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In order to improve the automatic generation control (AGC) performance of thermal generators, this paper presents a stochastic model predictive control (SMPC) approach for a battery/flywheel hybrid energy storage system (HESS) to distribute power. The approach combines an adaptive Markov chain for power demand prediction of HESS, a scenario tree generation and model predictive control strategy. To develop an effective prediction model to deal with the randomness of a thermal generator in response to AGC command, a Markov chain is used to describe the randomness of the HESS power demand, and a posteriori information is used to adapt to the fluctuation of the AGC command. A scenario tree generation approach is proposed to make better use of the Markov probability matrix. Based on these efforts, an SMPC approach is proposed for HESS energy management. Simulation results show that the regulation performance of the proposed approach outperforms conventional approaches, and that its performance is close to the model predictive control strategy with prescient information of the future power demand. In addition, compared with other approaches such as rule-based strategies, it does less damage to the battery and yields higher annual average net benefits in the whole life cycle.

The electric power system is a complicated dynamic system with a range of operating states and parametric uncertainties, especially due to change of the network topology, load increment and generation scheduling. Under this circumstance, traditional power system transient stability analysis methods may not always be appropriate. This paper presents the development of a computational methodology for evaluating the effect of parametric uncertainties on the small-signal stability analysis of power systems. A probabilistic approach is applied as a metric for the dynamic performance of the damping ratio of critical eigenvalues. The method is based on a Monte Carlo simulation for the analysis of automatic control of generation. The methodology is used for the performance evaluation of three classical controller tuning techniques: Frequency Response, Approximate Method and Ziegler-Nichols. The results show that the methodology is valid and potentially useful for quantifying the effect of parametric uncertainties in power systems dynamics simulations.

In this paper, a cascade PI-PD secondary control scheme is presented in automatic generation control (AGC) application under a deregulated environment. Along with conventional electric power genera...

This article deals with automatic generation control (AGC) of a three-area power system having five diversified sources of generation like thermal unit, hydro unit, wind unit, diesel unit and a gas unit are interconnected together. Area-1 of the power system consists of a thermal, a hydro and a wind unit, area-2 has a thermal, a hydro and a diesel unit and area-3 consists of a thermal, a hydro and a gas unit. To make system more realistic different nonlinear components like governor dead band (GDB), generation rate constraint (GRC), boiler dynamics and communication delay are taken into account. A novel two-degree of freedom fractional order PID with derivative filter and fractional order PD with derivative filter (2-DOF-FOPIDN-FOPDN) cascaded control strategy is adopted to improve the dynamic performance of the power system. The results obtained with the proposed cascaded controller are compared with that of PID, FOPID and 2-DOF-PIDN-PDN cascaded controller to prove its superiority. To enumerate the gains of different controllers optimally, a recently developed bio-inspired optimisation algorithm named selfish herd optimisation (SHO) is capitalised. Further, the work is extended by taking a two-area hydro-thermal system to compare the result of the SHO tuned PID controller with that of modern hybrid firefly algorithm-pattern search (hFA-PS) technique. Transient analysis is carried out by applying a sudden load disturbance of 0.01 p.u in area-1 and the robustness of the controller is examined by varying both system parameters and applying a randomly varying load in area-1. From the investigation it is concluded that the 2-DOF-FOPIDN-FOPDN controller gives a flawless and a distinct performance.

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.

This paper presents the modeling, design, and experimental analysis of an Automatic Generation Control (AGC) for a hydropower plant using Adaptive-Neuro-Fuzzy Inference system (ANFIS). This was aimed at reducing the frequency deviations which occur during power generation. The Adaptive-Neuro-Fuzzy Inference system (ANFIS) was used to intelligently control the selection of parameters for the effective control of power in the hydropower plant. The proposed ANFIS was trained with input–output data of the fuzzy logic controller (FLC). The ANFIS model is used as a hybrid learning model which includes the Least Square Estimate (LSE) and back propagation algorithm (BPA). The conventional PID, FLC, and ANFIS controllers were investigated using MATLAB. In order to determine the best controller, the controllers were experimented and compared to determine the controller with the best performance. The results show that frequency deviations occur as a result of a continuous variation of loads, which make the deviations difficult to control when a governor is not applied. Furthermore, the response of the AGC of the hydropower plant (in the single and double area) with step load changes was studied. The simulation results show that the ANFIS controller performs better compared to the PID as well as the FLC. Further results indicate that the proposed ANFIS controller helps to speed up the performance of the AGC of the hydropower plant. The ANFIS controller not only improved the performance but also made the fuzzy inference system (FIS) less dependent on the expert system.

The multi-area micro-grid system (MAMG) is a complex nonlinear system. Instability or performance degradation may cause due to inadequate damping under the sudden load fluctuation in the system. Owing to this, to provide uninterrupted electric power with additional attributes, strong and intelligent control methods are curiously necessary for automatic generation control of micro-grid (MG). The utilization of ennoble class topper optimization (E-CTO) tuned fuzzy cascaded proportional-integral proportional-derivative (fuzzy PI-PD), i.e., fuzzy PI-PD controller in frequency control in two areas interconnected MG (isolation mode) with renewable penetration is a novel work. A maiden attempt of the E-CTO is intended to optimize the range of the membership function of the fuzzy controller by employing the integral time absolute error (ITAE) criterion. The effectiveness of the proposed controller is exposed by opposing the dynamic answers of MAMG with a fuzzy PI-PD and traditional controller. In the end, a sensitivity study is offered to show the power of the planned approach to wide fluctuations in the MG parameters, magnitude likewise the situation of step/ random load disturbance. The recommended MAMG and optimization techniques are simulated in the LabVIEW@2015 environment. Also, OPAL-RT based real-time analysis is presented to full-prof the novel work.

In this article, Particle Swarm Optimisation (PSO) algorithm is used to obtain the controller gain for Automatic Generation Control (AGC) of the representation of an Interconnected Power System (IP...

This paper reviews on the function of Automatic Generation Control (AGC) as an intelligent mechanism in enhancing electrical power systems dynamic performance at various perturbations; by sustainin...

This work proposes a novel frequency regulation paradigm for multi-area interconnected power systems. The developed approach capitalizes on phasor measurement units (PMUs) advanced monitoring to overcome design limitations imposed by legacy supervisory control and data acquisition (SCADA) systems. For this, a novel measurement-based controller integrating primary and secondary regulation is proposed. First, taking advantage of PMUs synchronized measurements, novel centralized corrective actions are developed using dynamic data aggregation. These actions provide a foreknowledge of generators expected steady-state during the system dynamics, significantly improving the system frequency rebound and steady-state realization while ensuring null area control errors (ACE). Next, a new local power system stabilizing perspective is developed. This controller provides local counterweighting actions allowing for faster corrective actions without compromising the system dynamic response, i.e., mitigating oscillations and overshoots. The proposed approach design considers monitoring and communication system non-idealities, such as: latency, processing and waiting time, measurements synchronization and sampling resolution. In addition, stability and steady-state objectives are mathematically demonstrated, and comparative simulation case-studies developed. Obtained results indicate a meaningful improvement of power systems frequency regulation and reliability, including faster steady-state realization and rebound, mitigation of oscillations and overshoot, improved service capacity and higher reliability to cascade events.

Reflecting the uncertainty of renewable energy generations and loads, power system AGC signals are essentially random processes. For the sake of the optimal operation and control of the AGC participant, such as energy storage systems (ESSs) in a performance/mileage-based regulation market, taking the uncertain nature, especially the temporal correlation, of the AGC signals into consideration can be beneficial; hence, random process models of the AGC signals are needed. However, a continuous random process model of the AGC signal that jointly considers the probability distribution and the temporal correlation is still lacking. To fill this gap, this paper first presents a systematic methodology for modeling the continuous random processes of AGC signals based on stochastic differential equations (SDEs). It is shown that AGC signals may have a saturated stationary probability density function and a biexponential temporal correlation, which are very different from the renewable generations. To capture these special characteristics, SDEs are then carefully constructed, which are easy to use in optimization and control. Using the PJM traditional and dynamic regulation (RegD and RegA) signals and the signal received from a battery ESS (BESS) plant in Jiangsu, China for example, simulation shows that the SDE is able to simultaneously capture the probability distribution and temporal correlation accurately.

The automatic generation control (AGC) system is temporally situated between economic dispatch and synchronous generator dynamics, and its primary role is to regulate frequency within and tie-line flows between control areas. Given appropriate choice of participation factors (feed-forward controller gains that govern the disaggregation of the area-level power requirement to individual generators), the AGC can be engineered to nudge system dynamics toward a steady-state operating point corresponding to economic optimality. This paper establishes necessary and sufficient conditions under which a widely accepted choice of participation factors guarantees the alignment of steady-state synchronous generator outputs with a global minimum of a prototypical economic dispatch problem. In so doing, it resolves several ambiguities and formalizes technical assumptions governing the role of the standard AGC architecture in the context of economic dispatch and steady-state operation. Numerical case studies tailored to a modified version of the New England 39-bus 10-machine test system validate the theoretical results.

This paper proposes a novel Improved Artificial Electric Field Algorithm (IAEF)-based AGC in a multi-area power system to minimize the area control error and to obtain an optimal solution. Here, fo...

In the incessantly growing size and structure of the modern power system having different load demand uncertainties, the use of intelligent, proficient and robust automatic generation control (AGC) strategy is indispensable to deliver decent superiority of power supply to the consumers. This research presents a fuzzy-tilt-fractional order integral-filtered derivative (FTIλDN) controller as a novel control tool to improve AGC recital of multi-area interconnected power systems (PSs). The imperialist competitive algorithm (ICA) is employed to optimize the FTIλDN controller parameters. To boost FTIλDN controller performance, asymmetrically spaced membership functions (MFs) are utilized, where MFs adjoining to zero are meant for fine-tuning and MFs far of zero are used for coarse-tuning. Firstly, a well adopted linear 2-area reheat thermal PS (RTPS) is explored critically and then to signify the virtue and scalability of the approach, the analysis is conducted on a 5-area unequal RTPS comprising deadband and generation rate constraint nonlinearities. The supremacy of the approach is established by contrasting the results with ICA/bacterial foraging optimization algorithm optimized FTIDN and various existing controllers. Investigation of results affirms the better performance of FTIλDN over other controllers regarding minutest error criteria and undershoot/overshoot/settling time of frequency/tie-line power deviations at disturbances. Additionally, robustness analysis validates the robust conduct of FTIλDN controller under variations of parameters, load disturbances and in the incident of temporary/permanent tie-line outage. At last, a real-time hardware-in-the-loop (HIL) simulation test is performed to authenticate the viability and effectiveness of the method for practical applications.

The amalgamation of the various renewable energy sources to the grid reduces the dependence on non-renewable energy sources for power requirements. The introduction of electric vehicles (EV), not only further reduces the burden on these non-renewables and renewables but also provides better stability fluctuated by load demands. This paper presents automatic generation control (AGC) of a two-area thermal system with a stab for incorporating EV within them and analyze its impact alongside other renewables. The performances of proportional integral-proportional derivative (PI-PD), proportional plus Integral (PI), proportional plus integral plus derivative (PID) and fractional-order proportional integral-fractional-order proportional derivative (FOPI-FOPD) controller are evaluated in the system without incorporation EV. The supremacy of FOPI-FOPD is limpidly revealed over others during the analysis. The superior FOPI-FOPD controller is then used to analyze the system with incorporation EVs. Extensive simulations and comparisons are provided to justify the competency of the EVs when incorporated in the system with other renewables. The optimization of controller parameters is performed using the Sine Cosine Algorithm, and the system is analyzed considering a step load perturbation of 5% in area1. All simulation works are performed in MATLAB/Simulink platform.

This paper presents the integration of renewable energy resources into the Automatic Generation Control (AGC) of two area power system under deregulation. Area-1 includes the combination of thermal system, gas power system, aggregate Electric Vehicle (EV), and Dish-Stirling Solar Thermal system (DSTS) whereas area-2 contains thermal system, gas power system, aggregate electric vehicle, and Wind Turbine System (WTS). To achieve the realistic approach, nonlinearities such as Generation Rate Constraint (GRC), Governor Dead Band (GDB), and Boiler Dynamics (BD) are explored in proposed test system. AGC's main aim is to keep the balance between load and generation and to achieve this balance secondary frequency regulation mechanism play an important role. Therefore, a tilt proportional integral derivative controller has been used to achieve desired dynamic response of the system. A new Quasi Opposition Lion Optimization Algorithm (QOLOA) has been suggested for studied system to get the optimum values of controller and system parameters. Integral Square Error (ISE) is considered as an objective function for the optimization of the anticipated AGC mechanism. Furthermore, Hybrid Energy Storage (HES) is used to damp the oscillation of the considered AGC system. Hence, for this investigation, it consists of the hybridization of the Redox Flow Battery (RFB) and Superconducting Magnetic Energy Storage (SMES) system. Additionally, the sensitivity analysis is also performed to evaluate the robustness of the proposed QOLOA based control scheme. The suggested control mechanism is compared with previously published work on the same platform to show the effectiveness and its superiority of presented work.

In this article, a robust model predictive control (MPC) is proposed for the gate-controlled series capacitor (GCSC) to contribute to load frequency control. The proposed control model consists of a two-layer MPC in which a nominal MPC produces an initial control signal together with a prediction of system frequency response in a nominal system without uncertainty. An ancillary MPC then produces a control command for the actual system with uncertainty based on measurements, and signals provided by the nominal system. The control signals are provided in such a way that the error in the frequency response of the actual system is minimized with respect to that of the nominal system. Optimization procedures are also conducted to attain optimal values of weighting coefficients associated with the input, and output in the objective functions. Constraints associated with system, and GCSC control are also taken in providing the control signals into consideration. A linear model is presented to formulate the contribution of GCSC control to the power flow of tie-line. The effectiveness of the proposed method in dealing with uncertainties is compared with a conventional MPC, a scheme with proportional-integral controllers, and a system without GCSC. The method robustness is also illustrated by case studies with load uncertainty, and wind power fluctuations, and also uncertainty of parameters. The impact of the delay in communication links on the performance of the control scheme is also evaluated.

This brief proposes an approach to estimate static gains of dynamic models for automatic generation control systems in coal-fired power generation units. The main idea is to infer static gains of dynamic models from amplitude changes of model inputs and outputs in finite-time ramp responses. First, input and output time sequences are approximated by a few straight lines as piecewise linear representations. Second, finite-time ramp responses are detected as the data segments where inputs and outputs simultaneously move along straight lines with significant amplitude changes. Third, estimated static gains are obtained by solving linear equations of significant amplitude changes in the ramp responses. One technical challenge of selecting the thresholds of significant amplitude changes is resolved by exploiting a relationship among the amplitude change, noise variance, and signal-to-noise ratio. Comparing with a standard system identification approach, the obtained results from the proposed approach can be validated in a convincing manner: ramp responses being found from historical data samples can be confirmed by checking whether data segments of inputs and outputs are actually in straight lines; estimated static gains can be verified by comparing output amplitude changes in the ramp responses with their estimated values. Numerical and industrial examples are provided to support the proposed approach.

The present work focuses on the power oscillation damping of an interconnected multi-area multi-source power system depicting the load fluctuations issue in reference frame of automatic generation control (AGC). In real-time, the load profile characteristic is un-deterministic and uncertain in nature. Therefore, a diverse perspective of area load profiles such as step load disturbance, pulse load perturbation, sinusoidal load pattern, random load pattern and uniformly distributed random load are taken into study. The investigated power system model is a two-area system, classified by reheat thermal, hydro and gas generating units, lumped together in a control area with the impacts of governor deadband and generation rate constraint. AGC performance work is propagated by proportional-integral-derivative (PID) controller with filter and, then, fractional order PID (FPID) controller. Also, a fast-acting thyristor controlled series compensator (TCSC) device modified by the Taylor theorem is incorporated as a damping controller to pursuit its significances in AGC tool. In an additional work, a lead-lag compensator block is also incorporated to the basic TCSC controller to show its impact on AGC. Sensitivity analysis of both the basic and the advanced TCSC damping controller is investigated under loading and model parameter variations. The design problem i.e., the constrained optimization problem is tuned by employing the quasi-oppositional harmony search (QOHS) algorithm. From the mathematical point of view, calculations eigenvalues, performance indices and transient parameters, and sensitivity analysis are presented in support of the designed TCSC-QOHS controller. Graphically, the simulation results are presented. The obtained simulation results showed that the realization of TCSC in series with the tie-line with the FPID controller can be an effective approach for the AGC tool.

The PID controller still remains a widely used and very effective means of achieving stability in control systems. Generally, the performance of the controller is determined by the proportional, integral and derivative gains of the controller. The classical techniques: ZieglerNichols (ZN) open loop method; ZN closed loop method; Chien-Hrones-Reswick (C-H-R) load rejection method; and meta-heuristic technique: the fuzzy logic algorithm, are used to determine the tuning parameters of the PID controller in this study. The performance comparison of these controllers is done for automatic generation control (AGC) of a multi-source single-area hydrothermal-gas power system. In such power systems, each source has a participation factor that determines its contribution to total power generation. The root mean square error (RMSE) is deployed to determine the proportionate balance of each generator’s output with its corresponding participation factor. The performance comparison of the controllers using Simulink/MATLAB shows that the fuzzy-PID controller achieved the most proportionate generation balance. http://dx.doi.org/10.30572/2018/kje/100310

Previously linearised plug-in electric vehicle (PEV) models are adopted in automatic generation control functioning. However, lesser attention is given towards the exact designing of a PEV unit con...

To enable power supply in rural areas and to exploit clean energy, fully renewable power systems consisting of cascaded run-of-the-river hydropower and volatile energies such as pv and wind are built around the world. In islanded operation mode, the primary and secondary frequency control, i.e., hydro governors and automatic generation control (AGC), are responsible for the frequency stability. However, due to limited water storage capacity of run-of-the-river hydropower and river dynamics constraints, without coordination between the cascaded plants, the traditional AGC with fixed participation factors cannot fully exploit the adjustability of cascaded hydropower. When imbalances between the volatile energy and load occur, load shedding can be inevitable. To address this issue, this paper proposes a coordinated tertiary control approach by jointly considering power system dynamics and the river dynamics that couples the cascaded hydropower plants. The timescales of the power system and river dynamics are very different. To unify the multi-timescale dynamics to establish a model predictive controller that coordinates the cascaded plants, the relation between AGC parameters and turbine discharge over a time interval is approximated by a data-based second-order polynomial surrogate model. The cascaded plants are coordinated by optimising AGC participation factors in a receding-horizon manner, and load shedding is minimised. Simulation of a real-life system with real-time pv data collected on site shows the proposed method significantly reduces load loss under pv volatility., Comment: Accepted by IET Renewable Power Generation; 13 pages

The paper addresses the Automatic Generation Control (AGC) of the interconnected two-area power system under deregulated environment. Area 1 includes thermal system, Distributed Generation (DG), an...

This letter proposes a novel decentralized event-triggered control algorithm to replace the conventional periodic sampling/communication mechanism of automatic generation control (AGC) for multi-area power systems. For each control area, event-triggering rules that only rely on local measurements are designed to decide the sampling/communication instants. Further, a strictly positive dwell time is introduced to exclude Zeno behaviours. The $\mathcal {L}_{\infty }$ -stability of the multi-area power system with the developed event-triggered control law and essentially bounded net load disturbances is studied, and a sufficient stability criterion is established. Finally, case studies demonstrate the effectiveness and efficiency of the proposed method.

Automatic generation control (AGC) is an automation scheme that regulates the output of several generators employed at different areas of an interconnected power system simultaneously in response to load variation in the most economical way. This article implements a fuzzy-two degree of freedom-PID controller considering derivative filter (F-2D-PIDF) optimally tuned through grasshopper optimization algorithms (GOA) for AGC of a three unequal area interconnected power system. Initially, comparative performance analysis is carried out for conventional PID controllers optimally designed by particle swarm optimization, teaching learning-based optimization and GOA techniques. After ensuring better performance from GOA based PID controller, the study extended to establish dominance of the proposed F-2D-PIDF controller over others like PID, PID with derivative filter (PIDF), two degree of freedom-PIDF, and fuzzy-PIDF for the same power system in presence and absence of nonlinearities with GOA framework. In all these above studies, a load perturbation of 0.01 p.u. is applied in area-1. Comparative performance analysis reveals that GOA based F-2D-PIDF controller outperforms other controllers in all aspects. Finally, robustness of the proposed controller verified by varying system parameters and loading condition.

This article investigates the impact of phase-locked loop (PLL) systems along with virtual inertia on system dynamics of a parallel AC/HVDC tie-line integrated two-area automatic generation control...

The multimicrogrid system is a complicated nonlinear system, which brings performance degradation due to deficient damping under the unexpected fluctuation in power generation due to the presence of renewable sources, dynamically changing loading conditions, and parameter variations. Owing to this, to provide consistent electric power with superlative attribute, sturdy and intelligent control techniques are amazingly imperative in the automatic generation control of microgrid (MG). The application of imperialist competitive algorithm (ICA)-based fractional-order integral proportional derivative with filter (IPDF), i.e., integral tilt derivative with filter controller (ITDF) controller in frequency control in two areas interconnected MG (isolation mode) with renewable penetration, is a novel work. A maiden attempt of the ICA is proposed to optimize the gains of the ITDF controller utilizing the integral time absolute error criterion. To demonstrate the supremacy of ICA, its outcomes are contrasted with two existing optimization strategies, namely the genetic algorithm and the particle swarm optimization. The effectiveness of the proposed controller is revealed by contrasting the dynamic responses of multi microgrid (MMG) with proportional integral derivative with filter (PIDF) and tilt integral derivative with filter (TIDF) controllers. At last, a sensitivity investigation is performed to exhibit the power of the studied strategy to wide variations in the MG parameters, magnitude as well as the location of step/random load disturbance. The proposed MMG is simulated in MATLAB/Simulink environment.

This article deals with the Automatic Generation Control (AGC) mechanism for an interconnected, deregulated hybrid power system. The physical generation constraints such as boiler dynamics, governor dead band (GDB) and generation rate constraints (GRC) are included for a more realistic approach. The integration of distributed generation (DG) systems is also considered to match with present scenario. The intermittency nature of the DGs ruins the system's inertia and dynamic behaviour. A novel cascaded fractional order controller configuration 2-DOF (FOPIλDN)-PDN is proposed for the AGC mechanism and a new quasi opposition based volleyball premier league (QVPL) algorithm is employed to optimize the controller gains. On the other hand, the inertia emulation strategy (IES) based modified HVDC tie-line is used parallel to the AC tie-line to retrace the power system's inertia. The impact of the proposed controller and IES based modified HVDC tie-line has been analysed by subjecting to several case studies. The effectiveness of the QVPL based proposed controller has also been tested by comparing it with the previously published works on the same platform.

This article introduces grasshopper optimization algorithm (GOA) for getting an optimum and effective solution of an automatic generation control (AGC) problem in hybrid power systems under a dereg...

In this study, an adaptive fuzzy-based PID (AFPID) controller has been suggested for frequency regulation of power systems where the parameters of the proposed controller are optimised by a simplif...

We reexamine and critique the classical area control error concept used in automatic generation control, and propose a new error signal termed the area injection error. Unlike the ACE, the AIE uses direct measurement of generator power levels, yielding improved AGC performance in the presence of bias uncertainty and nonlinearity in generator turbine-governor responses. As a by-product of our analysis, we conclude that the effective frequency biasing found in a common textbook implementation of AGC is larger than intended, resulting in unintended interaction between control areas. Our theory is validated via simulations on a detailed two-area test system.

In this article, an Improved Frequency Regulation (IFR) for an interconnected hybrid power system under a deregulated scenario is proposed. The analyzed test system comprises of a thermal power system, biogas plant, and Distributed Generation (DG). The impact of the integration of Renewable Energy Sources (RES) has been examined by considering DG system with solar and wind power generation. In order to make the system more realistic, appropriate non-linearities are incorporated into the thermal and biogas system. Electric Vehicle (EV) is also employed to take care of some portion of uncontracted demand. A maiden hybridized fuzzy-Proportional Integral (FPI)-Linear Active Disturbance Rejection Control (LADRC) is proposed and successfully implemented for IFR. Also, a new Quasi-Opposition-based Artificial Electric Field Algorithm (QO-AEFA) is proposed to acquire the optimal controller gain parameters of the tested system. A pertinent performance analysis is also examined for other employed algorithm in this study, to figure out the eminence of the proposed control algorithm. A comprehensive examination and comparison of the proposed controller with other controllers shows its effectiveness for the proposed test system. In order to ensure the reliability of the proposed controller, sensitivity analysis is also carried out for system parameters that reveal its robust nature. Further, case studies for random load variations and comparison with the works in previous literature also manifest the puissance of the current research work. Moreover, the improved results under random weather conditions illustrate the effectiveness of the proposed IFR under deregulation.

For the dependable, safe, and stable operation of electric systems, frequency should be regulated continuously employing appropriate intelligent controllers. Hence, this paper proposes a Chaotic Ha...

The frequency control is the mandatory task in a modern power system because of load demand variation and the integration of renewable energy sources (RESs). The main objective of automatic generation control (AGC) scheme is to maintain the balance between generation and load demand. This study proposes a graphical robust characteristic ratio assignment (RCRA) approach for PIDA controller tuning based on maximum sensitivity ( M s ) for AGC. The basic feature of this graphical approach is that characteristic ratios ( γ i ) in RCRA scheme are calculated in terms of M s . The important part of this design is that the analytical expressions for coefficient gains of PIDA controller are derived in terms of γ i and system parameters. Here, the non-reheat steam-turbine and hydro-turbine based single and multi-area power systems are considered for validation of the proposed control scheme. The critical issue in controller design for a hydro-turbine based power system is the non-minimum phase behaviour. The robustness and performance of RCRA-PIDA controller are examined under system non-linearities, parametric uncertainty and disturbances. Further, the performance of proposed approach is evaluated in the presence of RESs. Finally, it is observed that the proposed control approach performs better in comparison to the recently published control schemes.

Power flow formulated with a distributed slack bus involves modeling the active-power output of each generator with three elements: a nominal injection modulated by a fraction of the net-load imbalance allocated via a participation factor . This setup acknowledges generator dynamics and system operations, but it has long been plagued by ambiguous and inconsistent interpretations of its constituent elemental quantities. In this paper, we establish that, with the: i) nominal active-power injections set to be the economic dispatch setpoints, ii) participation factors fixed to be the ones used in automatic generation control, and iii) net-load imbalance considered to be the total load and loss unaccounted in economic dispatch, the power flow solution best matches results from a simulation unto steady state of the system differential algebraic equation (DAE) model. Numerical case studies tailored to the New England test system validate the analysis by demonstrating that solutions obtained from a distributed slack formulation offer lower errors (with respect to DAE-model simulations) compared to the exhaustive set of all single slack bus choices.

Background Automatic generation control (AGC) of multi-area interconnected power system (IPS) is often designed with negligible cross-coupling between the load frequency control (LFC) and automatic voltage regulation (AVR) loops. This is because the AVR loop is considerably faster than that of LFC. However, with the introduction of slow optimal control action on the AVR, positive damping effect can be achieved on the LFC loop thereby improving the frequency control. In this paper, LFC synchronized with AVR in three-area IPS is proposed. Model predictive controller (MPC) configured in a dense distributed pattern, due to its online set-point tacking is used as the supplementary controller. The dynamics of the IPS subjected to multi-area step and random load disturbances are studied. The efficacy of the developed scheme is ascertained by simulating the disturbed system in MATLAB/Simulink. Results Based on the comparative analysis on the system responses, it is established that by cross-coupling the LFC loop with AVR, reductions of 66.45% and 59.09% in the frequency and tie-line power maximum deviations respectively are observed, while the respective settling times are found to be reduced by 29.68% and 22.77% when compared with the uncoordinated control scheme. In addition, the standard deviation and variance of the integral time absolute error of the system’s responses have reduced by 23.21% and 20.83% respectively compared to those obtained in a similar study. Conclusions The reduction in the maximum deviations and settling times in the system states indicates that introducing the voltage control via AVR loop has improved the frequency control significantly. While the lower standard deviation and variance of the integral time absolute error signify improvement in the robustness of the developed algorithm. However, this improvement is at the detriment of the controller size and computational complexity. In the uncoordinated control scheme, the control vector is one-dimensional, while in the coordinated scheme, the control vector is two-dimensional for each CA.

Energy storage systems (ESSs) have been applied in power systems for a long time. It participates in the grid frequency control at two levels, the primary frequency control and the secondary frequency control. In this paper, a control strategy that integrates one synchronous generator (SG) and ESS as one single generation unit named flexible generator (FG) is proposed, allowing ESS to participate in primary frequency control. The main trick is that the proposed control strategy not only adopts SG's frequency as the ESS control input but also feeds back FG's total output power to control the ESS. FGs have more substantial damping and stability than SGs. The proposed control strategy has a superior performance compared to traditional PID control and fuzzy control strategies of ESSs. The flexible control parameters are determined by the linear quadratic regulator (LQR) with disturbance suppression. The ESS's capacity is discussed and determined. Simulation results of the New England 39-bus system verify the advantages of FGs both in load changes and faults. The HIL experiment results also illustrate the applicability and effectiveness of the flexible control.

This study proposes a new modelling approach for studying low-frequency oscillations (LFOs) in a droop controlled islanded microgrid. Due to the absence of inertia, these sources are more vulnerable to power and frequency oscillations. Their quick response can introduce faster electrical dynamics in the system which should be monitored from time to time. To ease the analysis process, this study proposes a simplified method for finding LFO in the microgrid. Inspired from the small-signal automatic generation control model of the conventional grid, a transfer function based closed-loop small-signal model of inverter-based islanded microgrid is presented in this study to study power-sharing among the various inverters. The proposed model uses the concept of dynamic power flow through the network to find the power output of each source following load perturbations in a system. Time domain simulation results and eigenvalue analysis is provided to verify the effectiveness of the proposed small-signal model. By comparing the results obtained with actual system simulation in MATLAB/Simulink, it is found that the proposed simplified model is able to predict the stability margin and the LFO of the system without using actual detailed state-space modelling procedure.

Frequency disturbances caused by load perturbations of multi area interconnected power system are reduced by using proper control schemes provided via secondary control loop along with primary control of generation plants. These secondary controllers provide simultaneous control signal to individual machines of a particular area when the control scheme is area centralized. This area centralized mechanism provides quick control action with less computational burden since control problem dimensions gets reduced even the area consists multiple plants/machines. Such centralized new cascade control scheme is proposed in this paper to supervise the secondary control mechanism and it is implemented with parallel connection of 2-Degree of Freedom Proportional-Integral-Derivative (2-DOF PID) controller combined with regular PID controller. The performance of this new controller is studied on multi area multi machine interconnected power system with participation factor concept and later its relative performance in terms of dynamic and steady state specifications are compared with conventional PID and 2-DOF PID controllers. These control parameters are tuned by Invasive Weed Optimization (IWO) algorithm to achieve better system outputs. Case studies presented in this paper show the advantages of proposed control scheme.

LFC (Load Frequency Control) difficulty is created by load of power system variations. Extreme acceptable frequency distinction is ±0.5 Hz which is extremely intolerable. Here, LFC is observed by PID controller (PID-C), Fuzzy and ANFIS controller (ANFIS-C). To control different errors like frequency and area control error (ACE) in spite of occurrences of load disturbance and uncertainties of system is checked by MATLAB/SIMULINK software. Proposed Controller offers less, and small peak undershoot, speedy response to make final steady state. LFC is mandatory for reliability of large interconnected power system. LFC is used to regulate power output of generator within specified area to maintain system frequency and power interchange. Here, two area multi source LFC system is analyzed. ANFIS is utilized for tie-line power deviation and controlling frequency. Proposed controller is compared with other controller and it is found that proposed controller is better than other controller. Proposed controller is better in terms of Robustness. The output responses of interconnected areas have been compared on basis of peak-undershoot, peak-overshoot and settling time (Ts). Result of FLC is compared to that of with classical controller such as proportional derivative plus integral (PID) controller which suggests that conventional controller is slow. Keywords: LFC, Fuzzy, PID, ANFIS, LFC; FLC; ACE; PID-C, AGC.

In most of the automatic generation control (AGC) studies proposed so far, area control error (ACE) signal is derived considering fixed step size load disturbance which does not represent the real ...

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 v...

In the present work, a hybrid Teaching Learning Based Optimization and Pattern Search (hTLBO-PS) technique with Tilted Integral Derivative (TID) controller is suggested for Automatic Generation Control (AGC) under deregulated environment. In the beginning, a two-area four units thermal-gas system is assumed and Tilted Integral Derivative (TID) controller is employed. The superiority of hTLBO-PS based TID is illustrated by comparing the published results with Genetic Algorithm (GA) and Differential Evolution (DE) tuned I/PI/PID controllers for the identical test scheme. After that the suggested methodology is extended to 2-area with 6-units power system by considering the non-linearities parameters. Additionally, to improve the performance of the system, a Thyristor Controlled Phase Shifter (TCPS) is connected in the tie-line and Superconducting Magnetic Energy Storage (SMES) devices are considered in each area. Finally, the sensitivity analysis of proposed method with perturbation in plant parameters has been analyzed.

This dissertation characterize the Automatic Generation Control (AGC) knit together with tandem reheat and cross reheat thermal system wielding Proportional Integral (PI) and Fuzzy Logic Controller (FLC). Multi area thermal system is designed with non-linearities such as governor dead band in addition with generation rate constraint. This study is come up with a knowledgeable application of Redox flow batteries (RFB) correlative along Static Synchronous Series Compensator. In order to enhance dynamic reaction of the load frequency control, redox flow battery has been appended in couple of the units as a result of being active rejoinder and nether time persistent. Ziegler Nichols method is wielded to regulate the PI controllers. During enormous load distraction within areas, conventional control methods alone would be ineffective in diminishing frequency deflection and power of tie-line swinging as a result of medium reaction by speed governor process. In order prevail this hindrance; fuzzy logic controller has been executed within the plant. Simulation studies affirm the efficacy of the fuzzy logic controller, depends upon the parameters like overshoots, under shoots and settling time, hence AGC functioning during abnormality in power system has been ameliorated. System is also studied for hydro thermal power system with simulation outcome.

This study presents the structural, operational and control aspects of doubly fed induction generator (DFIG) based wind integrated power systems. The automatic generation control (AGC) of a meshed power system including DFIG-based wind turbines has been framed and investigations under various system perturbation are presented. The two-area system consisting of non-reheat thermal turbines with DFIG and interconnected through parallel AC/DC tie-lines is considered for the study. The system non-idealities such as governor lag and generation rate constraints are taken into consideration. An AGC strategy using a layered recurrent artificial neural network (ANN) is proposed in this work. The gains of the proposed AGC are obtained by effectively training the ANN using a set of reliable data obtained from a widespread range of operating system conditions using robust control strategy. The study also incorporates the design of AGC for the power system using the fuzzy logic concept and other AGC actions such as integral (I), proportional–integral (PI) and proportional–integral–derivative (PID) calculated via the means of particle swarm optimization (PSO). The results obtained with the proposed ANN created AGC are linked and demonstrated their superiority over fuzzy logic PI and traditional PSO-based I/PI/PID AGC strategies under numerous system operating conditions.

As a widely-used technology, fluidized bed combustor (FBC) boiler is facing many control challenges such as coupling, high-order dynamics and nonlinearity. With more and more sustainable energy integrating into the bulk power system, the FBC boiler is obliged to speed up the respond to the automatic generation control command and this results in great challenges for keeping the bed temperature in the desired range. To this end, a modified active disturbance rejection control (MADRC) is put forward for high order systems with the type of K∕(Ts+1)n to enhance the control quality of the FBC boiler. Firstly, a simulation is carried out to discuss the estimation ability of the MADRC. A theorem about the stability analysis of the MADRC is provided and proofed theoretically. To help the field personnel to understand and use more easily, an effective tuning procedure is also concluded. Besides, a tuning toolbox based on the tuning procedure is established. Simulations and comparative experiments based on the Peltier temperature control platform validate the superiority of the MADRC, where the MADRC is able to enhance the control quality. Then the MADRC and other comparative controllers (the proportional integral controller and the regular ADRC) are designed for the FBC boiler. Simulation results illustrate that the MADRC can obtain the best performance. Statistical indices show that the MADRC has the smallest overshoot and shortest settling time. In addition, the MADRC still has the strongest ability to reject the input and coal quality variation disturbances. The advantages of the MADRC guarantee the high control quality and can be applied to industrial practice widely.

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.

A novel quasi-oppositional-based salp swarm algorithm (QSSA) is used to obtain the optimal values of controllers for automatic generation control of the two-area, assorted source of the generation-based power system. Thermal, hydro, and gas generating units are considered in the power system. Optimal values of proportional–integral–derivative (PID) controllers obtained using SSA and QSSA are compared with that of PID controllers based on differential evolution, teaching learning-based optimisation, and imperialist competitive algorithm. Observing the superiority with a QSSA-based PID controller, the study is protracted to obtain the optimal values of two-degree-of-freedom conventional PID (2DOF-PID) and 2DOF fractional order PID (2DOF-FOPID) controller with SSA and QSSA techniques. It is evidenced that QSSA outperforms SSA and also the QSSA-based 2DOF-FOPID controller establishes a better dynamic response than other controllers. 2DOF-FOPID controller is also employed for the system with generation rate constraint (GRC). The complete analysis is carried out by applying a step load disturbance of 1 p.u. in area-1. Robustness of the controller is verified by varying the systems' parameters and a randomly varying loading pattern in both areas with and without GRC. In both cases, the proposed QSSA-based 2DOF-FOPID controller is found firmly robust.