Load Frequency Control Controller Research Articles

Novel tuning rules for IMC-high-order PID load frequency controller of power systems

The problem of load frequency control (LFC) for power grids has received widespread attention in power industrial control. Ensuring the stability of the frequency is the primary concern that constrains the development of the electric power system. This paper presents a novel internal model control (IMC)- proportional-integral-derivative (PID) controller design method for LFC control systems, centered on pole-zero conversion. This method aims to simplify the system model's complexity and improve the system's performance. The IMC controller is approximated as a PID series compensator (high-order PID), with exact tuning formulas provided for various scenarios. These include single-area without or with reheat turbines, hydro turbines with transient droop compensators, wind turbines, and gas turbines. The tuning formulas are characterized by a single parameter, determined under robustness constraints and integrated time absolute error (ITAE). The proposed tuning method considers the generator rate constraint (GRC) and applies to multi-area single-source and multi-area multi-source power systems. The results of the simulation confirm that the controller tuning method presented in this paper has significant advantages over the existing design methods concerning robustness and anti-interference performance.

Optimum Fractional Tilt Based Cascaded Frequency Stabilization with MLC Algorithm for Multi-Microgrid Assimilating Electric Vehicles

An important issue in interconnected microgrids (MGs) is the realization of balance between the generation side and the demand side. Imbalanced generation and load demands lead to security, power quality, and reliability issues. The load frequency control (LFC) is accountable for regulating MG frequency against generation/load disturbances. This paper proposed an optimized fractional order (FO) LFC scheme with cascaded outer and inner control loops. The proposed controller is based on a cascaded one plus tilt derivative (1+TD) in the outer loop and an FO tilt integrator-derivative with a filter (FOTIDF) in the inner loop, forming the cascaded (1+TD/FOTIDF) controller. The proposed 1+TD/FOTIDF achieves better disturbance rejection compared with traditional LFC methods. The proposed 1+TD/FOTIDF scheme is optimally designed using a modified version of the liver cancer optimization algorithm (MLCA). In this paper, a new modified liver cancer optimization algorithm (MLCA) is proposed to overcome the shortcomings of the standard Liver cancer optimization algorithm (LCA), which contains the early convergence to local optima and the debility of its exploration process. The proposed MLCA is based on three improvement mechanisms, including chaotic mutation (CM), quasi-oppositional based learning (QOBL), and the fitness distance balance (FDB). The proposed MLCA method simultaneously adjusts and selects the best 1+TD/FOTIDF parameters to achieve the best control performance of MGs. Obtained results are compared to other designed FOTID, TI/FOTID, and TD/FOTID controllers. Moreover, the contribution of electric vehicles and the high penetration of renewables are considered with power system parameter uncertainty to test the stability of the proposed 1+TD/FOTIDF LFC technique. The obtained results under different possible load/generation disturbance scenarios confirm a superior response and improved performance of the proposed 1+TD/FOTIDF and the proposed MLCA-based optimized LFC controller.

A Reinforcement Learning Based Coordinated but Differentiated Load Frequency Control Method With Heterogeneous Frequency Regulation Resources

As the diversification of frequency regulation resources, it is increasingly important to find a load frequency control (LFC) scheme that can effectively coordinate different resources. This paper proposes a coordinated but differentiated LFC control method that utilizes heterogeneous frequency regulation resources with their characteristics fully considered. The optimal LFC models for the single area and multi-area power systems are formulated as partially observable Markov decision process model (POMDP) and partially observable Markov game (POMG) considering the accessibility of system information. A deep reinforcement learning method called recurrent proximal policy optimization is leveraged to solve the POMDP and POMG models. Simulations based on historical data demonstrate that the proposed control scheme can better coordinate different resources and achieve superior control performance.

Optimization of Load Frequency Control Gain Parameters for Stochastic Microgrid Power System

Interconnected multi-area microgrids are vital for the future of sustainable and reliable power systems. Effective load frequency control (LFC) is indispensable for ensuring their stable operation. This paper introduces a PID-based LFC system tailored for a stochastic microgrid with diverse power sources, including solar, wind, diesel engine generators, and electrical batteries. The gain parameters of the proposed microgrid PID LFC controller are optimized using genetic algorithms (GA), teaching learning-based optimization (TLBO), and cohort intelligence algorithms. Integral time-multiplied absolute error (ITAE) and integral time-squared error (ITSE) serve as the cost functions for all optimization algorithms. The study evaluated the performance of these optimized microgrid PID LFC configurations under random step load disruptions. Our primary findings reveal that the cohort intelligence-optimized PID LFC controller excels in minimizing computation time (upto 76% and 94% lesser than GA and TLBO respectively) and exhibits superior robust response characteristics. Moreover, the cohort intelligence algorithm requires fewer iterations (upto 66% and 90% lesser than GA and TLBO respectively) and enhances power supply quality within the multi-power microgrid electrical framework, specifically in terms of effective load frequency control.

Optimizing Artificial Neural Networks for Multi Area Network : A Metaheuristic Perspective

Regression, data classification and function approximation are among the most common applications that make use of machine learning models. However, due to the vast range of applications for machine learning, comprehensive understanding of how to choose a model based on machine learning, as well as how to choose its structure, training method, and performance analysis criterion, is frequently lacking. This key issue is addressed using a well-known load frequency control (LFC) problem which is instrumental in maintaining balance between power generation and load demand. The study relates to developing and contrasting the performance of the Meta heuristic optimization based approach to the artificial neural network (ANN) based machine learning topology for the LFC control against the optimized ANN controller. The selected performance indices are peak time and settling time of the frequency deviation. It is observed that Linear Neural Network (LNN) outperforms Feedforward Neural Network (FNN) and Recurrent Neural Network (RNN). The performance is enhanced when its parameters are optimized by the metaheuristic algorithms.

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.

Optimal self-tuning fractional order fuzzy load frequency control considering sustainable energy sources and electric vehicle

Considering uncertainties associated with sustainable energy sources in modern power systems, one of the main obstacles for load frequency control (LFC) is that various publications regard the parameters of FOPID controllers as fixed after optimisation. To tackle this challenge, an adaptive control structure may be a viable option for LFC. In this paper, in a two-zone hybrid power system that includes a thermal power plant, green energy sources, fuel cells, and plug-in electric vehicles, an optimal self-tuning fractional order fuzzy (OSTFOF) controller for LFC is proposed. Pathfinder algorithm (PFA) has been utilised for optimal tuning of parameters of the OSTFOF controller, including the upper bands of output membership functions, the input scaling factors, and the order of integral and derivative operators. In the proposed controller, the values of proportional, integral, and derivative gains are adaptively obtained. In the thermal power plant, non-linear factors, such as governor dead band (GDB) and generation rate constraint (GRC), are considered. Four different scenarios have been considered to evaluate the performance of the proposed OSTFOF controller, and results have been compared with PI, PID, and FOPID controllers. The simulations’ results indicate the excellent performance indices of the suggested OSTFOF controller compared to other controllers.

TFODn‐FOPI multi‐stage controller design to maintain an islanded microgrid load‐frequency balance considering responsive loads support

AbstractModern electrical power system design will increase renewable energy sources (RES) dominance. Rotating masses, the main source of inertia in power systems, have been greatly decreased in renewable energy producing systems. Thus, load–frequency equilibrium is an important indicator of these systems' performance and safety. This work introduces a fractional‐order (FO) operator‐based cascade control structure for islanded microgrid (μG) load‐frequency control (LFC). The structure utilizes a tilt–FO derivative with filter (TFODn) in the first level to reduce noise. The second level implements the proportional integral (PI) controller's FO form (FOPI), making it a tilt–FO derivative with a filter cascaded to the FOPI controller (TOFDn‐FOPI). The optimal controller parameters for quick dynamic responses with low frequency fluctuation are determined using a unique cost function and prairie dog optimization (PDO) algorithm. The LFC control loops have frequency deviation‐based responsive loads (RL). Control signal delays, RES output changes, parameter uncertainties, and cyber vandalism are examined. Laboratory‐scale tests also assessed the controller's practicality. The proposed controller outperforms standard and FO type proportional integral derivative (PID) and PD‐PI controllers. The TFODn‐FOPI controller is suitable for complicated closed‐loop systems like islanded μGs due to its faster error clearing, reduced RL capacity, and superior time and frequency domain indicators.

An innovative LFC scheme for multi-area microgrid incorporating with hydrogen-based demand response mechanism

Due to the unpredictability of renewable energy sources (RESs) in Microgrids (MG), the Load Frequency Control (LFC) mechanism is crucial for frequency management of these systems. Frequency control in MGs can be made more cost-effective with the help of demand-side management by temporarily boosting or decreasing power usage, in addition to Automatic Generation Control (AGC) techniques. Hydrogen can provide considerable benefits to MGs since it can be used in two ways: as a fuel cell for electricity generation or as an electrolyzer for power consumption, satisfying the needs of AGC and demand response (DR) simultaneously utilizing Hydrogen Energy Storage (HES) system. This research proposes an MG structure that integrates appropriate energy storage technologies, is able to provide both energy and waste management, and enables use of a variety of RES (including solar, wind, wastewater, agricultural, and residential wastes). With the newly announced Improved-Salp Swarm Algorithm (ISSA), this research aims to design a novel double-input interval type-2 fuzzy fractional order proportional-integral-derivative PI + TID (DIT2-FOPI + TID) cascade controller for LFC of multi-area MGs considering DR with a broad range of Distributed energy resources (DER). In all case studies, the proposed controller demonstrates outstanding effectiveness within the setting of LFC of MGs and, especially by employing DRM approaches, boosts frequency response performance by up to 65%. The results indicate that the dynamic frequency response of the MG is significantly enhanced by the addition of DRM capability with the HES system.

Multi‐objective salp swarm algorithm‐based fractional order fuzzy precompensated PDPI controller for frequency regulation of hybrid power system

AbstractSignificant number of renewable sources when integrated with distributed generations and energy storage components form a sustainable power system called hybrid power system (HPS). But the stochastic nature of the sources and continuous variation of load cause serious issues in the power systems like frequency instability. Our work proposes a novel approach for load frequency control (LFC) of the proposed system. For the work, both frequency deviation and control signal are used to give the input signal to energy storage components. A new fuzzy‐based controller called fractional order fuzzy precompensated PDPI (FO‐FPPDPI) controller for LFC of the mentioned HPS is suggested in this paper. The controller is optimized by a nature inspired multi‐objective salp swarm algorithm (MSSA). The comparison of the proposed approach with the classical PID controller and Fuzzy PID (FPID) controller is carried out by taking several system operating conditions. Robustness of the FO‐FPPDPI controller is also validated for system parameter variations and the superiority of the proposed control strategy over PID and FPID controllers is validated for all the situations. Finally, the control strategy is realized in OPAL‐RT to measure its fidelity with the simulation results.

Load Frequency Control Using Golden Eagle Optimization for Multi-Area Power System Connected Through AC/HVDC Transmission and Supported With Hybrid Energy Storage Devices

The reliability of a power system depends on its ability to handle fluctuations and varying load demands, as uncontrolled frequency deviations can lead to load-shedding and blackouts. Optimally tuned controllers are essential for Load Frequency Control (LFC) applications to efficiently stabilize the power system by minimizing frequency undershoots, overshoots, and settling time. This paper proposed the application of novel Golden Eagle Optimization (GEO) algorithm for the optimal tuning of the LFC controller, which has not been previously employed in any LFC applications. Moreover, this paper presents the first-ever implementation of a hybrid energy storage system consisting of Vanadium Redox Flow Battery (VRFB) and Super Magnetic Energy Storage System (SMES) coupled with AC/HVDC transmission lines in a multi-area power system. A GEO optimized Proportional-Integrative-Derivative (GEO-PID) robust controller is designed with the Integral Time Absolute Error (ITAE) objective function to enhance the power system’s stability. The proposed controller is tested on two and four areas power systems considering the sensitivity and nonlinearity of the power systems. A robustness test is also performed to verify the stability of the system under randomly chosen loading conditions. In comparison with particle swarm optimization, dragonfly algorithm, sine cosine algorithm, ant lion optimization, and whale optimization algorithm, the GEO-PID controller significantly reduced the settling time up to 80% for different area’s frequencies. Simulation results indicate that the proposed controller outperforms other recent optimization algorithms by effectively dampening the frequency and tie-line deviations with less settling times, as well as reduced frequency undershoots and overshoots.

Resilient Load Frequency Control of Multi-Area Power Systems Under DoS Attacks

Cyber security of modern power systems has become increasingly significant due to their open communication architecture and expanding network connectivity exposed to malicious cyber attacks. Resilient control represents an effective means to preserve the survivability of the power system under cyber attacks. In this paper, we address the resilient load frequency control (LFC) design for multi-area power systems under a new class of time-constrained denial-of-service (DoS) attacks. First, different from the widely-explored duration- and frequency-constrained DoS attack models, we consider a general time-constrained DoS attack model where only the attack durations are confined into some bounds, which represents less a priori knowledge of an attacker’s actions. Second, instead of using a traditional yet conservative time-invariant Lyapunov function (TILF), we develop an attack-parameter-dependent time-varying Lyapunov function (TVLF) approach to enable a resilient LFC design without jeopardizing the desired closed-loop system stability and performance. Furthermore, we provide a formal stability and performance analysis condition as well as a design criterion for the desired DoS-resilient output feedback LFC controller. We also show that the minimum allowable sleeping period and the maximum allowable active period of the attacked LFC system can be explicitly disclosed. Finally, we present two simulation case studies on a two-area LFC system and a three-area LFC system to demonstrate the effectiveness of the obtained results.

Optimal Ultra-Local Model Control Integrated with Load Frequency Control of Renewable Energy Sources Based Microgrids

Since renewable energy sources (RESs) have an intermittent nature, conventional secondary frequency control, i.e., load frequency control (LFC), cannot mitigate the effects of variations in system frequency. Thus, this paper proposes incorporating ultralocal model (ULM) control into LFC to enhance microgrid (µG) frequency stability. ULM controllers are regarded as model-free controllers that yield high rejection rates for disturbances caused by load/RES uncertainties. Typically, ULM parameters are set using trial-and-error methods, which makes it difficult to determine the optimal values that will provide the best system performance and stability. To address this issue, the African vultures optimization algorithm (AVOA) was applied to fine-tune the ULM parameters, thereby stabilizing the system frequency despite different disturbances. The proposed LFC controller was compared with the traditional secondary controller based on an integral controller to prove its superior performance. For several contingencies, the simulation results demonstrated that the proposed controller based on the optimal ULM coupled with LFC could significantly promote RESs into the µG.

A novel adaptive model predictive frequency control using unscented Kalman filter

This paper proposes a novel adaptive load frequency control (LFC) method based on model predictive control (MPC) using an unscented Kalman filter (UKF). The objective is to develop a robust LFC controller against the change in frequency characteristics of a power system. An adaptive MPC (AMPC) control is realized by updating the prediction model on-line to capture the frequency characteristics of a target system. A simplified prediction model is identified in the preliminary examination of the closed-loop performance of the target system, and then the system parameters are estimated in real-time circumstances. The estimated parameters include equivalent inertia of the target system. An interconnected three-area test system is used in the simulations to confirm the effectiveness of the proposed approach. The simulation results verify that the proposed MPC method can work successfully and efficiently handle the disturbances of the power system. The successful results also confirm that the proposed control scheme can be applied to a microgrid (MG) project, which is being promoted by the authors.

Retired electric vehicle battery to reduce the load frequency control oscillation in the micro grid system

The potential of a retired electric vehicle battery (REVB) is its capacity to provide backup power supply to the power system grid. This paper proposed energy storage system (ESS) based on REVB called retired battery energy storage system (retired BESS) to tackle the intermittent of renewable energy source such as wind turbine and dynamic load change. To examine the efficacy of the proposed technique, the load frequency control (LFC) of microgrid (MG) system is utilized in this study and the proposed technique is compared to conventional LFC controller, PI controllers, superconducting magnetic energy storage (SMES), and a new electric vehicle battery. The kind of retired BESS cell used in this study is Li-ion nickel manganese cobalt oxide (NMC) type with a state of charge as of 70%. The capacity of each cell for retired BESS is 38 Ah. From the simulation result, the use of retired BESS can reduce frequency oscillation, compress the settling time to reach steady state, and maintain the robustness of the MG system. A retired BESS has a minimum error performance index value compared to conventional LFC, proportional integral (PI) controller, and SMES.

Delay-Dependent Optimal Load Frequency Control for Sampling Systems With Demand Response

The continuous-time model is widely used to design load frequency control (LFC) schemes for power systems with delays and demand response (DR). In practice, however, the LFC system works in a sampled-data manner. The plant operates continuously while the control updates every few seconds. This paper focuses on the sampled-data LFC system with DR and the delay. Based on the linear quadratic regulator (LQR) theory, an optimal control scheme is designed to regulate the DR and generation units in a sampled-data manner. The LQR controller can be applied to the LFC system with constant and time-varying delays, regardless of DR. An observer is also designed to make the state-feedback control law feasible. The observer is efficient in that its gain is delay independent. Case studies on the one-area and multi-area LFC systems are conducted to verify the effectiveness of the proposed LFC controller. Simulation results also demonstrate the excellent stability and performance of the proposed LQR controller in the sampled-data LFC-DR system.

Deep reinforcement learning based active disturbance rejection load frequency control of multi-area interconnected power systems with renewable energy

The use of renewable energy to replace traditional coal, oil and natural gas for power generation is of great significance to the alleviation of environmental pollution and resource depletion. As the core of automatic generation control (AGC), load frequency control (LFC) is of great significance to the stable operation of the power system. However, adding renewable energy sources such as wind turbines and solar energy will bring unstable factors to the frequency response, while frequency fluctuations will reduce the power quality. To minimize the adverse effects caused by renewable energy, the linear active disturbance rejection control (LADRC) based on the soft actor-critic (SAC) algorithm is designed. Through the ability of LADRC to eliminate unknown disturbances and the decision-making ability of the SAC algorithm, an intelligent LFC controller is applied to a two-area interconnected power system with a solar thermal power plant (STPP) and a four-area interconnected power system with wind turbine generator (WTG), photovoltaic (PV) cells and hydro turbine. The simulation results are compared with that of the PID, MPC, and conventional LADRC controllers, highlighting the effectiveness of the proposed method.

Sampled‐data‐based H∞ load frequency control for power systems with wind power

AbstractLoad disturbances, the intermittent generation of wind power, and the uncertainty of sampling period will significantly affect the frequency stability of power system. This paper presents a sampled‐data‐based load frequency control (LFC) scheme for power systems with wind power (PSWPs), which aims to guarantee the stable operation of the systems under a sampling period as large as possible while retaining the desired robust performance. First, by considering the feature that power system plant operates in continuous‐time domain and LFC controller operates in discrete‐time domain, the closed‐loop PSWPs installed with a PI‐type LFC controller is modeled as sampled‐data control system with aperiodic samplings. Second, by utilizing an augmented two‐side looped Lyapunov functional and the linear matrix inequality (LMI), two sampling‐interval‐dependent stability criteria with less conservatism are derived for the studied systems, which guarantee that the presented LFC scheme operates in a large sampling period. Then, based on proposed stability condition, a sampled‐data‐based PI‐type LFC controller with the consideration of sampling period and performance index is obtained. Following this, an algorithm is given to obtain the gain of sampled‐data‐based PI‐type LFC controller and the admissible maximum sampling period while preserving the desired robust performance. Finally, case studies are carried out on the one‐area power system and three‐area PSWPs. The simulation results demonstrate the effectiveness of the presented LFC scheme, and robustness against load disturbances, wind power fluctuations and sampling period. It also shows that the designed LFC scheme can tolerate a larger sampling period while preserving the desired performance.

Seagull Optimization Algorithm–Based Fractional-Order Fuzzy Controller for LFC of Multi-Area Diverse Source System With Realistic Constraints

This study initiates the implementation of fractional-order (FO) fuzzy (F) PID (FOFPID) controller fine-tuned using a seagull optimization algorithm (SOA) for the study of load frequency control (LFC). Initially, the SOA-tuned FOFPID regulator is implemented on the widely utilized model of dual-area reheat-thermal system (DARTS), named test system-1 in this work for a perturbation of 10% step load (10% SLP) on area-1. Dynamical analysis of the DARTS system reveals the viability of the SOA-tuned FOFPID control scheme in regulating frequency deviations effectively compared to other control schemes covered in the literature. Later, the presented regulator is implemented on the multi-area diverse sources (MADS) system possessing realistic constraints in this study, termed test system-2. The sovereignty of the presented FOFPID controller is once again evidenced with controllers of PID/FOPID/FPID fine-tuned with the SOA approach. Moreover, the effect of considering practical realistic nonlinearity constraints such as communication time delays (CTDs) on MADS system performance is visualized and the necessity of its consideration is demonstrated. Furthermore, AC-DC lines are incorporated with the MADS system to enhance the performance under heavy-load disturbances and the robustness of the proposed regulatory mechanism is deliberated.

Optimal Design of TD-TI Controller for LFC Considering Renewables Penetration by an Improved Chaos Game Optimizer

This study presents an innovative strategy for load frequency control (LFC) using a combination structure of tilt-derivative and tilt-integral gains to form a TD-TI controller. Furthermore, a new improved optimization technique, namely the quantum chaos game optimizer (QCGO) is applied to tune the gains of the proposed combination TD-TI controller in two-area interconnected hybrid power systems, while the effectiveness of the proposed QCGO is validated via a comparison of its performance with the traditional CGO and other optimizers when considering 23 bench functions. Correspondingly, the effectiveness of the proposed controller is validated by comparing its performance with other controllers, such as the proportional-integral-derivative (PID) controller based on different optimizers, the tilt-integral-derivative (TID) controller based on a CGO algorithm, and the TID controller based on a QCGO algorithm, where the effectiveness of the proposed TD-TI controller based on the QCGO algorithm is ensured using different load patterns (i.e., step load perturbation (SLP), series SLP, and random load variation (RLV)). Furthermore, the challenges of renewable energy penetration and communication time delay are considered to test the robustness of the proposed controller in achieving more system stability. In addition, the integration of electric vehicles as dispersed energy storage units in both areas has been considered to test their effectiveness in achieving power grid stability. The simulation results elucidate that the proposed TD-TI controller based on the QCGO controller can achieve more system stability under the different aforementioned challenges.