In this paper an optimized Fuzzy based controller is proposed for automatic generation control of two area hydro-thermal power system connected to the wind farm. The parameters and membership functions of the proposed controller are optimized by a modified version of Cuckoo search algorithm (CSA). Also, a weighted objective function is proposed to minimize the frequency deviation and transmission power oscillation. The suggested heuristic objective function is a weight function from maximum frequency drift and oscillations fading time. To assessing the performance of suggested controller, studies is accomplished by two different scenarios. In the first scenario, the simulations are performed without wind farm and in the second scenario, the simulation is done in the presence of a wind farm. The simulation results indicate that the wind farm presence has major effect on sustained improvement of power system and the reason is considering the load variations in the area, the demand electrical energy in the same area is provided by the wind farm and hence, the frequency oscillations are decreased in both areas.

In this paper, an improved version of the particle swarm optimization algorithm is proposed for the online tuning of power system stabilizers in a standard four-machine two-area power system to mitigate local and inter-area mode oscillations. Moreover, an innovative objective function is proposed for performing the optimization, which is a weight function of two functions. The first part of fitness is the function of the angular velocity deviation of the generators, and the other part is a function based on the percentage of undershoot and maximum overshoot, and also the damping time of the power system oscillations. The performance of the proposed stabilization method is compared with the genetic algorithm and bacteria foraging algorithm results. Simulations are made in three different power system operation conditions by changing the system load. The simulation results indicate the superiority of the proposed method over the genetic algorithm and bacteria foraging algorithm. In all the scenarios, power system oscillations are damped faster and with lower amplitude when the power system stabilizers coordinate with the proposed optimization method.

In this paper, a modified version of Imperialist Competitive Algorithm (ICA) is proposed for the optimal energy management of a Microgrid (MG) with Parking Lots (PL) and Distributed Generation (DG) units. A 24-h scheduling for participation in DG units and electric vehicles PLs in two scenarios is done. The PLs are divided into seven group that each group has different trip behavior. Therefore, energy management should be done in such a way as to minimize operating costs according to the charging status of electric vehicles as well as the production capacity of distributed generation sources. Finally, the results of the two scenarios are reviewed separately and compared. The simulation results proved the effectiveness of the proposed method. The MG operation cost is decreased about 63%. Also, the optimization results. The optimization results by the proposed ICA algorithm are compared with the results of genetic algorithm (GA) and particle swarming optimization (PSO) algorithms. The optimization results confirm better performance of the proposed algorithm compared to GA and PSO algorithms.

Full capacity cannot be exploited in power systems for various reasons, including voltage constraints and various types of stability. Creating a competitive open market in refurbished systems requires that the maximum capacity of the lines be used. Using the maximum capacity of the lines may cause overload on some lines beyond their capacity. This phenomenon may result in some limits being exceeded. Therefore, it is necessary to manage the line load correctly. One of the suggested ways for this purpose is to use FACTS devices in the power system. The various capabilities of the FACTS devices have made it possible to set different targets to determine their optimal location and position. Some of the most important placement targets include increasing voltage stability, improving voltage profiles, reducing losses, increasing line capacity limit and reducing fuel costs for power plants through optimal power distribution. In this paper, the placement of FACTS devices in the power system is conducted to increase the load capacity limit of the transmission line, as well as reducing the losses using the artificial bee colony algorithm. The artificial bee colony algorithm with high accuracy and high convergence speed is suitable for conducting FACTS placement studies. Finally, a comparison was made between the artificial bee colony algorithm and two particle swarm and genetic algorithms. The results indicate that the artificial bee colony algorithm works better than the other two algorithms in minimizing the objective function.

With the growing demand for electricity, the inability of governments to provide the necessary resources to invest in the electricity industry and the rising price of fossil fuels, the tendency is to study and pay attention to economic issues in power systems studies. In this paper a new modified version of gray wolf optimization (MGWO) is proposed to solve the unit commitment (UC) problem in a power system in case of uncertainty. Market price variation is the main source of uncertainty in the UC program. Therefore, a model based on normal probability density function (PDF) is present for reducing the market price uncertainty effect in the model. Simulations are done for a standard 10 thermal units power system, and the results of the optimization by the proposed MGWO are compared with the previous version of the GWO algorithm and particle swarm optimization (PSO) algorithm results. The simulation results confirm the superiority of the proposed MGWO algorithm over the two algorithms PSO and GWO.

Reactive power compensation is one of the practical tools that can be used to improve power systems and reduce costs. These benefits are achieved when the compensators are installed in a suitable place with optimal capacity. This study solves the issues of optimal supply and the purchase of reactive power in the IEEE 30-bus power system, especially when considering voltage stability and reducing total generation and operational costs, including generation costs, reserves, and the installation of reactive power control devices. The modified version of the artificial bee colony (MABC) algorithm is proposed to solve optimization problems and its results are compared with the artificial bee colony (ABC) algorithm, the particle swarm optimization (PSO) algorithm and the genetic algorithm (GA). The simulation results showed that the minimum losses in the power system requires further costs for reactive power compensation. Also, optimization results proved that the proposed MABC algorithm has a lower active power loss, reactive power costs, a better voltage profile and greater stability than the other three algorithms.