## 1 Introduction  
Electricity generated from renewable energy resources (RESs) is used as grid-on or grid-off power. Sudden climatic changes can cause large changes in their output parameters. This situation is also seen in PV-SPP systems, because of shading. Their output voltages and frequencies are affected by this [1]. In the case of large frequency or voltage changes, the network is in danger of collapse. Moreover, the quality of the energy also deteriorates. These situations can be harmful to both the grid and the consumers [2]. One way to eradicate this kind of problem is through load frequency control (LFC). LFC is one of the most important processes during the operation of a power system. The energy balance between the generation and loads is controlled by means of a numerical value obtained from sensors that measure the grid frequency [3,4]. For this reason, to solve the load frequency problem, several control methods have been proposed by previous researchers. The aim of these studies has been to maintain the frequency at a desired value. Another problem is that multi-area power systems have a large number of parameters that must be controlled and have enormous, complex parts. Therefore, the control procedures and operation of the systems are more complex. In particular, it is quite difficult to stabilize the frequency in each area, because of the increase of the multi area. Consequently, more intelligent controllers have to be designed for LFC [5,6]. The most common control strategy for such systems is the PID controller. Many system instabilities can be managed by the conventional PID controller. However, the conventional PID controller is inadequate, owing to the expansion of gridsand the rapidly increasing energy demands [7]. The utilization of different modern optimization methods embedded in the conventional PID controller is recommended for the load frequency control, by some researchers [8]. The cuckoo search algorithm (CSA) and intelligent bee colony optimization (IBCO) are just two of the many examples of these methods [6; 9; 10]. [PERSON] et al. compared an emotional controller with the conventional PID controller [11]. A sugeno fuzzy logic controller (S-FLC), optimized by particle swarm optimisation (PSO), was applied by [PERSON] et al. [12]. For micro grid networks with photovoltaic (PV) systems, battery units, micro wind turbines (MWT), and fuel cells, the most suitable control method is a self-adjusting control method [11; 13; 14]. Hence, [PERSON] et al. realized a genetic algorithm (GA) for LFC in a two area interconnected hydro-thermal power system [15]. Additionally, another comparative study presented a controller employing linear matrix inequalities (LMI) and a controller optimized GA (GALMI) for a three area interconnected power system, simulated by [PERSON] et al. [16]. They achieved a robust LFC performance. In addition to these, there are also studies that adjust the membership functions of the fuzzy logic controller (FLC), using a GA [17]. [PERSON] and [PERSON] actualized a FLC process with a GA that was applied to a grid network which had two or three areas, in a classic or hybrid power system [18]. For a three area interconnected power system, a controller using an artificial neural network (ANN) was designed to regulate the load frequency imbalance, by [PERSON] et al. [19]. Moreover, optimization methods embedding flexible alternative current transmission systems (FACTS) were executed on multi area interconnected power systems, in some studies. In the systems, the effects of the FACTSs on the frequency of the line were monitored [20; 21]. As the world rapidly changes, the development of electric grids increases the importance of choosing the best control method. Since the frequency value continuously changes, depending on the energy consumption, it is more difficult to maintain the desired frequency value [22; 23].