Automatic Generation Control Research Articles

NEURO-FUZZY INTELLIGENT CONTROLLER FOR LFC OF A FOUR-AREA POWER SYSTEM

In modern complex power systems, the problem of automatic generation control arises due to a sudden increase or decrease in load. This problem leads to instability in the system if the frequency control is not automatic, which may finally lead to system collapse. Hence, automatic control of frequency and tie-line power is significant. This research paper develops and compares the performance of an adaptive neuro-fuzzy inference system (ANFIS) controller with the conventional PID controller and the Takagi-Sugeno-Kang fuzzy logic controller for load frequency control (LFC) of a four-area power system with generation rate constraint (GRC) on turbines. The performance is compared in terms of errors, settling time and maximum undershoot of the frequency deviation for different step load changes using Matlab. The proposed ANFIS controller performs with less peak undershoot of - 0.7374 Hz and a settling time of 27.9823 sec at a 4% change in load. It reduces the steady-state error to zero. Thus, the proposed controller is the most suitable LFC in energy centers. The system parameters are taken from the IEEE press, and EPRI published books.

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.

Fractional order control strategy for superconducting magnetic energy storage to take part effectually in automatic generation control issue of a realistic restructured power system

Keeping frequency regulation (FR) while fulfilling bilateral contracts and pool-co transactions simultaneously in a competitive environment become a challenging task for solitary automatic generation control (AGC) owing to inherent nonlinear limitations like generation rate constraint (GRC), governor dead band (GDB), and communication time delay (CTD) in realistic power systems. An effective solution to help AGC is employing rapid-response energy storage devices (ESDs) like superconducting magnetic energy storage (SMES). It is an appropriate choice for applications like AGC which need to deliver a large amount of power within such little time. Although employing the SMES in the AGC issue has been addressed widely in literature, however, the considered SMES models have fundamental weak points which are explained thoroughly in this paper. A comprehensive literature review reveals that the lack of an efficient control strategy implemented on the real dynamics of the SMES unit remains unsolved and needs further investigation. In this paper, a maiden supplementary control approach based on fractional order controllers (FOCs) is put forward for the SMES per its real dynamics to take part effectually in the deregulated AGC. The concurrent design of the proposed SMES and AGC is treated as a nonlinear minimization issue wherein an appropriate performance index is minimized utilizing different meta-heuristic algorithms. To achieve realistic and reliable results, a fully competitive two-area various-GENCOs multiple-DISCOs power system is taken into consideration for examinations with taking into account the nonlinearity sources of GDB and GRC. Detailed investigations are conducted under different contract violation scenarios and different CTDs to assess the competency of the suggested SMES model. The robust behavior of the proposed SMES-AGC coordinated control is verified using sensitivity analyses. The simulation results confirm the superiority of the presented SMES model and control strategy over the former models to enhance AGC performance.

Frequency Stability Analysis of Multi-Renewable Source System with Cascaded PDN-FOPI Controller

The present work describes a multi-area (two and three) renewable-energy-source-integrated thermal-hydro-wind power generation structure along with fleets of plug-in electrical vehicles (PEVs) in each control area. The generation–load balance is the prime objective, so automatic generation control (AGC) is adopted in the system. In the paper, a cascaded combination of proportional derivative with filter PDN and fractional-order proportional integral (FOPI) is proposed and tuned using the hybrid chemical reaction optimization with pattern search (hCRO-PS) algorithm. The hCRO-PS algorithm is designed successfully, and its effectiveness is checked through its application to various benchmark functions. Further, Eigen value analysis is carried out for the test system to verify the system stability. The impacts of diverse step load perturbation (i.e., case I, II, III, and IV) and time-varying load perturbation are also included in the study. Moreover, the impact of renewable sources, PEVs in different areas, and varied state of charge (SOC) levels on the system dynamics are reflected in the work. From the analysis, it can be inferred that the proposed controller provides comparable results with other fractional-order and conventional controllers under varying loading conditions.

Stabilization of frequency in Multi-Microgrid system using barnacle mating Optimizer-based cascade controllers

This paper presents a new control strategy in multi-microgrid system for supressing the variations of frequency and of tie-line power flow due to intermittent nature of renewable sources and frequently varying load demands. A novel cascade combination of fractional order-based integral derivative and proportional integral derivative with filter called CFOID-FOPIDN controller is utilized as secondary controller for automatic generation control. In this work, diverse sources such as photovoltaic, wind, diesel engine, fuel-cell, electrical vehicles and battery energy storage systems are considered for investigations under various uncertainties conditions. A newly developed barnacle mating optimizer is presented to tune the CFOID-FOPIDN for diminishing the frequency and tie-line power changes. Moreover, the amalgamation of redox flow batteries and modern flexible alternating current transmission system device like distributed power flow controller is integrated into the system for ameliorating the dynamic stability of the system. The tested outcomes show that the barnacle mating optimizer-based CFOID-FOPIDN controller with the combination of distributed power flow controller and redox flow batteries exhibits low fluctuation rate for frequency (5.6 s for area-1 and 5.5 s for area 2) and tie-line power (6.2 s). Finally, the robustness of the CFOID-FOPIDN is validated under three operating conditions, such as step load perturbations, change in wind speed, irradiance, and change in both wind and irradiance levels.

The automatic generation controller based on whale optimization algorithm in PV-thermal power systems

Güç sistemlerinde üretilen güç ile tüketilen güç arasında denge sağlanması amacıyla otomatik üretim kontrolü (OÜK) adı verilen bir kontrol sürecinin gerçekleştirilmesi gerekmektedir. Rüzgâr türbinleri (RT) ve fotovoltaik (FV) paneller gibi yenilenebilir enerji kaynaklarının (YEK) kullanımının artması bu süreci daha zor hale getirmektedir. Bu nedenle, OÜK sürecinin daha hassas bir şekilde gerçekleştirilmesi gerekmektedir. Bu çalışmada, fotovoltaik (FV) sistem ve termal generatörden meydana gelen hibrit bir güç sisteminde OÜK işlemi için balina algoritması (BA) kullanılarak PID kontrolörün parametrelerinin belirlenmesi amaçlanmıştır. Alan-1 ve alan-2’de meydana gelen yük değişimleri durumunda amaçlanan BA ayarlı PID kontrolörün performansının yeterliliği incelenmiştir. Ayrıca, ateşböceği algoritması (ABA), genetik algoritma (GA) ve aşırı nüfus optimizasyonu (ANO) gibi optimizasyon tekniklerinin performanslarıyla bir karşılaştırma yapılmıştır. Elde edilen sonuçlar, çalışmada önerilen BA ayarlı PID kontrolörün, sistem frekansının aşma değeri ve oturma süresi bakımından diğer yöntemlere göre daha iyi sonuçlar verdiğini göstermiştir.

Hierarchical Distributed Coordinated Control for Battery Energy Storage Systems Participating in Frequency Regulation

At present, battery energy storage systems (BESS) have become an important resource for improving the frequency control performance of power grids under the situation of high penetration rates of new energy. Aiming at the problem that the existing control strategy is not sufficient for allocating the frequency regulation power instructions, a hierarchical distributed coordinated control strategy for BESS to participate in the automatic generation control (AGC) of a regional power grid is proposed. At the upper layer, the state of charge (SOC) of BESS and the technical characteristics of different frequency regulation power sources are comprehensively considered to complete the coordinated distribution of frequency regulation commands between BESS and traditional generators; at the lower layer, for the purpose of optimizing the economic operation of the regional power grid, the distributed consistency algorithm is used to control the distributed BESS in order to realize the fine management of power output of BESS. The simulation results indicate that this control strategy can give full play to the technical characteristics of different frequency power sources and improve the frequency regulation of the power grid. The excessive power consumption of BESS is successfully avoided, and the continuous operation of BESS is realized.

Hour-Ahead Optimization Strategy for Shared Energy Storage of Renewable Energy Power Stations to Provide Frequency Regulation Service

With the rapid growth of intermittent renewable energy sources, it is critical to ensure that renewable power generators have the capability to perform primary frequency response (PFR). This paper proposes a framework for using a shared battery energy storage system (BESS) to undertake the PFR obligations for multiple wind and photovoltaic (PV) power plants and provide commercial automatic generation control (AGC) service in the ancillary service market at the same time. Furthermore, an optimal bidding strategy in the hour-ahead energy and ancillary service markets is proposed to maximize the BESS' benefits, considering frequency regulation performance, battery degradation cost, and the state of charge (SoC) limits. The optimization model is first built as a chance-constrained optimization problem and then transformed into mixed-integer quadratically constrained programming using Wasserstein metric-based distributionally robust optimization technique. Simulation results show that the proposed optimal bidding strategy and solution method can effectively take advantage of a BESS to perform PFR and provide AGC services simultaneously and improve its economic benefits.

A Decentralized Intrusion Detection System for Security of Generation Control

Recently, several incidents have been reported relating to security breaches in the power system network. As the operation of the automatic generation control (AGC) system fully depends on communication technologies, any compromise in its functionality could lead to total system collapse. For example, the intruders may target the communication network of the legacy grid by launching deception and data-availability attacks, which have the potential to manipulate the crucial telemetered quantities, such as wide-area sensor measurements and (or) the control signals. Despite significant work on AGC security, most of the past studies were limited to the detection of intrusions in sensor measurements, and less emphasis is given to addressing the impact of attacks on control signals. To this end, this article proposes a decentralized intrusion detection system (IDS) that jointly identifies data anomalies in the sensor measurement and the control signals. The IDS is powered by a novel machine learning (ML) classifier, which we call cluster-driven ensemble learning (CDEL) algorithm. The proposed CDEL is based on the ensemble principle that combines the predictive power of multiple support vector machines and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> -means clustering algorithm. Experimental results reveal the supremacy of CDEL over existing state-of-the-art ML techniques.

A resilience-based recovery scheme for smart grid restoration following cyberattacks to substations

The recent surge of interest towards deploying cyber systems in power grids exposes the resulted Smart Grids (SGs) to cyberattacks, and consequently resilience degradation. Among different possible remedies, i.e., prevention, mitigation, and recovery, developing a well-crafted recovery scheme following these cyberattacks can noticeably improve the system resilience. However, an appropriate resilience-based optimization problem with a metric to capture the system resilience during each step of recovery is needed to be solved and enhance it by the right decisions. To address such a concern, this paper proposes an innovative recovery framework based on a novel metric that captures the resilience posture of SGs against cyberattacks. The developed metric captures (i) Power-side Resilience (PsR) including load, available reserve capacity, available line capacity, and reliability, of which the last three are traditionally overlooked in resilience literature, and (ii) Cyber-side Resilience (CsR) considering the maximum possible implication of attacks on the power grid. PsR will calculate the physical side resilience based on immediate damage and CsR measure the cyber-side resilience e based on possible damage to the system. The SG is fully recovered from an attack when the proposed scheme brought back all the cyber and physical aspects to the proximity of the pre-disturbance conditions. For practical feasibility, we have incorporated different SG capabilities and limitations including generator Automatic Generation Control (AGC) capability and ramp rate, load variations during the recovery phase, transient stability, and multi-stage attacks. Moreover, an AC multi-island load flow is utilized to check the power balance, voltage boundaries, and transmission lines limitations in each recovery step. The proposed framework is evaluated on the 39-bus New England test system to demonstrate its effectiveness and also applied to the IEEE 30-bus test case to compare the results of the proposed approach with those of existing works. The comparisons show that the proposed approach can enhance the overall system resilience by 22% in comparison to existing works.

Impact of Spotted Hyena Optimized Cascade Controller in Load Frequency Control of Wave-Solar-Double Compensated Capacitive Energy Storage Based Interconnected Power System

The concept of automatic generation control has an immense role in providing quality power in an interconnected system. To obtain quality power by controlling the oscillations of frequency and tie-line power, a proper controller design is necessary. So, an innovative endeavor has been undertaken to enforce a two-stage controller with the amalgamation of a proportional-derivative with filter (PDN) (integer-order) and a fractional order integral-derivative (FOID), i.e., PDN(FOID). In an effort to acquire the controller’s gains and parameters, a bio-inspired meta-heuristic spotted hyena optimizer is applied. Various examinations manifest the excellence of PDN(FOID) over other controllers such as integral, proportional–integral, proportional–integral-derivative filter, and fractional order PID from perspectives concerning the diminished amount of peak anomaly oscillations, and the instant of settling for a three-area system. The system includes thermal–bio-diesel in area-1; a thermal–geothermal power plant in area-2; and a thermal–split-shaft gas turbine in area-3. It is also observed that the presence of renewable sources such as wave power plants and photovoltaics makes the system significantly better compared to the base system, when assessed individually or both together. Action in a combination of capacitive energy storage with duple compensation is also examined using the PDN(FOID) controller, which provides a noteworthy outcome in dynamic performance. Moreover, PDN(FOID) parameter values at a nominal condition are appropriate for the random patterns of disturbance needed for optimization.

Line Overload Alleviations in Wind Energy Integrated Power Systems Using Automatic Generation Control

Modern power systems are largely based on renewable energy sources, especially wind power. However, wind power, due to its intermittent nature and associated forecasting errors, requires an additional amount of balancing power provided through the automatic generation control (AGC) system. In normal operation, AGC dispatch is based on the fixed participation factor taking into account only the economic operation of generating units. However, large-scale injection of additional reserves results in large fluctuations of line power flows, which may overload the line and subsequently reduce the system security if AGC follows the fixed participation factor’s criteria. Therefore, to prevent the transmission line overloading, a dynamic dispatch strategy is required for the AGC system considering the capacities of the transmission lines along with the economic operation of generating units. This paper proposes a real-time dynamic AGC dispatch strategy, which protects the transmission line from overloading during the power dispatch process in an active power balancing operation. The proposed method optimizes the control of the AGC dispatch order to prevent power overflows in the transmission lines, which is achieved by considering how the output change of each generating unit affects the power flow in the associated bus system. Simulations are performed in Dig SILENT software by developing a 5 machine 8 bus Pakistan’s power system model integrating thermal power plant units, gas turbines, and wind power plant systems. Results show that the proposed AGC design efficiently avoids the transmission line congestions in highly wind-integrated power along with the economic operation of generating units.

Fuzzy Logic Controller Equilibrium Base to Enhance AGC System Performance with Renewable Energy Disturbances

Owing to the various sources of complexity in the electrical power system, such as integrating intermittent renewable energy resources and widely spread nonlinear power system components, which result in sudden changes in the power system operating conditions, the conventional PID controller fails to track such dynamic challenges to mitigate the frequency deviation problem. Thus, in this paper, a fuzzy PI controller is proposed to enhance the automatic generation control system (AGC) against step disturbance, dynamic disturbance, and wind energy disturbance in a single area system. The proposed controller is initialized by using Equilibrium Optimization and proved its superiority through comparison with a classical PI optimized base. Results show that the fuzzy PI controller can reduce the peak-to-peak deviation in the frequency by 30–59% under wind disturbance, compared to a classical PI optimized base. Moreover, a fuzzy PID controller is also proposed and EO initialized in this paper to compare with the PIDA optimized by several techniques in the two-area system. Results show that the fuzzy PID controller can reduce the peak-to-peak deviation in the frequency of area 1 by 30–50% and the deviation of frequency in area 2 by 13–48% under wave disturbance, compared to the classical PIDA optimized base.

A Novel Improved GSA-BPSO Driven PID Controller for Load Frequency Control of Multi-Source Deregulated Power System

In this paper, a novel improved gravitational search algorithm–binary particle swarm optimization (IGSA-BPSO) driven proportional-integral-derivative (PID) controller is proposed to deal with issues of automatic generation control (AGC) of interconnected multi-source (thermal-hydro-gas) multi-area deregulated power systems. The effectiveness and robustness of the proposed controller is compared and analyzed with GSA and PSO-driven PID controllers. The simulated and mathematically formulated results show the superiority of the proposed IGSA-BPSO driven PID controller compared with the other two techniques in settling time, overshoot, and convergence time. The two-area test system considered in this article is integrated with a thermal, hydro, and gas turbine power plant. Integral time multiplied by absolute error (ITAE) is used as the objective function (minimization) by optimization techniques for getting optimum parameters of PID controllers installed in each area. The system’s dynamics are examined using poolco, bilateral, and contract violation cases under a deregulated environment, and the comparative results are shown to analyze the efficacy of the proposed concept. Physical constraints such as generation rate constraints (GRC) and time-delay (TD) have been considered in the system as a realistic approach. This paper considers an accurate AC-DC tie-link model for the proposed AGC mechanism. Dynamic load change condition is tested and verified. The variations of different parameters will be used in the robustness analysis of the proposed system. The comparison shows that the designed controllers are more robust and produce better results than those considered as references.

Diagonal Stability of Systems With Rank-1 Interconnections and Application to Automatic Generation Control in Power Systems

We study a class of matrices with a rank-1 interconnection structure, and derive a simple necessary and sufficient condition for diagonal stability. The underlying Lyapunov function is used to provide sufficient conditions for diagonal stability of approximately rank-1 interconnections. The main result is then leveraged as a key step in a larger stability analysis problem arising in power systems control. Specifically, we provide the first theoretical stability analysis of automatic generation control (AGC) in an interconnected nonlinear power system. Our analysis is based on singular perturbation theory, and provides theoretical justification for the conventional wisdom that AGC stabilizes under the typical time-scales of operation. We illustrate how our main analysis results can be leveraged to provide further insight into the tuning and dynamic performance of AGC.

Imposter Attacks in Energy Market Operation

The cyber-security of the power grid is of extreme significance due to growing dependency on modern technologies. Several recent real-world incidents reveal the risk associated with false data injection attacks. Besides corrupting the sensor’s data, intruders can also target real-time dispatch signals (DS) sent to generation systems over an unsecured network to launch financially motivated attacks. To conduct the attacks, we construct a novel self-adaptive undetectable attack model, termed Imposter Attacks (IA). The devised IA adjusts its intensity based on system conditions allowing energy providers to unethically raise profit without impacting the grid stability. It is shown that by adopting IA, the infected DS remains undetectable by traditional detectors. Unlike existing attack models, the IA tend to hide the corrupted quantity among the normal system data making them extremely difficult to identify. The feasibility of the IA is studied on the Automatic Generation Control system of the IEEE-39 bus system.

Particle Swarm Optimization Based Dual -Mode Fractional Order Control for Automatic Generation Control of Two Area Interconnected Hybrid System

Particle Swarm Optimization Based Dual -Mode Fractional Order Control for Automatic Generation Control of Two Area Interconnected Hybrid System

Review of the Requirements for Load Following of Small Modular Reactors

CO2 net neutralization by 2050 is a global target. Renewable energy and nuclear power generation are emerging as major power sources for CO2 net neutralization. Therefore, this paper comprehensively reviews the load-following operation method of nuclear power plants as a method to compensate for intermittency, which is the biggest weakness of renewable energy. First, this paper looks at the types of SMRs and elaborates the concept and necessity of load following. The comprehensive requirements for the load-following operation of an SMR, i.e., planned operation, automatic generation control, governor-free operation, cooperative control of the reactor and turbine generator, unit control of a multiple-module SMR, cogeneration, etc., are studied. Finally, the interaction between an SMR and the power grid during load-following operation and other technical issues are also reviewed. This paper can be used as a guide for load-following operations or as a guide for requirement analysis when developing a comprehensive control system of load following in SMR fleets.

Frequency Regulation on an Interconnected Power System With Fractional PID Controllers Optimized Using Rao Algorithms

Developing an optimization algorithm with independent algorithm-specific parameters is crucial in tuning the controller parameters of Automatic Generation Control (AGC). In 2020, Rao developed a robust and newly proposed algorithm independent of algorithm-specific parameters with faster convergence and less computational complexities. This paper presents the AGC of a sample two-area interconnected multi-source power systems with fractional-order PID controllers optimized using the Rao algorithm. The objective function selected for tuning the optimal gain of the controller using the Rao algorithm is based on Integral Squared Error (ISE). The robustness of the Rao algorithm for parameter variations and load changes is compared with other metaheuristic algorithms such as Genetic Algorithm (GA), Grey Wolf Optimization (GWO), and Particle Swarm Optimization (PSO). The efficacy of the proposed tuning method for AGC application is validated using the dSPACE 1103 hardware-in-loop simulation.

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.