## 4 GWO's Functions in the Distribution Network
It has been shown that different case studies can be used to investigate the positive effects of DFR on a radial distribution system with 69 buses [60]. Figure 4 depicts a typical configuration of a doubly fed induction generator (DFIG). This figure contains two types of controllers, GSC and RSC. The two types contain executable code related to the GWO algorithm. The usage of unified power quality conditioners is utilized to demonstrate multi-objective planning for reactive power correction in radial distribution networks that are powered by wind energy (UPQC). It is decided to use the UPQC model, which is based on phase angle control (PAC). Achieving optimal performance of the UPQC-PAC requires concurrently decreasing numerous objective functions, such as grid power loss, the fraction of nodes suffering voltage drops, and the capacity of UPQC. The model that has been proposed is a non-linear optimization problem that is very difficult to solve. A probabilistic load flow technique based on a multi-objective grey wolf optimizer (MOGWO) is used to present a group of non-dominated solutions, and then a fuzzy set theory is used to determine the optimal response [61]. A simulation of a 33-bus distribution network was used to investigate the reliability of the method that was previously mentioned. A smart distribution system's aim of active power loss reduction and voltage stability improvement is realized by suitable reconfiguration of the system, which is accomplished during the design phase. Renewable distributed generation (RDG) systems are distributed generation (DG) systems equipped with wind turbines and solar panels, among other items. Due to the availability of smart metering devices, it is possible to assess the present health of the network with pinpoint precision at any moment in time. Network conditions are taken into consideration to develop the optimal architecture. With the help of a newly proposed technique known as GWO, which is a version of the grey wolf algorithm, we were able to solve the problem of optimization. The validation condition in GWO is a retrieval validation, which entails comparing real retrieval products to values based on independent direct measurements of the same geophysical parameter. In situ measurements have been precise for validation purposes. To account for \"smoothing error\" and a priori information influence in retrieval products, the true profile is transformed as: \(x_{sim}=x_{a}+A\ (x_{true}-x_{a})\). \(x_{sim}\) is the simulated retrieved profile, \(\mathbf{x_{true}}\) is the true profile, \(\mathbf{x_{a}}\) is the a priori profile, and \(\mathbf{A}\) is the retrieval averaging kernel matrix. The elements of \(\mathbf{x}\) linked with surface temperature and emissivity have been ignored since they are not dependable and independent [62]. Figure 4: A typical configuration of a DFIG. To simultaneously minimize active power losses and utility operating costs while taking into consideration the uncertainties associated with vehicle load, it has been recommended to implement a demand response program and reconfiguration strategy that are integrated into one another. A demand response program based on a load management contract is created to control load consumption, and the GWO is employed for reconfiguration to optimize the configuration. The use of a conventional 69-bus system is being considered for authenticating the scheduled work. Losses were reduced by 57.2 percent with the implementation of reconfiguration and demand response, resulting in economic gains for both customers and utility companies [63]. Figure 5 explains how to reconfigure a distribution network (DN) using distribution network (DN) reconfiguration. For the charging station to maintain a healthy voltage profile and reduce power loss to the bare minimum, it must be placed at the most appropriate node possible. The assignment of electric vehicle charging stations in an IEEE 33-node radial distribution network has been accomplished.