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High penetration of renewable energy-based generators increases variability in power systems. To prevent transmission lines from overloading due to such variations, advanced power flow management system should be developed. This paper proposes an Automatic Generation Control (AGC) design that mitigates power flow increase on heavy load lines. The developed method optimizes dispatching of control order from AGC so as not to increase power flow of these lines. This is achieved by considering how the output change of each control unit effects power flow in the system. The performance of the proposed method is examined by simulation study with 10-machine power system model. Furthermore, the communication infrastructure of the developed AGC system and its components are developed according to IEC 61850 communication standard. Operational instructions are mapped onto IEC 61850 messages. Simulations are performed with network emulators to validate the developed communication models and their operation. The benefit of the proposed communication system with IEC 61850 is achieving plug and play capability with universal models. The results of both simulations show that communication infrastructure is successfully developed and AGC prevents power flow increase in heavily loaded lines by making use of remained capacity of light loaded lines as proposed.

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.

With the energy transition, the cyber-physical system (CPS) is expanded to the cyber-physical-social system (CPSS), and connected by the communication network, which is introduced in the form of a highly reliable cellular star network in this paper. Due to the uncertainties of wind turbine/photovoltaic power outputs, an improved rolling ultra-short-term forecast method with high reliability is proposed, which is based on long short-term memory networks, and the superiority is proved by comparing with multi-layer perception neural networks, linear regression, support vector regression and random forest. Then, two stochastic optimization models are constructed, one is based on CPS, the other is based on CPSS. As for social factors, electric vehicle aggregator is stimulated by cost to actively participate in the dispatch operation. Moreover, in order to ensure the stable operation of the smart energy system, the frequency deviation of automatic generation control system is studied as one security constraint. The optimal results display that social factors can not only make the system more economic and flexible, but also further increase the consumption of RE. Moreover, accurate forecast results can improve the performance of optimization dispatch operations.

Communication plays a vital role in incorporating smartness into the interconnected power system. However, historical records prove that the data transfer has always been vulnerable to cyber-attacks. Unless these cyber-attacks are identified and cordoned off, they may lead to black-out and result in national security issues. This paper proposes an optimal two-stage Kalman filter (OTS-KF) for simultaneous state and cyber-attack estimation in automatic generation control (AGC) system. Biases/cyber-attacks are modeled as unknown inputs in the AGC dynamics. Five types of cyber-attacks, i.e., false data injection (FDI), data replay attack, denial of service (DoS), scaling, and ramp attacks, are injected into the measurements and estimated using OTS-KF. As the load variations of each area are seldom available, OTS-KF is reformulated to estimate the states and outliers along with the load variations of the system. The proposed technique is validated on the benchmark two-area, three-area, and five-area power system models. The simulation results under various test conditions demonstrate the efficacy of the proposed filter.

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.

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 rapid integration of variable generation (VG), such as photovoltaics (PV), necessitates an increase in the secondary frequency regulation (SFR) to handle the system intra-dispatch interval imbalance because of VG's variation. Although PV has the control capability to provide SFR, the main challenge is in guaranteeing the delivery of PV's energy and SFRs capacities in the automatic generation control (AGC) within each dispatch interval considering its uncertainty. This paper proposes a deliverable VG SFR provision model with endogenous VG's power uncertainty decomposition. First, the uncertainty of VG is decomposed using distributionally robust chance constraints through which the deliverable SFR provision is guaranteed. Next, the intra-interval frequency response of PV's SFR is validated with a user defined AGC model including the PV power plant. Finally, the economic benefits and the reliability improvements—such as the frequency deviation reduction with PV providing SFR—can be integrated and evaluated. The proposed model is tested in a modified 18-bus system and in the 240-bus simplified Western Electricity Coordinating Council test system. Results demonstrate that with the deliverable SFR from PV, the system cost and the frequency reliability can be improved simultaneously. PV's SFR performance can be guaranteed with the proposed model.

In response to the automatic generation control (AGC) signals, the direct power control of a large-scale PV power plant easily encounters the communication bottlenecks, the high optimization difficulty and computation burden due to the large number of controllable inverters with different response performances. To handle these problems, a hierarchical framework of coordinated power control (CPC) is constructed, which is decomposed into a upper-layer CPC between different sub-areas and a lower-layer CPC between different inverters in each sub-area. Instead of a centralized optimization, a novel random forest-assisted fast distributed auction-based algorithm (FDAA) is proposed for a distributed optimization of CPC. The random forest can rapidly generate a dynamic surrogate model of the optimization results from the low-layer CPC to the upper-layer CPC, thus these two-layer optimizations of CPC can be decoupled without too much interactions and computations. The effectiveness of the proposed method is thoroughly evaluated on a PV power plant with 10 sub-areas and 100 inverters under various irradiation conditions.

This paper presents an automatic generation control algorithm applicable for a multi-area power system. A more practical models is used to represent the main components of the power system. The frequency bias factor (B) is set optimally by using an artificial intelligence technique. The generation rate constraints (GRC) are taken into consideration with dead band characteristics of the governor. For the first time, a sequential optimization of the fractional order (PID) control parameters, the governor speed regulation (R) and the frequency bias factor (B) is proposed. The objective function to be minimized is the updated integral time absolute error (UITAE). The tuning of the control parameters is dependent on minimizing the objective function by using the Grey wolf optimization (GWO) algorithm. The proposed algorithm is tested on an interconnected three power pools different system (Reheat, Gas and Hydro) with varying degrees of load step change. The simulation results of the presented process are compared to those achieved by using the particle swarm optimization method. The results obtained reveal the robustness of the proposed algorithm in terms of settling time and peak overshoot.

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.

With the large-scale integration of renewable power generation, frequency regulation resources (FRRs) are required to have larger capacities and faster ramp rates, which increases the cost of the frequency regulation ancillary service. Therefore, it is necessary to consider the frequency regulation cost and constraint along with real-time economic dispatch (RTED). In this paper, a data-driven distributionally robust optimization (DRO) method for RTED considering automatic generation control (AGC) is proposed. First, a Copula-based AGC signal model is developed to reflect the correlations among the AGC signal, load power and renewable generation variations. Secondly, samples of the AGC signal are taken from its conditional probability distribution under the forecasted load power and renewable generation variations. Thirdly, a distributionally robust RTED model considering the frequency regulation cost and constraint is built and transformed into a linear programming problem by leveraging the Wasserstein metric-based DRO technique. Simulation results show that the proposed method can reduce the total cost of power generation and frequency regulation., Comment: This paper has been accepted by IEEE Transactions on Power Systems

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 rapid advancement of electric consumer demand enforces challenging situations to manage high penetration of Solar PV and Wind Power into the hydrothermal system resulting in load frequency cont...

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.

Techniques for simulation and application of automatic digital generation control are presented. Objectives of generation control are reviewed. A digital control scheme is described and implementation. The presented directive software is capable of guiding the whole system so that control objectives are achived in a satisfactory manner.

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.

Control and communication technologies are key building blocks of cyber-physical systems (CPSes) that can improve the efficiency of the physical processes. However, they also make a CPS vulnerable to cyberattacks that can cause disruptions or even severe damage. This article focuses on one particular type of CPS cyberattack, namely the time delay attack (TDA), which exploits vulnerabilities in the communication channels to cause potentially serious harm to the system. Much work proposed for TDA detection is tested offline only and under strong assumptions. In order to construct a practical solution to deal with real-world scenarios, we propose a deep learning-based method to detect and characterize TDA. Specifically, we design a hierarchical long short-term memory model to process raw data streams from relevant CPS sensors online and continually monitor embedded signals in the data to detect and characterize the attack. Moreover, various strategies of interpreting the outputs of the model are proposed, which allow the user to tune the performance based on different objectives. We evaluate our model on two representative types of CPS, namely power plant control system (PPCS) and automatic generation control (AGC).Code and dataset can be found at: https://github.com/prakharg24/tda For TDA detection, our solution achieves an accuracy of 92% in PPCS, compared with 81% by random forests (RFs) and 72% by k-nearest neighbours (kNNs). For AGC, our solution achieves 98% accuracy, compared with 74% by RFs and 71% by kNNs. It also reduces the mean absolute error in the delay value characterization from about six to two seconds in the PPCS, and from about three seconds to half a second in the AGC, with about 3x to 4x shorter reaction latency in both systems.

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.

This paper presents one of the first real-life demonstrations of coordinated and distributed resource control for secondary frequency response in a power distribution grid. A series of tests involved up to 69 heterogeneous active distributed energy resources consisting of air handling units, unidirectional and bidirectional electric vehicle charging stations, a battery energy storage system, and 107 passive distributed energy resources consisting of building loads and solar photovoltaic systems. The distributed control setup consists of a set of Raspberry Pi end-points exchanging messages via an Ethernet switch. Actuation commands for the distributed energy resources are obtained by solving a power allocation problem at every regulation instant using distributed ratio-consensus, primal-dual, and Newton-like algorithms. The problem formulation minimizes the sum of distributed energy resource costs while tracking the aggregate setpoint provided by the system operator. We demonstrate accurate and fast real-time distributed computation of the optimization solution and effective tracking of the regulation signal over 40 min time horizons. An economic benefit analysis confirms eligibility to participate in an ancillary services market and demonstrates up to $\$ $ 53k of potential annual revenue for the selected population of distributed energy resources.

This paper reveals Automatic Generation Control (AGC) strategies of power systems including diverse type power generating sources and comprehensive literature review is also presented. These diverse type energy sources considered conventional power sources like thermal, diesel, nuclear, etc. and Renewable Energy Sources (RESs). RESs are solar energy, wind energy, hydro energy, etc. A variety of AGC schemes based on hard, soft and artificial intelligent computation techniques is presented here. The benefits and their limitations of these energy generating units are also taken in this article. In the present scenario, deregulation, smart micro-grid and grid concept is also utilized with interconnection of the considered energy generating sources. The literature of this review article fulfills the gap of recent and previous decades research work and provides future exploration in AGC techniques.

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.

This work pertains to integration of supercapacitor energy storage system with wind-penetrated two-area power system. A small-sized SCES device is incorporated in automatic generation control of each area. For robust and optimum usage of SCES, a customized neuro-adaptive predictive control model is developed using level 1 S-function coding. This model feeds reference power command to the converter of storage system with its voltage used as feedback loop. A step load disturbance reflecting load outage is introduced back to back in the power system. It is seen through simulation studies carried out in MATLAB Simulink Environment that there is significant improvement in frequency response and tie power deviations. SCES converter power limits are fully maintained; besides, SCES voltage and power response is also depicted.

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

As the conventional automatic generation control (AGC) is inadequate to deal with the strong random disturbance issues induced by the ever-increasing penetration of renewable energy to the power grids, this article proposes a deep-reinforcement-learning-based three-network double-delay actor-critic (TDAC) control strategy for AGC to handle the above problem, which is mainly developed by multiple neural networks to fit the system action strategies and evaluate the value. The proposed strategy can increase the exploration efficiency and the quality of AGC and improve the system control performance using the modified actor-critic (AC) method with incentive heuristic mechanism, while a novel iterative way of the value function is also used to reduce the bias of optimization effectively for achieving optimal coordinated control of the power grid. The simulations are provided in the work to show the control performance of the strategy. Compared with other smart methods, the simulation study demonstrates that TDAC has excellent exploratory stability and learning ability. Meanwhile, it also can improve the dynamic performance of the power system and achieve the regional optimal coordinated control.

Traditional reinforcement learning cannot effectively solve the strong random disturbance problem caused by large-scale new energy access; therefore, the response speed of AGC (automatic generation control) becomes slower and the performance becomes poorer. This study considers the double Q learning that can solve the problem of overestimation of the value of state-action pairs in the reinforcement learning algorithm based on the Q framework as the fulcrum. It integrates a grey wolf optimization algorithm that can quickly search for the optimal solution in the unknown search space. Additionally, it proposes a multiagent cooperative AGC strategy for multiregional energy interconnection, namely, the GWDQ strategy, to quickly obtain the multiregional cooperative optimal solution in the AGC process. The two-area integrated energy system model, such as the hybrid-electric gas turbine system, CCHP and other forms of energy, and the northeast power grid model with a multiregional energy interconnection, are simulated. The results showed that the proposed strategy has a strong learning ability. Besides, the convergence speed and control performance are superior to that of the traditional reinforcement learning algorithm. The proposed strategy can quickly obtain the multiregional collaborative optimal solution in the AGC process.

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...

As the penetration rate of new energies, energy storage devices and electric vehicles increases continuously in power grid, power grid faces strong random disturbances, as well as the issues of downfall in frequency control ability induced by the factors such as system inertia reduction and frequency control under-capacity in traditional power units. Therefore, an algorithm of automatic generation control with DQ( σ , λ) is proposed for the multi-area interconnected power grid. By integrating the eligibility trace, DQ( σ , λ) adopts the principle of double Q-learning to unify double Q(λ) and double Expected-Sarsa(λ), thereby the over-estimation of Q in the algorithm and the phenomenon of over-fitting can be avoided. The simulations are provided to demonstrate the effectiveness of the proposed algorithm, where the improved IEEE standard two-area load-frequency control model and the model of the multi-area interconnected power grid based on Central China Power Grid are adopted. The results show that the phenomena of random disturbances and frequency instability can be eliminated in multi-area interconnected power grid. The convergence is faster and the dynamic performance is better in the proposed algorithm compared with the traditional algorithms.

In this paper, a novel energy pricing and sharing strategy is proposed to solve the energy management and market bidding problems of a virtual energy station (VES) in multi-carrier energy systems. First, the energy cell-tissue-based group interaction framework and the multiagent-based communication and control architecture are established in the VES. Second, the general model of an energy cell is built, in which the integrated demand response process and the automatic generation control mode of a combined heat and power unit are designed. Third, according to the classification of energy cells, the aggregation rules of the VES without the participation of third-party agents are proposed, and the internal trading mechanism of the VES led by a higher energy cell is designed based on a Stackelberg game. Moreover, a three-stage hybrid stochastic robust optimization strategy is proposed to address uncertainties in market prices and renewable energy output. Finally, the optimization model is linearized and solved by duality theory, KKT conditions, and column-and-constraint generation (C&CG) algorithm. Simulation results prove the rationality and effectiveness of the framework and business model for the VES.

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.

For dynamic performance improvement of modern power systems, the use of fast acting energy systems like superconducting magnetic energy storage (SMES) is imperative. In this paper, incorporation of a small rating SMES in a solar and wind power penetrated multi-area power system is proposed. A non-linear neural adaptive predictive controller is used to generate an optimal power command taking into account the converter rating and energy level constraints of SMES unit. The SMES is represented by a control relevant model comprising of a first order lag compensator cascaded by an integrator to translate the hardware constraints pertaining to its coil current into modified power constraints. Moreover for avoiding the sudden SMES outage, the power thresholds are forcibly varied as the SMES current reaches near its maximum and minimum values. The uprightness of the designed scheme is illustrated by simulation studies performed on a three area power system.

The load frequency control (LFC) system is an essential component of the smart grid and is located between cyberspace information and the physical infrastructure. Because the operation of LFC depends on the modern network communication technology, it is vulnerable to network attacks. The false data injection attack (FDIA) injects attack data into the data collected by the LFC system, affecting the control center’s calculation and causing errors in the control signals, which affects the stable operation of the power system. For the LFC system, this article builds a cyber–physical model. Based on the model, an FDIA detection and defense mechanism based on the GAN network is proposed. By calculating, contrasting, and replacing the control signals, the proposed method can detect and defend against FDIAs. The experimental results on the two-area interconnected power system, the IEEE 39-bus, and IEEE 118-bus power system show that the proposed defense method can ensure the power system frequency and tie-line power deviation remain within the set value when suffered from FDIA.

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.