The paper optimizes the placement of soft open points (SOPs) devices, shunt capacitor banks (SCBs), and distributed generators (DGs) in the IEEE 69-node distribution power grid for reducing the power loss of a single hour and total energy losses of one year. EO is proven to be more effective than previous methods and three other applied algorithms, including the Coot optimization algorithm (COOT), Modified weight inertia factor and modified acceleration coefficients-based particle swarm optimization (CFPSO), and Tunicate swarm algorithm (TSA). So, EO is applied for the last case considering one SOPs, one wind turbine (WT), two solar photovoltaic systems (PVs), and two SCBs over one year with twelve months and 24 h each month. The study reaches the smallest power loss compared to previous studies in the first case with one SOPs device. The results from the second to the fourth cases indicate that the power grid needs the placement of SCBs and DGs first and SOPs devices to reach the lowest power loss. Case 5 indicates that the hybrid system with one WT and two PVs suffers higher power losses than the base system at hours with high generation from renewable sources; however, integrating the SOPs and SCBs into the hybrid system can reach smaller losses than the base system at these hours. Thus, using SOPs and SCBs in integrated distribution power grids with renewable energies can greatly benefit energy loss reduction.

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.

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.

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.

This research proposes a strategy to minimize the active power loss in the standard IEEE 85-node radial distribution power grid by optimizing the placement of wind turbines in the grid. The osprey optimization algorithm (OOA) and walrus optimization algorithm (WOA) are implemented to solve the problem. The two algorithms are validated in three study cases of placing two wind turbines (WTs) in the system for power loss reduction. Mainly, in Case 1, WTs can only produce active power, while in Case 2 and Case 3, WTs can supply both active and reactive power to the grid with different ranges of power factors. In Case 4, the best-applied methods between the two are reapplied to reach the minimum value of the total energy loss within one year. Notably, this case focuses on minimizing the total power loss for each hour in a day under load demand variations and dynamic power supply from WTs. On top of that, this case uses two different sets of actual wind power data acquired from the Global Wind Atlas for the two positions inherited from the previous case. Moreover, the utilization of wind power is also evaluated in the two scenarios: (1) wind power from WTs is fully used for all values of load demand, (2) and wind power from WTs is optimized for each load demand value. The results in the first three cases indicate that the WOA achieves better minimum, mean, and maximum power losses for the two cases than the OOA over fifty trial runs. Moreover, the WOA obtains an excellent loss reduction compared to the Base case without WTs. The loss of the base system is 224.3 kW, but that of Case 1, Case 2, and Case 3 is 115.6, 30.6 kW, and 0.097 kW. The placement of wind turbines in Case 1, Case 2, and Case 3 reached a loss reduction of 48.5%, 84.3%, and 99.96% compared to the Base case. The optimal placement of WTs in the selected distribution power grid has shown huge advantages in reducing active power loss, especially in Case 3. For the last study case, the energy loss in a year is calculated by WSO after reaching hourly power loss, the energy loss in a month, and the season. The results in this case also indicate that the optimization of wind power, as mentioned in Scenario 2, results in a better total energy loss value in a year than in Scenario 1. The total energy loss in Scenario 2 is reduced by approximately 95.98% compared to Scenario 1. So, WOA is an effective algorithm for optimizing the placement and determining the power output of wind turbines in distribution power grids to minimize the total energy loss in years.

This study applies Jellyfish Search Algorithm and five other algorithms to minimize the electricity generation cost of two hybrid systems for one operating day. The first system comprises one pumped storage hydroelectric plant and two thermal power plants. The second system is expanded by integrating one wind and one solar photovoltaic power plant into the first system. For each system during one operating day, the pumped storage hydroelectric plant with only generation mode acts as a conventional hydroelectric plant in the first scenario. Still, it can run pumps to store water and produce electricity in the second scenario. As a result, JSA can reach smaller costs than all compared algorithms, from about 1 % to higher than 10 % for two scenarios in the two systems. The comparisons of generation cost indicate the second scenario with the pumped storage hydroelectric plant can reach a smaller cost than the first scenario with the conventional hydroelectric power plant by $53,359.7, corresponding to 7.4 % in the first system and $39,472.8, corresponding to 6.95 % in the second system. Therefore, the water storage function of the pumped storage hydroelectric plant is very effective in reducing the electricity generation costs for hybrid power systems.