Load Frequency Control (LFC) is an important issue in stabilizing the system frequency under different types of disturbance. A two-area system with a controller in each area to enhance the performance in the transient duration is commonly used in the previous literature [
1]. The Area Control Error (
ACE) is an index to measure the deviation in frequency and power flow through a tie line. The
ACE can be presented as in Equation (1) for area number
i for several areas system.
where Δ
Pij is the power flow through tie line between areas number
i and
j and
Bi is the frequency biasing coefficient of area number
i. Several controllers are presented in the literature to enhance the transient performance of the electrical power system through optimization algorithms. Proportional-Integral (PI) controller and Integral (I) controller are used in [
1]. Tunning of controller parameters is carried out by Hybrid Particle Swarm Optimization to minimize the performance index (μ) given by,
where α, β, and γ are constants and
and
are changing in area
i frequency, change in area ii frequency, and changing in tie power, respectively. The results show that PI succeeded in enhancing the system damping. The adaptive neuro-fuzzy inference system is applied to the three areas system, and it has a fast response to keep the frequency at its nominal value [
2]. The authors of [
3] show the superiority of the Fuzzy logic controller over the classical PI in terms of fast response. Fuzzy logic controller is also used in [
4], where the automatic generation control (AGC) includes superconducting magnetic energy storage. The results illustrate the capability of the Fuzzy controller to damp system oscillations. The PID controller designed by maximum peak resonance specification is presented in [
5] which is superior to the classical controller in enhancing AGC performance.
The authors of [
6] compared between fractional order PID and integral order PID to enhance the AGC performance in a two-area system. The fractional order PID had better performance than the integral order PID. Integral-double derivative offers faster dynamic response than several classical controllers used for AGC in multi-area systems [
7]. Imperialist competitive algorithm (ICA) is utilized to optimize the parameters of the robust PID controller, and the results show its superiority compared to classical PI controller optimized by Genetic Algorithm (GA) and neural network [
8]. Artificial neural networks ANN are applied for four-area systems and succeed in improving the AGC performance as presented in [
9]. The GA based controller is used to enhance the performance of AGC [
10]. The authors of [
11] used Bacteria Foraging optimization algorithm to optimized PI controller to enhance the LFC in a two-area system. The results show that the Bacteria Foraging optimization algorithm had better performance than GA used for optimization of PI parameters. Fuzzy logic based Integral (I) controller are proposed in [
12] and compared with classical PI controller. Results showed that the fuzzy logic based Integral (I) controller has lower overshoot and settling time than the classical PI controller. Particle swarm optimization (PSO), harmony search algorithm, cuttlefish algorithm, and emperor penguin optimizer are proposed to optimize parameters of the PI controller in an MPPT system [
13,
14,
15,
16]. The results show that these techniques succeed in improving the transient performance of the area frequency and the power in the tie line. The authors of [
17] used an optimal output feedback method to enhance the dynamics of the AGC for two-area systems. The method succeeds in minimizing the performance index. Genetic fuzzy gain scheduling controller is presented in [
18] and succeeded in enhancing the AGC performance of two-area systems. The authors of [
19] enhanced the AGC performance of two-area systems by using a differential evolution-based tilt integral derivative controller with a filter and compared its performance with several optimization techniques. Adaptive weighted particle swarm based multi-objective PID controller is presented in [
20] and results showed that this method is very fast compared with GA and PSO techniques. The authors of [
21] used differential evolution algorithm to enhance AGC with considering nonlinearity as governor dead band. A more adequate technique based on expanding the transfer function of the controller using Laurent series is carried out in [
22] to enhance the performance of a single-area AGC. The Firefly Algorithm proved its superiority to different optimization techniques in enhancing the controller performance of two-area AGC systems [
23]. Gravitational search algorithm and differential evolution are used for the AGC of two-area systems [
24,
25]. The transient response of the three-area AGC system is improved by using the PSO based Multi-stage Fuzzy logic PID controller [
26]. Kharitonov’s theorem and stability boundary locus are used for controller design of AGC to damp the system oscillations [
27]. Furthermore, the PI controller is used for the AGC system of photovoltaic and thermal generators [
28]. In addition, the PID controller is used for the AGC with AC-DC tie line [
29]. It is shown that the tilt integral derivative is superior to classical controllers [
30]. Moreover, the flower pollination based fractional order controller is used to enhance the LFC [
31]. Furthermore, other techniques such as PID based on double-derivative controllers, BAT algorithm based cascaded PD and PID controllers, Cuckoo search based two degree of freedom controller, and Proportional-Integral-Derivative and Acceleration (PIDA) controller optimized by Teaching-Learning-Based-Optimizer (TLBO) are used for LFC enhancing [
32,
33]. The authors [
34] proposed an approach based on reference Offset Governor method to enhance the load frequency control system. While, the authors [
35] proposed a PI controller tuned using a genetic algorithm to enhance the load frequency control system performance. Moreover, the authors of [
36] proved that the DGs have an important role in enhancing the power system performance.
In most of the literature, although control strategies succeeded in enhancing the LFC system performance, the controller needs to be readjusted if the disturbance is changed. Moreover, disturbances caused by the intermittent nature of renewable energy such as wave energy are not considered, even with its high penetration nowadays. Furthermore, most of the presented LFC systems in the literature depended on off-line optimization techniques which cannot follow the disturbance due to the load variations. Moreover, optimization algorithms experience a long time of implementation. To overcome the aforementioned problems, this paper presented an LFC system based on an adaptive controller. The proposed method is examined with the Wave Energy Conversion System (WECS) under load variation and wave disturbances. The dynamic performance of the proposed controller is compared with previous work [
32] where optimization techniques are applied for LFC of the same system model utilized in this paper. Finally, the reliability of the system is examined through the hybrid PV-thermal nonlinear system.