wind and battery energy storage system (BESS). The gains of the PI/PD DMC are suitably obtained by the novel application of quasi opposition based Sine Cosine Algorithm (QOBSCA) algorithm. The dynamic outcomes of the model are examined with the application of abrupt load deviation of 0.02 per unit in control area 2. During this deviated loading condition QOBSCA algorithm is utilized to achieve the appropriate gains of PID and PI/PD dual mode controller in view of a time based fitness function denoted as integral time absolute error (ITAE). Considering few response evaluative indices like overshoots, settling time and undershoots the ascendency of the projected PI/PD dual mode controller is recognized over popular PID controller during the abnormal loading condition. Additionally, the efficacy of the mentioned PI/PD dual mode controller is verified by applying arbitrary load pattern in control area 2. The robustness of the mentioned control schemes is also evidenced by confirming the stable response during system parameter deviations.

Optimal False Data Injection Attacks Against Power System Frequency Stability

The automatic generation control (AGC) is one of the core control systems in power grids that regulate frequency within the permissible range. However, its dependence on communication makes it highly vulnerable to cyber-attacks. An arbitrary false data injection attack (FDIA) on AGC frequency and tie-line flow measurements will likely be detectable by bad data detection methods; however, if an attack can be launched optimally, it often becomes stealthy. In this regard, we develop a framework of optimal FDIAs (OFDIAs) to demonstrate the feasibility of such attacks in the power system frequency control loop. We propose a linearized formulation of discretized power systems’ dynamics in an optimization framework to model OFDIAs that compromise the AGC system by corrupting tie-line flow and generators’ frequency measurements. Using the proposed formal modeling, we study the effects of two types of FDIAs, continuous and time-limited, on the frequency behavior in power grids. The results demonstrate that continuous OFDIAs can lead to severe consequences on a power grid’s performance, such as frequency instability. In contrast, the time-limited FDIAs can cause the frequency to fluctuate beyond the acceptable range, which may lead to the triggering of the frequency-based protection relays.

A Survey on Load Frequency Control of Multi-Area Power Systems: Recent Challenges and Strategies

Load frequency control (LFC) is well known for balancing the load demand and frequency for a multi-area power system. Studies have proven that LFC can improve the global performance of multi-area power systems. In recent years, the increasing proportion of renewable energy, integration of EVs, and cyber-attacks have become the main challenges in LFC power systems. Different strategies have been applied in the literature for LFC power systems and the possible impacts of renewable energy, EVs, and cyber-attacks. This survey paper is devoted to the research on directions in LFC multi-area power systems. The mathematical model of recent challenges in LFC multi-area power systems is summarized and the similarities and differences of these challenges are analyzed. The uncertainty of renewable energy is a frequently noted issue in LFC power systems; however, the uncertainty that exists in controller design is often ignored. In this survey, we analyze methods for treating the uncertainty of renewable energy and controller. This survey paper introduces the most recent research on LFC and acquaints anyone interested in its development, such that the most effective strategies can be developed by the researchers.

PSOα: A Fragmented Swarm Optimisation for Improved Load Frequency Control of a Hybrid Power System Using FOPID

Particle swarm optimisation (PSO) is one of the widely adopted meta-heuristic methods for solving real-life problems. Its practical utility can be further enhanced by improving its performance. In order to acheive this, academics have presented several variants of the original PSO over the past few years, including the quantum PSO (QPSO), bare-bones PSO (BB-PSO), hybrid PSO, fuzzy PSO, etc. In this paper, the performance of PSO is improved by proposing a fragmented swarm optimisation approach known as the PSOα. The PSOα is tested and compared with PSOs over 14 different benchmarking cost functions to validate its efficacy. The analysis is also carried out to see the impact of α on its performance. It is observed that the average value of the cost function over 50 simulations obtained using the fragmented swarm approach is lower than that obtained using the standard PSO in 12 out of 14 benchmark functions. Similarly, the fragmented approach outperforms the standard PSO in 13 out of 14 benchmark functions when compared with the best fitness value achieved out of 50 simulations. Finally, the proposed approach is applied to solve the well-known real-life optimisation problem of load frequency control (LFC) in power systems. A test system comprising both renewable and traditional power sources is considered to evaluate the efficacy of the proposed technique. A fractional order proportional-integral-differential (FOPID) controller is used, whose parameters are optimised using the proposed PSO for achieving the LFC. The proposed fragmentation approach can be applied with other optimisation techniques to improve their performance.

Load Frequency Control and Automatic Voltage Regulation in Four-Area Interconnected Power Systems Using a Gradient-Based Optimizer

Existing interconnected power systems (IPSs) are being overloaded by the expansion of the industrial and residential sectors together with the incorporation of renewable energy sources, which cause serious fluctuations in frequency, voltage, and tie-line power. The automatic voltage regulation (AVR) and load frequency control (LFC) loops provide high quality power to all consumers with nominal frequency, voltage, and tie-line power deviation, ensuring the stability and security of IPS in these conditions. In this paper, a proportional integral derivative (PID) controller is investigated for the effective control of a four-area IPS. Each IPS area has five generating units including gas, thermal reheat, hydro, and two renewable energy sources, namely wind and solar photovoltaic plants. The PID controller was tuned by a meta-heuristic optimization algorithm known as a gradient-based optimizer (GBO). The integral of time multiplied by squared value of error (ITSE) was utilized as an error criterion for the evaluation of the fitness function. The voltage, frequency, and tie-line power responses of GBO-PID were evaluated and compared with integral–proportional derivative (GBO-I-PD), tilt integral derivative (GBO-TID), and integral–proportional (GBO-I-P) controllers with 5% step load perturbation (SLP) provided in each of the four areas. Comprehensive comparisons between GBO-PID and other control methodologies revealed that the proposed GBO-PID controller provides superior voltage, frequency, and tie-line power responses in each area. The reliability and efficacy of GBO-PID methodology were further validated with variations in the turbine time constant and speed regulation over a range of  ± 25%. It is evident from the outcomes of the sensitivity analysis that the proposed GBO-PID control methodology is very reliable and can successfully stabilize the deviations in terminal voltage, load frequency, and tie-line power with a shorter settling time in a four-area IPS.

Regional Load Frequency Control of BP-PI Wind Power Generation Based on Particle Swarm Optimization

The large-scale integration of wind turbines (WTs) in renewable power generation induces power oscillations, leading to frequency aberration due to power unbalance. Hence, in this paper, a secondary frequency control strategy called load frequency control (LFC) for power systems with wind turbine participation is proposed. Specifically, a backpropagation (BP)-trained neural network-based PI control approach is adopted to optimize the conventional PI controller to achieve better adaptiveness. The proposed controller was developed to realize the timely adjustment of PI parameters during unforeseen changes in system operation, to ensure the mutual coordination among wind turbine control circuits. In the meantime, the improved particle swarm optimization (IPSO) algorithm is utilized to adjust the initial neuron weights of the neural network, which can effectively improve the convergence of optimization. The simulation results demonstrate that the proposed IPSO-BP-PI controller performed evidently better than the conventional PI controller in the case of random load disturbance, with a significant reduction to near 10 s in regulation time and a final stable error of less than 10−3 for load frequency. Additionally, compared with the conventional PI controller counterpart, the frequency adjustment rate of the IPSO-BP-PI controller is significantly improved. Furthermore, it achieves higher control accuracy and robustness, demonstrating better integration of wind energy into traditional power systems.