In this paper, a new method of reactive power compensation is proposed for reducing power loss of distribution power networks. The new method is the combination of local compensation at each load and distribution line compensation. In the method, local capacitors at each load are determined to increase power factor of load to an expected value first and then a number of capacitors are placed in distribution lines with two factors, location and capacity by using the three applied methods. Particle swarm optimization (PSO), parasitism predation algorithm (PPA), and tunicate swarm algorithm (TSA) are applied for the proposed method on four distribution systems with 15, 33, 69, and 85 buses. The comparisons of results with previous methods indicate that the three applied methods can reach higher loss reduction for all study cases. TSA can reach loss reduction more effectively than others by 19.4%, 0.5%, 7.9%, and 10.76% for the four distribution systems, respectively. The most important emphasis is that PSO, which was considered to be of low effectiveness in previous studies, can reach much better loss than approximately all previous methods thank to the proposed compensation method. PSO also reached better loss reduction than others by 18.97%, 0.4%, 7.73%, and 10.21%, respectively. Consequently, it is recommended that the proposed compensation method is useful for distribution systems in minimizing total power loss and TSA can be selected a new and powerful method for the problem.

Radial distribution power systems (RDPSs) are essential in the power system to supply electricity to loads for developing industries, commerce, and services in countries. However, RDPSs have many distribution lines with high resistance, causing high active power loss. So, the core target of the study is to reduce the total active power loss, considering the highest penetration level of installed PVUs. Photovoltaic units (PVUs) are placed optimally by implementing the Coot optimization algorithm (COOA), the archimedes optimization algorithm (AOA), the transient search optimization algorithm (TSOA), the crystal structure algorithm (CrSA), the war strategy optimization algorithm (WSA), and the average and subtraction-based optimizer (ASBO). Two test RDPSs with 33 and 69 nodes are employed for four different study cases, including (1) the number of PVUs is three and the power factor is 1.0; (2) the number of PVUs is three, and the power factor is optimally determined; (3) the number of PVUs is determined optimally while the power factor is 1.0; and (4) the number of PVUs and the power factor of PVUs are optimally determined. For the first system, COOA can reach smaller power losses than others by 0.14% to 13.059% for Case 1, 0.5% to 77.89% for Case 2, 0.101% to 10.126% for Case 3, and 11.818% to 88.492% for Case 4. For the second system, COOA can reach a lower loss than others by 0.164% to 15.511% for Case 1, 0.774% to 78.6% for Case 2, 0.173% to 15.104% for Case 3, and 46.201% to 95.389% for Case 4. Especially in Case 4, the power loss is approximately equal to zero. The loss reduction is the first significant contribution of the study. In addition, the study also investigated the difference between the four cases for obtaining power loss reduction, the maximum penetration level of active and reactive power of PVUs, and the improvement of load voltage. As a result, Case 4 is superior to the three other cases. Thus, the number, peak power, power factor, and location of added PVUs in RDPSs should be simultaneously optimized, and one of the most effective optimization tools is COOA.

Economic load dispatch problem under the competitive electric market (ELDCEM) is becoming a hot problem that receives a big interest from researchers. A lot of measures are proposed to deal with the problem. In this paper, three versions of PSO method such as conventional particle swarm optimization (PSO), PSO with inertia weight (IWPSO) and PSO with constriction factor (CFPSO) are applied for handling ELDCEM problem. The core duty of the PSO methods is to determine the most optimal power output of generators to obtain total profit as much as possible for generation companies without violation of constraints. These methods are tested on three and ten-unit systems considering payment model for power delivered and different constraints. Results obtained from the PSO methods are compared with each other to evaluate the effectiveness and robustness. As results, IWPSO method is superior to other methods. Besides, comparing the PSO methods with other reported methods also gives a conclusion that IWPSO method is a very strong tool for solving ELDCEM problem because it can obtain the highest profit, fast converge speed and simulation time.

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In this paper, photovoltaic generators (PVGs) are placed in radial distribution networks (RDNs) for reducing active power loss of one operation day by using three recently published metaheuristic algorithms including coot optimization algorithm (COOA), transient search algorithm (TSA), and crystal structure algorithm (CRSA). In one operation day, the variation of loads is considered, and the change of solar radiation over daytime is also taken. The study has two main contributions regarding the effectiveness of COOA: energy loss reduction and voltage improvement. COOA can reach high energy loss reduction, better solutions, and faster search speed than TSA and CRSA. In fact, COOA finds better energy loss than the algorithms by 1% and 1.77% for the IEEE 69-node system and 0.75% and 1.4% for the IEEE 85-node system. Furthermore, COOA is at least three times faster than CRSA and two times faster than TSA. As compared to a base system without PVGs, COOA can find better energy loss up to 60.96% and improve voltage up to 4.5%. Thus, COOA is a highly effective optimization tool with the optimal solution, high stability, and fast computation process for placing PVGs in RDNs.