3.1 Fuzzy Logic AGC

Nowadays, because of simplicity, robustness, and reliability, fuzzy logic is used in almost all fields of science and technology, including solving a wide range of control problems in power system control and operation. Unlike the traditional control theorems, which are essentially based on the linearized mathematical models of the controlled systems, the fuzzy control methodology tries to establish the controller directly based on the measurements, long-term experiences, and knowledge of domain experts/operators.

Several studies have been reported for the fuzzy-logic-based AGC design schemes in the literature.^52–70 There are many possible fuzzy logic controller structures for AGC purposes, some differing significantly from each other by the number and type of inputs and outputs, or less significantly by the number and type of input and output fuzzy sets and their membership functions, or by the type of control rules, inference engine, and defuzzification method. In fact, it is up to the designer to decide which controller structure would be optimal for the AGC problem. The applications of fuzzy logic in AGC systems can be classified into three categories: (1) using a fuzzy logic system as a dynamic fuzzy controller called fuzzy logic controller (FLC),^55,58,61,64–^66,68 (2) using a fuzzy logic system in the form of a proportional integral (PI) (or proportional-integral-derivative [PID]) controller, or as a primer for tuning the gains of the existing PI (or PID) controller,^{53,55,57,59,62,69} and (3) using fuzzy logic for other AGC aspects, such as economic dispatching.⁷⁰ Next, the first two categories are briefly described.

3.2 Neuro-Fuzzy and Neural-Networks-Based AGC

As described in Figure 3.2, to perform a self-tuning/adaptive fuzzy controller, one may use the learning capability of neural networks in the block of a tuning mechanism. This combination provides a type of neuro-fuzzy controller. A neuro-fuzzy controller is a fuzzy controller that uses a learning algorithm inspired by the neural network theory to determine its parameters by processing data samples. The combined intelligent-based AGC design using ANN and fuzzy logic techniques is presented in several works to utilize the novel aspects of both designs in a single hybrid AGC system.^63,71

Neural networks are numerical model-free estimators, which can estimate from sample data how output functionally depends on input without the need for complex mathematical models. An ANN consists of a number of nonlinear computational processing elements (neurons) arranged in several layers, including an input layer, an output layer, and one or more hidden layers in between. Every layer contains one or more neurons, and the output of each neuron is usually fed into all or most of the inputs of the neurons in the next layer. The input layer receives input signals, which are then transformed and propagated simultaneously through the network, layer by layer.

A neuron accepts one or more input signals and produces one output, which is a nonlinear function of the weighted sum of inputs. The mapping from the input variables to the output variables can be fixed by setting all the weights associated with each neuron to some constant. In fact, the training of an ANN in a control structure is a procedure to adjust these values so that the ANN can map all the input control values to the corresponding output control values.

The ANNs with their massive parallelism and ability to learn any type of nonlinearities are also purely used for the design of AGC systems.^14,72–80 A multilayer nonlinear network for supplementary control design using a backpropagation training algorithm is addressed in Beaufays et al.⁷² The ANN control performance is compared with classical PI control design on single-area and two-area power systems. An AGC scheme to incorporate the nonconforming load problem showing an effort to develop algorithms capable of discriminating between noncontrollable short-term and long-term excursions is presented in Douglas et al.¹⁴ The ANN techniques are used for recognition of controllable signals in the presence of a noisy random load.

The AGC system performance is evaluated with a nonlinear neural network controller using a generalized neural structure in Chaturvedi et al.⁷³ An ANN-based AGC system is examined on a four-control area power system considering the reheat nonlinearity effect of the steam turbine and upper/lower constraints for generation rate constraint of the hydro turbine in Zeynelgil et al.⁷⁴

From the control configuration point of view, the most proposed ANN-based AGC designs can be divided into three general control structures that are conceptually shown in Figure 3.5: (1) using the ANN system as a controller to provide control command in the main feedback loop, (2) using ANN for tuning the parameters of an existing fixed structure controller (I, PI, PID, etc.), and finally, (3) using the ANN system as an additional controller in parallel with the existing conventional simple controller, such as I/PI, to improve the closed-loop performance.

The aforementioned three configurations are presented in Figure 3.5a–c, respectively. Backpropagation, which is a gradient descent learning algorithm, is one of the most popular supervised learning algorithms in all mentioned configurations. It backpropagates the error signals from the output layer to all the hidden layers, such that their weights can be adjusted accordingly. Backpropagation is a generalization of the least mean squares (LMS) procedure for feedforward, multilayered networks with hidden layers. It uses a gradient descent technique, which changes the weights between neurons in its original and simplest form by an amount proportional to the partial derivative of the error function with respect to the given weight.

In Figure 3.5b, the ANN performs an automatic tuner. The initial values for the parameters of the fixed structure controller (e.g., k_p, k₁, and k_D gains in PID) must first be defined. The trial and error, and the widely used Ziegler– Nichols tuning rules are usually employed to set initial gain values according to the open-loop step response of the plant. The ANN collects information about the system response and recommends adjustments to be made to the controller gains. This is an iterative procedure until the fastest possible critical damping for the controlled system is achieved. The main components of the ANN tuner include a response recognition unit to monitor the controlled response and extract knowledge about the performance of the current controller gain setting, and an embedded unit to suggest suitable changes to be made to the controller gains.

Combinations of ANN and robust control methodologies are presented for AGC synthesis in interconnected power systems.⁸⁰ These ideas use the robust performance indices provided by robust control techniques for desirable training of neural networks under various operating conditions. These approaches combine the advantage of neural networks and robust control techniques to achieve the desired level of robust performance under large parametric uncertainness and lead to a flexible controller with a relatively simple structure.

Application of ANN to AGC design is comprehensively presented in Chapter 5. The application is supplemented by some nonlinear simulation on single- and multiarea power system examples.

3.3 Genetic-Algorithm-Based AGC

A genetic algorithm (GA) is a searching algorithm that uses the mechanism of natural selection and natural genetics, operates without knowledge of the task domain, and utilizes only the fitness of evaluated individuals. The GA as a general purpose optimization method has been widely used to solve many complex engineering optimization problems over the years. In fact, GA as a random search approach that imitates the natural process of evolution is appropriate for finding a global optimal solution inside a multidimensional searching space. From the random initial population, GA starts a loop of evolution processes, consisting of selection, crossover, and mutation, in order to improve the average fitness function of the whole population.

Several reports are also available on the use of GAs in AGC systems.^41,81–90 GAs have been used to adjust parameters for different AGC schemes, e.g., integral, PI, PID, sliding mode control (SMC),^{82,84,87,89,90} or variable structure control (VSC).^85,86 The overall control framework is shown in Figure 3.6.

An optimal adjustment of the classical AGC parameters for a two-area nonreheat thermal system using genetic techniques is investigated in AbdelMagid and Dawoud.⁸¹ A reinforced GA has been proposed as an appropriate optimization method to tune the membership functions, and rule sets for fuzzy gain scheduling of supplementary frequency controllers of multiarea power systems to improve the dynamic performance. The proposed control scheme incorporates dead-band and generation rate constraints also.

The GA in cooperation with fuzzy logic is used for optimal tuning of the integral controller’s gain according to the performance indices’ integral square error (ISE) and the integral of time multiplied by the absolute value of error (ITAE) in Chang et al.⁸⁴ A reinforced GA is employed to tune the membership function, and rule sets of fuzzy gain scheduling controllers to improve the dynamic performance of multiarea power systems in the presence of system nonlinearities, such as generation rate constraints (GRCs) and governor dead-band. Later, contrary to the trial-and-error selection of the variable-structure-based AGC feedback gains, a GA-based method was used for finding optimal feedback gains.⁸⁵ Parameters of a sliding mode control-based AGC system are tuned in Vrdoljak et al.⁹⁰ using GA in a way to minimize the integral square of the area control error and control signal. Combinations of GA and robust control methods for AGC synthesis in a multiarea power system are shown in Rerkpreedapong et al.⁴¹ The main idea is in running a GA optimization to track a robust performance index provided by robust control theory.

Chapter 11 addresses several GA-based AGC design methodologies and application examples. Chapter 11 shows the way to successfully use GAs for the tuning of control parameters, the solution of multiobjective optimization problems, satisfying robust performance indices, and the improvement of learning algorithms during the AGC synthesis process.

3.4 Multiagent-Based AGC

A multiagent system (MAS) is a system comprising two or more intelligent agents to follow a specific task. Nowadays, the MAS technology is being widely used in planning, monitoring, control, and automation systems. The MAS philosophy and its potential value to the power systems are discussed in McArthur et al.^91,92 Several reports have been published on the application of MAS technology with different characteristics and intelligent cores for the AGC systems.^25,93–98

For a real-time AGC system, structural flexibility and having a degree of intelligence are highly important. In such systems, agents require real-time responses and must eliminate the possibility of massive communication among agents. In the synthesis of real-time MAS, the designer must at least denote the required number and type of agents in the system, the internal structure of each agent, and the communication protocol among the available agents.²⁵ Each agent is implemented on a software platform that supports the general components of the agent. The software platform must provide a communication environment among the agents and support a standard interaction protocol.

Some MAS-based frequency control scenarios using reinforcement learning, Bayesian networks, and GA intelligent approaches are addressed.^93,97,98 A multiagent reinforcement learning-based control approach with the capability of frequency bias estimation is proposed in Daneshfar and Bevrani.⁹⁴ It consists of two agents in each control area that communicate with each other to control the whole system. The first agent (estimator agent) provides the ACE following the area’s frequency bias parameter estimation, and the second agent (controller agent) provides a control action signal according to the ACE signal received from the estimator agent, using reinforcement learning.

Bevrani²⁵ introduces an agent-based scenario to follow the main AGC objectives in a deregulated environment. An agent control system is used to cover a minimum number of required processing activities to the AGC objectives in a control area. The operating center for each area includes two agents: data acquisition/monitoring agent and decision/control agent. In the first agent, special care was taken regarding the provision of appropriate signals following a filtering and signal conditioning process on the measured signals and received data from the input channels. The sorted information and washout signals will be passed to the second agent. The decision/control agent uses the received data from the data acquisition/ monitoring agent to provide the generation participation factors and appropriate control action signal, through an H∞-based robust PI controller. The decision/control agent evaluates the bids and performs the ACE signal using the measured frequency and tie-line power signals. The agent software estimates the total power imbalance and determines AGC participation factors, considering the ramp rate limits. The proposed control structure, which is summarized in Figure 3.7, is examined using real-time nonlinear simulation.

A multiagent-based frequency control scheme for isolated power systems with dispersed power sources such as photovoltaic units, wind generation units, diesel generation units, and an energy capacitor system (ECS) for the energy storage is presented.^95,96 The addressed scheme has been proposed through the coordination of controllable power sources such as the diesel units and the ECS with small capacity. All the required information for the proposed frequency control is transferred between the diesel units and the ECS through computer networks. A basic configuration of the proposed frequency regulation scheme is shown in Figure 3.8. In this figure, W_ECS and P_ECS are the current stored energy and produced power by the ECS unit, respectively. Experimental studies have been performed on the laboratory system to investigate the efficiency of the proposed multiagent-based control scheme.

In Chapter 7, the main frameworks for agent-based control systems are generally discussed. New multiagent-based AGC schemes are introduced, and the potential of reinforcement learning in the agent-based AGC systems is explained. Furthermore, an intelligent multiagent-based AGC design methodology using Bayesian networks is described in Chapter 8. The proposed approaches are supplemented by several simulations, including a real-time laboratory examination.

3.5 Combined and Other Intelligent Techniques in AGC

In the light of recent advances in artificial intelligent control and evolutionary computations, various combined intelligent control methodologies have been proposed to solve the power system AGC problem.^{52,63,71,84,99–106} A study on the AGC synthesis for an autonomous power system using the combined advent of ANN, fuzzy logic, and GA techniques is presented in Karnavas and Papadopoulos.⁶³ In Ghoshal,⁹⁹ a hybrid GA-simulated annealing (SA)-based fuzzy AGC scheme of a multiarea thermal generating system is addressed. A specific cost function named figure of demerit has been used as the fitness function for evaluating the fitness of GA/hybrid GA-SA optimization. This function directly depends on transient performance characteristics such as overshoots, undershoots, settling times, and time derivative of the frequency. The hybrid GA-SA technique yields more optimal gain values than the GA method.

For optimal tuning of PID gains in designing a Sugeno fuzzy-logic-based AGC scheme, a particle swarm optimization (PSO) technique was reported.¹⁰⁰ PSO, as one of the modern heuristic algorithms, is a population-based evolutionary algorithm that is motivated by simulation of social behavior instead of survival of the fittest. The proposed PSO algorithm establishes the true optimality of transient performance, similar to those obtained by the GA-SA-based optimization technique, but it is faster than the GA-SA algorithm. It has been shown in Ahamed et al.¹⁰⁴ that the AGC problem can be viewed as a stochastic multistage decision-making problem or a Markov chain control problem, and algorithms have been presented for designing AGC based on a reinforcement learning approach.

The reinforcement learning (RL) approach is used to AGC design a stochastic multistage decision problem in Ahamed et al.¹⁰⁴ Two specific RL-based AGC algorithms are presented. The first algorithm uses the traditional control objective of limiting ACE excursions, while in the second algorithm the controller can restore the load-generation balance by monitoring deviation in tie-line flows and system frequency without monitoring ACE signal. In Karnavas and Papadopoulos,¹⁰⁵ an intelligent load-frequency controller was developed using a combination of fuzzy logic, genetic algorithms, and neural networks to regulate the power output and system frequency by controlling the speed of the generator with the help of fuel rack position control.

Some heuristic stochastic search techniques such as GA, PSO, and bacteria foraging optimization have been proposed for optimization of PID gains used in Sugeno fuzzy-logic-based AGC of multiarea thermal generating plants.¹⁰³ An attempt is made to examine the application of bacterial foraging to optimize some AGC parameters in interconnected three unequal area thermal systems.¹⁰⁶ The parameters considered are integral controller gains for the secondary control, governor speed regulation parameters for the primary control, and frequency bias. The system performance is compared with the GA-based approach and classical methods.

3.6 AGC in a Deregulated Environment

In a traditional power system, generation, transmission, and distribution are owned by a single entity called a vertically integrated utility, which supplies power to the customers at regulated rates. Usually, the definition of a control area is determined by the geographical boundaries of the entity. Toward the end of the twentieth century many countries sought to reduce direct government involvement and, to increase economic efficiency, started to change the power system management structure, often described as deregulation.

There are several control scenarios and AGC schemes depending on the power system structure. Different organizations are introduced for the provision of AGC as an ancillary service in countries with restructured power systems. The AGC service and related transactions can be supervised by an independent system operator (ISO), independent contract administrator (ICA), transmission system operator (TSO), or another responsible organization. The type of AGC scheme in a restructured power system is differentiated by how free the market is, who controls generator units, and who has the obligation to execute the AGC.¹⁰⁷

Several modeling and control strategies have been reported to adapt well-tested classical load-frequency control (LFC) schemes to the changing environment of power system operation under deregulation.^25,108–113 A generalized modeling structure based on the introduced idea in several references^25,112,114 is presented in Chapter 4. The effects of deregulation of the power industry on AGC, several AGC schemes, and control scenarios for power systems after deregulation have been addressed.^{25,107,113–121}

An agent-based AGC scenario in a deregulated environment is introduced in Bevrani.²⁵ In order to cover a minimum number of required processing activities for the AGC objectives in a control area, a two-agent control system is used. Based on the proposed control strategy, a decision and control agent uses the received data from a data acquisition and monitoring agent to provide the generation participation factors and appropriate control action signal, through a simple robust controller. The system frequency has been analytically described, and to demonstrate the efficiency of the proposed control method, real-time nonlinear laboratory tests have been performed.

A decentralized robust AGC design in a deregulated power system under a bilateral-based policy scheme is addressed in Bevrani et al.¹²⁰ In each control area, the effect of bilateral contracts is taken into account as a set of new input signals in a modified traditional dynamical model. The AGC problem is formulated as a multiobjective control problem via a mixed H2/H∞ control technique. In order to design a robust PI controller, the control problem is reduced to a static output feedback control synthesis, and then it is solved using a developed iterative linear matrix inequalities algorithm to get a robust performance index close to a specified optimal one. The results of the proposed multiobjective PI controllers are compared with H2/H∞ dynamic controllers.

Chapter 4 reviews the main structures, characteristics, and existing challenges for AGC synthesis in a deregulated environment. The AGC response and an updated AGC mode concerning the bilateral contracts are also introduced in Chapter 4.

3.7 AGC and Renewable Energy Options

The power system is currently undergoing fundamental changes in its structure. These changes are associated not just with the deregulation issue and the use of competitive policies, but also with the use of new types of power pro duction, new technologies, and rapidly increasing amounts of renewable energy sources (RESs).

The increasing need for electrical energy, as well as limited fossil fuel reserves, and the increasing concerns with environmental issues call for fast development in the area of RESs. Renewable energy is derived from natural sources such as the sun, wind, hydropower, biomass, geothermal, and oceans. These changes imply a requirement for new AGC schemes in modern power systems. Recent studies have found that the renewable integration impacts on system frequency and power fluctuation are nonzero and become more significant at higher sizes of penetrations. The variability and uncertainty are two major attributes of variable RESs that notably impact the bulk power system planning and operations. The main difficulties are caused by the variability and lim ited predictability of power from renewable sources such as wind, PV, and waves.

Integration of RESs into power system grids has impacts on optimum power flow, power quality, voltage and frequency control, system economics, and load dispatch. The effects of variability are different than the effects of uncertainty, and the mitigation measures that can be used to address each of these are different. Regarding the nature of RES power variation, the impact on the frequency regulation issue has attracted increasing research interest during the last decade. Some studies represent a range of estimates based on different system characteristics, penetration levels, and study methods.²⁵

The RESs have different operational characteristics relative to the traditional forms of generating electric energy, and they affect the dynamic behavior of the power system in a way that might be different from conventional generators. This is due to the fact that the primary energy source in most RES types (such as wind, sunlight, and moving water) cannot presently be controlled or stored. Unlike coal or natural gas, which can be extracted from the earth, delivered to power plants thousands of miles away, and stockpiled for use when needed, variable fuels must be used when and where they are available. This fact alone results in RESs being viewed as nondispatchable, implying it is not possible to a priori specify what the power output of a RES unit should be.

3.8 AGC and Microgrids

A microgrid is a relatively novel concept in modern electric industry, consisting of small power systems owning the capability of performing isolated from the main network. A microgrid can tackle all distributed energy resources, including distributed generation (DG), RESs, distributed energy storage systems, and demand response, as a unique subsystem, and offers significant control capacities on its operation. Microgrids are usually based on loads fed through a low- or medium-voltage level, mostly in distribution radial systems. In order to ensure proper operation of the microgrid, it is important that its constituent parts and controls in both grid-connected and islanded modes operate satisfactorily.

A schematic diagram of a generic microgrid is shown in Figure 3.9. The microgrid is connected to the main grid at the point of interconnection (POI). The DG units of the microgrid can in general have any arbitrary configuration. Each DG unit is usually interfaced to the microgrid through a power electronic converter, at the point of coupling (POC). As mentioned, many of the DGs/RESs, such as PV and fuel cells, use a power electronic converter (inverter) for grid interfacing. Some wind applications as well as some synchronous machines and micro-turbines utilize power electronic devices for the grid interface, as the benefits of the electronic interface justify the additional cost and complexity.

Power electronic inverters are capable of converting the energy from a variety of sources, such as variable frequency (wind), high frequency (turbines), and direct energy (PV and fuel cells). Inverter-based DGs/RESs are generally considered low power by utility standards, from 1 kW up to a few MW. Generators connected to renewable sources are not reliable, and so are not considered dispatchable by the utility and are not tightly integrated into the power supply system. The inverter interface decouples the generation source from the distribution network. Since inverters monitor the frequency at their output terminals for control purposes, it is easy to detect when the inverter frequency shifts outside a window centered on the nominal frequency set point.¹²⁹

Increases in distributed generation, microgrids, and active control of consumption open the way to new control strategies with a greater control hierarchy/intelligence and decentralized property. In this direction, recently several new concepts and national proj ects, such as SmartGrids,¹⁴⁶ Intelligrid,¹⁴⁷ and Gridwise,¹⁴⁸ have been defined in Europe and the United States. Their aim has been to take advantage of the possibilities created by combining intelligent communication, information technology, and distributed energy resources in the power system.

Similar to conventional generating units, droop control is one of the important control methods for a microgrid with multiple DG units. Without needing a communication channel and specific coordination, the DG units can automatically adjust their set points using the frequency measurement to meet the overall need of the microgrid.¹⁴⁹

Unlike large power systems, the drooping system is poorly regulated in microgrids to support spinning reserve as an ancillary service in power markets. Some recent works address the scheduling of the droop coefficients for frequency regulation in microgrids.^150,151 A methodology based on bifurcation theory is used in Chandorkar et al.¹⁵¹ to evaluate the impact that droops and primary reserve scheduling have on the microgrid stability. It was demonstrated in Diaz et al.¹⁵⁰ that drooping characteristics can be successfully applied for controlling paralleled inverters in isolated alternative current (AC) systems, mimicking the performance of generator-turbine-governor units.

On the other hand, tie-line control can be designed to manage the feeder power flow at the POI to meet the needs of the system operator. Control is implemented by coordinating the assets of the microgrid, allowing the collection of these assets to appear as one aggregated dispatchable producing or consuming entity connected to the main grid. The overall objective is to optimize operating performance and cost in the normally grid-connected mode, while ensuring that the system is capable of meeting the performance requirements in stand-alone mode.¹⁵² Enforcing power and ramp rate limits at the POI with appropriate responses to system frequency deviation can be achieved via microgrid active power control. It is noteworthy that unlike standard AGC implementation, tie-line control is not applicable when an isolated microgrid is considered.

Another major issue in the area of microgrid control is the move toward active control of domestic loads. However, the flexibility introduced through the ability to control part of household power consumption is difficult to exploit, and up to now there have been no significant achievements.

As mentioned above, microgrids and DGs have great potential in contributing to the frequency control of the overall system. The main challenge is to coordinate their actions so that they can provide the regulation services. For this purpose, the aggregation technique to create virtual power plants (VPPs) could be useful. In this method, the aggregation is not based on the topology of the network; instead, any unit can participate in a VPP, regardless of its location. It facilitates the provision of some services (by VPPs), such as frequency regulation and power balance, which are global characteristics.

The possibility of having numerous controllable DG units and microgrids in distribution networks requires the use of intelligent and hierarchical control schemes that enable efficient control and management of this kind of system. During the last few years, several reports presenting various control methods on frequency regulation, tie-line control, and LFC/AGC issues have been published.^152–158 A PI control, following estimation of power demand (amount of load disturbance), is used in Fujimoto et al.¹⁵³ The method is applied on a real microgrid, including PV, micro-hydropower generator, diesel generator, storage system, and wind turbine. The tie-line controls are important microgrid control features that regulate the active and reactive power flow between the microgrid and the main grid at the point of interconnection, and are addressed in Adamiak et al.¹⁵² These controls essentially allow the microgrid to behave as an aggregated power entity that can be made dispatchable by the utility. Molina and Mercado¹⁵⁴ have used a combination of SMES and a distribution static synchronous compensator to control the tie-line power flow of microgrids. An electrolyzer system with a fuzzy PI control is used in Li et al.¹⁵⁹ to solve power quality issues resulting from microgrid frequency fluctuations.

Fuzzy logic PID controllers for frequency regulation in isolated microgrids are given in Chaiyatham et al.¹⁵⁵ and Hiyama et al.¹⁵⁸ A bee colony optimization is used in Chaiyatham et al.¹⁵⁵ to simultaneously optimize scale factors, membership function, and control rules of the fuzzy controller. A general and a hierarchical frequency control scheme for isolated microgrid operation are addressed in Madureia et al.¹⁵⁶ and Gil and Pecas Lopes,¹⁵⁷ respectively.

3.9 Scope for Future Work

Restructuring and introducing new uncertainty and variability by a significant number of DGs, RESs, and microgrids into power systems add new economical and technical challenges associated with AGC systems synthesis and analysis. As the electric industry seeks to reliably integrate large amounts of variable generation into the bulk power system, considerable effort will be needed to accommodate and effectively manage these unique operating and planning characteristics. A key aspect is how to handle chang es in topology caused by switching in the network, and how to make the AGC system robust and able to take advantage of the potential flexibility of distributed energy resources.

3.10 Summary

The AGC design problem in power systems can be easily transferred into a performance optimization problem, which is suitable for application of artificial intelligent techniques. This has led to emerging trends of application of soft computing or computational intelligence and evolutionary computing in the power system AGC synthesis issue.

In this chapter, the most important issues on the intelligent AGC with the past achievements in this literature are briefly reviewed. The most important intelligent AGC design frameworks based on fuzzy logic, neural network, neuro-fuzzy, genetic algorithm, multiagent system, and other intelligent techniques and evolutionary optimization approaches are described. An overview of the key issues in the power system intelligent AGC—deregulation and integration of RESs/DGs and microgrids—is presented. The need for further research on the intelligent AGC and related areas, including the necessity of revising existing performance standards, is emphasized.

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