## 1 Introduction
This is so that they can actively cut down on power loss. The IEEE 33-bus test system can be used to test a wide range of renewable energy generators (REGs) of different sizes and types. The size of REGs is based on how much power is lost in the distribution system. The best-distributed generator has been found and the right generator size has been chosen so that there are no power distribution losses [20]. Several studies have been carried out to find the best place for a REG based on factors such as active and reactive power loss and the overall efficiency of power systems, among other things. VSI performance improvement has also been investigated, using an online adaptive switching frequency technique to meet its performance goals (OASF). A new level of freedom has been added to the OASF algorithm to obtain the highest possible weighting factor and the best overall system optimization. Compensated math considers both the controller's reaction time and the inductance of the coupler [21]. A power-consumption limit is needed because there is not enough active power in the power system. This causes the supply network's frequency to drop because there is not enough active power in the system. Customer service and the technology used to make oil and its byproducts are damaged, as is the way that oil is made. Random disconnections of oilfield users should be avoided to keep the oil industry from going broke [22]. Active power losses must be kept to a minimum while the radial network works, and each node of the grid maintains a balance of power. To do this, the grid must be treated as an integer/mixed-integer linear programming problem. The authors generated a new metaheuristic optimization method that solves one of the most common problems in today's power systems. It was based on a lot of different things people do in a lot of different industries [23; 24]. It has a high rate of convergence because the roulette wheel selection mechanism is used. The IEEE 30-bus system is used to obtain the most power. This has become the norm in the business world. There is an algorithm called Human Behavior-Based Optimization (HBBO) that helps find a way to solve the problem at hand. All these things are part of the problem with the OPF. People who know how to do things such as particle swarm optimization (PSO) and other things can try them out when HBBO is used [25]. A new method of dividing transmission losses has been discovered in a deregulated energy market. It is difficult to calculate the amount of loss in a shared transmission corridor because it is not a simple number [26]. Lossy lines are given transmission losses based on the amount of power that passes through them [27]. As this strategy uses real power flow in transmission lines for each load to allocate losses, the allocations made by the strategy make sense and are appropriate for each load connected. _GWO as an Effective Metaheuristic Algorithm in Problem Optimization_  
GWO is preferred when the communication range and number of objectives are increased. It is easy to implement due to its simple structure, fewer storage and computational requirements, faster convergence due to continuous reduction in search space and fewer decision variables; its ability to avoid local minima; and having only two control parameters to tune the algorithm's performance, which results in better stability and robustness. Increasing the number of objectives and communication ranges can significantly improve the system performance in GWO and thus improve the algorithm performance [28]. The pseudocode of the GWO has been presented in [29] as follows (Algorithm 1):GWO is a well-known algorithm that has been designed to solve single-objective optimization problems. It can be used in different ways to solve the problem of active power loss. In this article, we show how GWO works and how it can be applied to power systems.