# On the Contribution of Wind Farms in Automatic Generation Control: Review and New Control Approach
## 3 Wind Farms Contribution in AGC
### Applied Control Methods  
Optimization of the parameters in a PI controller structure is one of the basic methods to reach efficient control performance. Simple parameter optimization based-on integral of square error index was applied to the PI controller in the wind turbine structure to coordinate the wind turbine with AGC system of an isolated power system, as reported in Reference [43]. The efficiency of DFIG's contribution in AGC of isolated power system was demonstrated by a high level of wind farm penetration. Similar methods were used in Reference [44], to prove the DFIGs contribution to the AGC of multi-area power systems. Dynamic participation of DFIG-based wind farms in an optimal AGC is demonstrated in Reference [45], which is effective to reach higher a stability margin and smooth the frequency response with a suitable damping. These studies showed the contribution of the DFIGs in the AGC of power systems; however, the simple method for the optimization process is the deficiency. A range of metaheuristic optimization methods are utilized in wind turbines to contribute to the AGC [46; 47; 48; 49; 50; 51; 52]. Ant colony optimization (ACO) approach [46], craziness-based particle swarm optimization (CRPSO) [47], improved particle swarm optimization (IPSO) [48], opposition learning based gravitational search algorithm (OGSA) [49], genetic algorithm (GA) [50], non-dominated sorting genetic algorithm-II (NSGAII) [51], and non-dominated Cuckoo search algorithm (NSCS) [52] are successfully applied to classic PI controller of VSWT wind turbines to contribute in the AGC. In Reference [53], ant lion optimization (ALO) is proposed to optimize the parameters of a new controller known as trajectory following controller in the structure of wind turbines. Efficiency of the proposed controller was shown over optimized PI and PID controllers in minimizing the settling time and peak overshoot of frequency performance. Intelligent methods like fuzzy logic and artificial neural network (ANN) are applied to automatic generation control of wind integrated power systems [54; 55; 56; 57]. Simplicity in the design process for fuzzy logic approach has made it impressive to apply in VSWT wind turbines. In Reference [54], a fuzzy-based PI controller is designed for DFIG-based wind farms to contribute in AGC of a two-area power system. It is shown that utilizing fuzzy approach can be more effective compared to a simple PI controller in the frequency regulation under different load changes and wind penetration levels. However, the fuzzy logic method is a suitable choice for frequency control of wind integrated power systems, the expert knowledge base design process of the fuzzy logic may deteriorate its performance. For this reason, in Reference [55], a combined Jaya algorithm (JA)-fuzzy logic based proportional integral differential (PID) controller is proposed for the LFC of three area power system integrated with wind farms. Trained for a wide range of operation conditions and load changes, a non-linear recurrent ANN is demonstrated in Reference [56] using off-line data for the wind farm contribution in the AGC of multi-area power system. Better frequency performance in terms of lesser undershoot and settling time, and faster oscillation damping compared to the conventional PI controller are the superiority of the proposed controller. As another class of supervised learning-based controller, a least square support vector machine method is proposed in Reference [57] for the AGC in a wind integrated multi-area power system. Compared to multi-layer perceptron neural network based AGC, the proposed controller is efficient for the frequency control purpose. The model predictive control (MPC) approach has been utilized for an AGC system, incorporated with wind farms successfully [58; 59; 60; 61]. The operation process through MPC is an optimization process that can be defined at each time instants.