Automatic Generation Control Control Research Articles

Renewable sources with virtual inertia penetration in multi area interconnected AGC using a modified MPC controller

The challenge of controlling frequency deviation becomes more difficult as the complexity of a power plant increases. The robustness of the controller has a major impact on the stability of a complex power system (CPS). Due to the hybridization of CPS basic Automatic Generation Control (AGC) controllers (PID, FOPID, and TID), they are insufficient to give optimal performance to a plant. This requires a robust controller. So, a modified MPC controller has been proposed and evaluated by comparing it with several existing controllers, which gives optimal performance in terms of overshoot, undershoot, and settling time and improves its performance approximately 45%. This research discusses a combined AGC model and control for a three-area CPS, where each producing area consists of a thermal plant, a Diesel power plant, and a Solar Thermal Power Plant (STPP). The results of the modified MPC are superior to those of the basic controller compared to several existing controllers. An improved version of Sea-horse Optimization (SHO) has been proposed to optimize the different controller settings. The superiority of the SHO is shown by a comparison with some well-known, current meta-heuristic methods. The higher penetration levels of renewable energy sources (RESs) reduced system inertia which further deteriorate frequency response in CPS. To overcome these challenges, virtual inertia (VI) has been implemented with MPC. VI is applied to improve the performance of the AGC of the interconnected CPS along with emphasizing the nature of intermittent RESs of PV and wind energy. The thorough study findings provide compelling evidence for the effectiveness and efficiency of the recommended control strategies and also point to the possibility of applying them in actual power systems to improve stability and performance.

A modified tilt controller for AGC in hybrid power system integrating forecasting of renewable energy sources

AbstractThis article emphasizes the intermittent characteristics of renewable energy sources (RESs) and explores the role of forecasting in improving the performance of the automatic generation control (AGC) mechanism of the interconnected power system. Due to hybridization of power system basic AGC controller (PID, TID, and ID‐T) are insufficient to stabilized the system parameters. So, a new type of fractional order integral tilted derivative controller (FIDN‐T) has been proposed, which give better performance in terms of settling time, undershoot and overshoot in case of RESs with real data forecasting also. FIDN‐T has been compared with some existing controller which give the results better than basic controller. In order to optimize the different parameters of the proposed controller, a new modified Opposition‐based Sea‐horse Optimization (OSHO) algorithm has been proposed. The OSHO is compared with a few existing, well‐known meta‐heuristic algorithms to show its superiority. The analysis has been conducted under different operating conditions, including step and random disturbances as well as the IEEE‐39 bus, to verify the robustness as well as adaptability of the suggested controller. The comprehensive results of the studies provide strong evidence in support of the effectiveness and efficacy of the suggested control methods and suggest that it has the potential to be implemented in real‐world power systems for improved performance and stability.

Discrete optimal quadratic AGC based cost functional minimization for interconnected power systems

The increasing complexity and difficulty of the Automatic generation control (AGC) problem has resulted from the increasing scale of interconnected power networks and changing daily demands. The primary goals of AGC are to control frequency variations at nominal levels and tie-line power variances at planned levels. To effectively deal with AGC control difficulties, this study introduces Discrete Optimal Quadratic Automatic Generation Control (OQAGC). One advantages of this method is the differentiation of quadratic cost function results into linear terms while minimizing control actions and minimizing state deviations. This developed control method leads to a simple and easy discrete control law that can be implemented for both linear and nonlinear systems. For optimizing the controller, this research work utilized an optimum control theorem using Lagrangian multipliers, while the functional minimization technique is used for systematically selecting the state and control weighting matrices in discrete form for N control regions (where N is the number of interconnected power systems). The discrete cost function needs are derived using this technique in terms of area control errors, integral area control errors, and control energy expenditure. Four interconnected power systems were analyzed with/without disturbances and area control errors, each with one thermal, hydro, and gas-generating unit. A two-area multi-source power system with renewable energy in control area 2 is analyzed for the performance of the proposed controller with generation rate constraints (GRCs). The functional minimization technique simplifies and eases the choosing of weighting matrices. Furthermore, the simulation findings suggest that the developed discrete optimum quadratic AGC control-based cost functional minimization approach enhances power system dynamics in terms of stability, steady-state performance, and the closed-loop control system's robustness to input load disturbances. As a result, the newly developed OQAGC approach demonstrates the significance of the discrete LQR controller for N multi-area power systems.

Design of optimal cascade control approach for LFM of interconnected power system

In the present era, due to increasing power demand and complex power system structures having various load disturbances, a load frequency management (LFM) scheme is indispensable to provide uninterrupted power to consumers. This research deals with a fractional-order proportional derivative - (one + fractional order integrator) (FOPD-(1+FOI)) cascade controller as a novel control structure to ameliorate the execution of automatic generation control (AGC) for the LFM of interconnected power system (PS). The implementation of this controller is uncomplicated, and it joins the output of the FOPD controller to (1+FOI) controller, where area control error and power error are considered in the outer and inner feedback control loops, respectively. A maiden attempt of a wild horse optimizer-assisted FOPD-(1+FOI) cascade controller for AGC of considered interconnected PS has been performed in this work. To benchmark the proposed control scheme, two areas reheat thermal PS with GDB and GRC nonlinearities is chosen as the test bench. A vivid comparative analysis of six state-of-the-art control techniques is performed, and the results reveal the potency of the presented control approach. Eigenvalues-based stability assessment of interconnected PS in conjunction with the proposed controller is also performed. Finally, for the real PS implementation of the presented control architecture a new england IEEE 39 test bus is considered and analyzed.

Comparative Performance Investigation of Genetic Algorithms (GAs), Particle Swarm Optimization (PSO) and Bacteria Foraging Algorithm (BFA) Based Automatic Generation Control (AGC) with Multi Source Power Plants (MSPPs)

This paper presents comparative performance analysis of GAs, PSO and BFA technique-based AGC regulators with MSPPs in each system. Each control area consists of hydro, thermal and gas power plants (HTGPPs) for generation of electricity. In this work, proportional integral derivative (PID) control structure-based AGC regulators are designed by using the above intelligent techniques. However, the cost function for designing of these intelligent controllers is formulated using the integral square error (ISE) performance index. In the previous AGC studies, simulation time for tuning of above intelligent AGC controller to analyze performance was not reported. Therefore, this research work proposes a new study to investigate the performance of these intelligent AGC controllers based upon performance indicators: (i) simulation time of these techniques, (ii) first peak value of transient response, (iii) oscillations in transient response and (iv) settling time of transient response. The parameters of the controllers are evaluated using these techniques and investigations are carried out to find the best performance of the system. Besides, system dynamic performance of BFA-tuned AGC controllers are also demonstrated for +1% to +4% step load disturbance in the respective control areas.

Control Strategy and Performance Analysis of Electrochemical Energy Storage Station Participating in Power System Frequency Regulation: A Case Study of the Jiangsu Power Grid

Electrochemical energy storage stations (EESSs) have been demonstrated as a promising solution to mitigate power imbalances by participating in peak shaving, load frequency control (LFC), etc. This paper mainly analyzes the effectiveness and advantages of control strategies for eight EESSs with a total capacity of 101 MW/202 MWh in the automatic generation control (AGC) in the power system of the Jiangsu power grid. Firstly, an adaptive tracking strategy for electricity quantity that considers the state of charge (SOC) of EESSs is proposed to calculate the baseline power of the EESS participating in AGC. This strategy can simultaneously coordinate different time scale application requirements, such as peak shaving and LFC. Then, an adaptive strategy for regulation requirement allocation among AGC control groups with EESSs that considers different area regulation requirements (ARRs) is proposed to calculate the regulation power of EESS participating in AGC. This strategy can realize the balance transfer of fast and slow regulation capacity, ensure the complementary advantages of various frequency regulation resources and improve the dynamic regulation performance of the control area. Finally, the proposed strategy is validated via a test system to confirm its effectiveness and advantages, as well as via a quantitative analysis on the improvement of the control performance standard (CPS) of the Jiangsu power grid with the participation of EESSs in AGC.

AGC in the Multi-Area Thermal System with Integration of Distribution Generation on the Frequency of the System Using the Classical PID & Hybrid Neuro-Fuzzy Controllers

In the essential operation and control of the intelligent power plants, the primary goal is to balance both powers generated on the generation side with the power demand on the demand side. But, the structural variation of the power systems with a continuing escalation of customer demand is changing load demand continuously. Due to these problems, it is a continuous requirement to control and accommodate the escalation power need of the customer. The system’s frequency is affected by disturbance and mismatching between power generation and power demand. The novel design of a hybrid Neuro-fuzzy system is used in the Automatic Generation Control (AGC) model of two equal-areas power systems. Installing non-conventional energy sources allows power producers and consumers to prefer power transactions in an economic scenario. The Distribution Generation (DG) is incorporated into the proposed model. The first design, the two equal-area AGC, uses a classical PID Controller considering DG. Second, the two equal- areas AGC design is investigated by considering Adaptive Neuro-Fuzzy Interface (ANFIS) with integrated Distributed generation. The simulation results show that an ANFIS incorporated with a DG-based PID controller exhibits more robust performance than the studied PID without and with the DG-based method. The reduction in vibration amplitude and the damping time provided by a PID-ANFIS-based AGC controller with the integration DG is 83.75%, much less than Classical Controllers.

Multi-Objective Mayfly Optimization-Based Frequency Regulation for Power Grid With Wind Energy Penetration

With the continuous development of society and under the background of sustainable development and resource conservation, the proportion of renewable energy in the global energy structure is increasing. At the same time, wind power has been widely used in many regions of the world because wind power technology is more advanced and mature than other renewable energy sources. In addition, with a large number of wind turbines connected to the grid, it not only helps automatic generation control (AGC) of power systems but also brings new challenges and difficulties. In this study, a multi-source cooperative control model of wind power participating in AGC frequency regulation is established to solve the dynamic problem of power distribution from real-time total power command to different AGC units. This study presents an optimal AGC-coordinated control method based on the multi-objective mayfly optimization (MMO) algorithm, which makes the fitting degree of power command output and actual output curve high and the adjustment mileage payment minimum, so as to achieve the best AGC performance. Finally, the simulation results show that this method can effectively decrease the total power deviation and adjustment mileage payment in the multi-source-coordinated control of AGC.

Power Generation Monitoring of a hybrid Power System with I-GWO designed trapezoidal type-II fuzzy Controller

ABSTRACT This article has implemented a robust type-II fuzzy PID controller for automatic generation control (AGC) of a diverse energy source-based multi-area power system under various operating conditions. The AGC aims to make stability over frequency and tie-line power of the power system. The proposed controller is accessed with Type-II Fuzzy systems with trapezoidal-based membership functions. In controller design concern, a novel soft computing algorithm called an improved-grey wolf optimizer (I-GWO) is approached for this study. The performance of the proposed trapezoidal membership function-based type-II fuzzy controller (TM-T2FC) has been extensively compared with the existing triangular membership-function based fuzzy-II controller (TrM-T2FC), type-I fuzzy controller (T1FC) and PID controller for AGC of the multi-area power system. Furthermore, the viability and effectiveness of the anticipated I-GWO algorithm have been separately assessed through suitable comparative studies. The robustness of the proposed TM-T2FC controller is investigated through different sensitive analyses and further assessment has been carried out by extending the proposed controller to a five area power system. It is observed from extensive studies and performance assessment that the proposed TM-T2FC controller can be a potential candidate for the AGC of the multi-area power system.

Intelligent Frequency Control Strategy Based on Reinforcement Learning of Multi-Objective Collaborative Reward Function

Large scale wind power integration into the power grid will pose a serious threat to the frequency control of power system. If only Control Performance Standard (CPS) index is used as the evaluation standard of frequency quality, it will easily lead to short-term centralized frequency crossing, which will affect the effect of intelligent Automatic Generation Control (AGC) on frequency quality. In order to solve this problem, a multi-objective collaborative reward function is constructed by introducing a collaborative evaluation mechanism with multiple evaluation indexes. In addition, Negotiated W-Learning strategy is proposed to globally optimize the solution of the objective function from multi dimensions, it avoids the poor learning efficiency of the traditional Greedy strategy. The AGC control model simulation of standard two area interconnected power grid shows that the proposed intelligent strategy can effectively improve the frequency control performance and improve the frequency quality of the system in the whole-time scale.

Design and implementation of a novel FO‐hPID‐FPID controller for AGC of multiarea interconnected nonlinear thermal power system

Automatic generation control (AGC) plays a crucial role in modern power system to control the frequency with change in power demand. A novel fractional order (FO) hybrid PID-fuzzy-PID (FO-hPID-FPID) controller is proposed in this paper as the secondary controller for AGC of a three unequal area interconnected nonlinear reheat thermal power system considering generation rate constraint. This paper also deals with the implementation of FO fuzzy PID (FOFPID) and FO PID (FOPID) controllers in addition to the proposed controller along with their counterpart integer order controllers to establish the dominance of FO controllers over integer order controllers. Various parameters of the controllers are optimized by hybridizing two global optimization techniques: particle swarm optimization and modified sine cosine algorithm (MSCA) and the optimization techniques so obtained is called as hybrid PSO-MSCA (hPSO-MSCA) technique. A step load perturbation of 0.01 p.u. is injected in area-1, and the system response is investigated independently with different controllers. It is revealed that the proposed FO-hPID-FPID delivers superior performance than the other controllers. Sensitivity analysis for parametric variations is carried out, and random load test is performed to validate the robustness of the proposed controller. Further, it is observed that the controllers deliver result in an ascending order of control performance as PID, FOPID, FPID, FOFPID, hPID-FPID and FO-hPID-FPID controllers with FO-hPID-FPID controller delivering the best result and the FO controllers deliver better control performance than their integer order counterparts.

Data-Driven Resilient Automatic Generation Control Against False Data Injection Attacks

With the advancement of communication technologies and the development of the smart grid, today's physical power systems present an ever-growing dependency on cyber resources. It increases cyber vulnerabilities, causing safety and system stability concerns. In this article, a data-driven resilient automatic generation control (AGC) scheme is proposed under a false data injection attack (FDIA). The key idea is to identify the relationship between AGC signals and system operational conditions. This is achieved by the proposed regression-based FDIA signal predictions, including sequence-to-point prediction and the long short-term memory network-based prediction. It allows us to reconstruct the AGC control signals without being affected by FDIAs and to attenuate attacks in the closed control loop, thus alleviating the impact of FDIA on system performance. Numerical results carried out on the benchmark systems validate the effectiveness of the proposed method.

Automatic generation control of diverse energy source-based multiarea power system under deep Q-network-based fuzzy-T2 controller

ABSTRACT The present research article has proposed a robust type-2 fuzzy PID (T2-FPID) controller to obtain automatic generation control (AGC) of a diverse energy source-based multiarea power system under various operating conditions. The AGC aims to make necessary control actions over frequency and tie-line power of the power system. The proposed T2-FPID controller is accessed with triangular membership functions for getting improved performance of fuzzy controllers. The parameters of proposed controller are optimally designed with suggesting a deep Q-network (DQN) algorithm under various operating conditions. The performance of proposed T2-FPID controller is validated over type-I fuzzy PID (T1-FPID) controller and PID controller in response to AGC of the multiarea power system. To justify superiority, the performance of DQN algorithm over particle swarm optimization (PSO) and genetic algorithm (GA) has been synthesized through suitable comparative studies. Performance analysis demands superiority of proposed T2-FPID controller for AGC of multiarea power system. The robustness of proposed T2-FPID controller is investigated under wide variation of system parameters and has been carried out through sensitive study. It has been conferred through various simulation results that proposed DQN-based T2-FPID controller gracefully improves the system performances in regard to the AGC of multiarea power system.

Optimal Automatic Generation Control with Hydro, Thermal, Gas, and Wind Power Plants in 2-Area Interconnected Power System

This paper explores automatic generation control (AGC) of a more realistic 2-area multi-source power system comprising hydro, thermal, gas, and wind energy sources-based power plants in each control area. The wind power plants (WPPs) have been growing continuously worldwide due to their inherent feature of providing eco-friendly sustainable energy. But, operations of WPPs are associated with system stability problems due to lack of inertia. However, WPPs do not participate in the elimination of mismatch between generation and demand by AGC but disturbance can be injected by the WPPs due to the stochastic nature of wind energy. An optimal controller based on full state feedback control theory is designed to conduct the study. The system dynamic performance analysis is carried out for 1% step load disturbance in corresponding control areas. It is observed that the system dynamic graphs of deviation in area frequency and tie-line power are significantly improved with the implementation of optimal AGC controller compared to GA tuned classical controller. It has also been shown that the WPPs aid the increase in load disturbance when the input wind power reduces but it negates the effect of increase in load disturbance for increase in wind energy to the WPPs.

Generation‐based automatic generation control with multisources power system using bacterial foraging algorithm

AbstractThis article presents an application of bacterial foraging algorithm (BFA) for design and implementation of generation‐based PID structured automatic generation control (AGC) in a 2‐area multisources power system with hydro, thermal, and gas power plants incorporated in each area. Most of AGC studies carried out so far have considered the initial system loading to be equal to 50% of the area generation capacity. But, AGC controller parameters are uncertain due to stochastic nature of power demand of the end users. Hence, in this article, the design of AGC controller is proposed on the basis of generation schedule by incorporating changes in power system gain constant, power system time constant, frequency bias constant, and so on. The dynamic responses of power system with BFA tuned AGC controller are compared with the genetic algorithm tuned AGC controller. The parameters of the controllers are evaluated by using these techniques and investigations are carried out to find the best performance of the system. Therefore, it is desirable to find the parameters of the generation‐based controller depending upon the contribution of its constituent hydro, thermal, and gas energy sources in the total power generation.

Pathfinder algorithm optimized fractional order tilt-integral-derivative (FOTID) controller for automatic generation control of multi-source power system

This paper introduces a fractional order tilt-integral-derivative (FOTID) controller which is structurally analogous to fractional order proportional-integral-derivative controller in a power system for solving automatic generation control (AGC) problem. It is optimized by a recent metaheuristic optimizer called pathfinder algorithm (PFA). An interconnected two-area power system model comprising of multi-sources like thermal, hydro and gas generating units including physical constraints namely, governor dead band (GDB) and generation rate constraint (GRC) are taken into consideration for the study. The efficiency of the proposed controller for AGC is shown by comparing it with PFA optimized tilt-integral-derivative (TID) and proportional-integral-derivative (PID) controllers with integral of time multiplied absolute error (ITAE) taken as the objective function. Simulation study supports the claim that the proposed controller provides better dynamic responses as compared to the others. Sensitivity and robust analyses are done to demonstrate the effectiveness of the proposed PFA optimized FOTID controller to a wide variation in system parameters, at different step load and random load disturbances.

Implementation of hybrid SSA–SA based three‐degree‐of‐freedom fractional‐order PID controller for AGC of a two‐area power system integrated with small hydro plants

In this work, a comparative study is analysed between the fractional-order controllers such as fractional-order proportional–integral–derivative (FOPID), two-degree-of-freedom proportional–integral–derivative (DOF FOPID) and 3DOF FOPID, and conventional controllers such as proportional–integral–derivative (PID), 2DOF PID and 3DOF PID employed in automatic generation control (AGC) in power system. The proposed 3DOF FOPID controller is validated as superior one among all other controllers. The gains of all the controllers are optimally plucked by novel salp swarm algorithm (SSA). Further, hybrid salp swarm algorithm–simulated annealing (hSSA-SA) algorithm is introduced to enhance the proficiency of 3DOF FOPID controller by sensibly plucking the gain parameters. The proposed approaches are implemented in a two-area thermal-hydro-diesel system. Small hydro plants (SHPs) of similar characteristics are enforced in both areas with their dynamic responses by conceding frequency deviations of each area. Further, sensitivity and robustness analysis of the system with and without SHP are observed by varying some important parameters of the system. Finally, the supremacy of three novel approaches (fractional based controllers, hSSA-SA and SHP) is substantiated gracefully.

Hybrid alopex based DECRPSO algorithm optimized Fuzzy-PID controller for AGC

This paper is corroborated the hybrid Alopex based DECRPSO algorithm (ADECRPSO) over DE, ADE, PSO, and CRPSO algorithms to pursuit the gain parameters of the PID and Fuzzy PID (FPID) controller. In a two area thermal-hydro-diesel power system, primacy of FPID controller is endorsed with PID controller tuned with assorted techniques. The hybrid ADECRPSO algorithm is affirmed over above mentioned algorithms to tune PID controller in a two area hydro-thermal system. PSO, DE, CRPSO, ADE and ADECRPSO are executed individually to optimize the controller to enhance the transient analysis by conceding undershoot, overshoot, and settling time of the system. The compilation of advantages of alopex based DE and craziness based PSO causes an adequate hybrid algorithm which enhances the performance of Automatic Generation Control (AGC). The step load uprise in area-1 is imposed to observe the activities of AGC. Undeniably, FPID controller optimized by ADECRPSO commits superior performance over PSO, DE, CRPSO, and ADE optimized controller as proposed AGC system. So, the modified mutation of DE by alopex scheme enhances the potentiality to tune the system variables.

Anticipatory AGC control strategy based on wind power variogram characteristic

Increasing penetration of wind power has a negative effect on system frequency deviation due to the fast variability and uncertainty characteristics of wind. In this study, a wind power variation prediction-based automatic generation control (AGC) feedforward control method is applied within the conventional AGC framework to minimise system frequency and tie-line deviations. Firstly, the variogram function is introduced for analysing the characteristics of wind power variations. A relationship between the variogram of wind power and the trend component of wind power is found and then a three-parameter power-law model is established. Then, the generation rate constrains of AGC generators is considered as the main constraint factor that influences wind power smoothing. By predicting the wind power variations in AGC control time-scale, a coordination AGC feedforward control strategy is proposed. The control strategy makes different types of AGC generators act in advance to improve the ability of the power system, corresponding to the forecasted ramp variations in wind power generation. Finally, the performance of the proposed coordination control strategy is verified and tested using the actual data from China. Simulation results show that with this new strategy, frequency deviation under wind power variations can be effectively decreased.

Grasshopper optimisation algorithm of multistage PDF + (1 + PI) controller for AGC with GDB and GRC nonlinearity of dispersed type power system

This article introduces Grasshopper Optimisation Algorithm (GOA)-based multistage Proportional Derivative filter type (PDF) with One plus Proportional Integral (1 + PI) controller for Automatic Generation control (AGC) in dispersed type three area power system. The control-area 1 consists of thermal, hydro and Wind units. Similarly, control-area 2 consists of thermal, hydro and Diesel units. Control-area 3 consists of thermal, hydro and Gas units. Nonlinearities like Governor dead-band, GRC (generation rate-constraint) and boiler dynamics are included for realistic representation. A comparative analysis is carried out by GOA with multistage PDF + (1 + PI) controller with classical PID/PI controller to justify the superiority of the proposed controller. The dominance characteristic of GOA with PDF + (1 + PI) controller structure is tested over Particle swarm optimisation (PSO) and Genetic algorithm (GA) optimisers with PDF + (1 + PI) controller. The robustness of the complete framework was justified by study and analysis of sensitivity by enhancing and decaying SLP and system elements in a band 50% from its minimal value. From the analysis, it is found that the GOA method with PDF + (1 + PI) controller is providing dominance characteristics performance with random load variations and system parameters of the various components of the power system.