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The principal motivation of the marine predators algorithm (MPA) is the common foraging technique, including Lévy and Brownian motions in ocean predators coupled with optimal contact intensity policy in predator–prey biological interaction. This paper proposes an improved marine predators algorithm (IMPA), which is an extension of the original MPA. The suggested improvements lead to rapid convergence and avoid local minima stagnation for the original MPA. IMPA controls the active and reactive power injected into distribution systems to minimize the total system losses and the total voltage deviations and maximize the voltage stability and improve the distribution system's overall performance. On the one hand, the proposed IMPA determines the optimal location and active power (location and size, respectively) of distributed generation (DG). On the other hand, the IMPA controls reactive power by optimally placing and sizing the shunt capacitors (SCs) and determining the PF of DGs. Two standard test systems, 69-bus and 118-bus distribution networks, are considered to prove the proposed algorithm’s efficiency and scalability. Results of the proposed IMPA are compared with those obtained by MPA, AEO, and PSO algorithms. The findings of the simulation results demonstrate that the proposed IMPA can effectively find the optimal problem solutions and beats the other algorithms. Moreover, the framework of multi-objective IMPA outperforms based on MPA in terms of the performance measures of diversity, spacing, coverage, and hypervolume.

Distribution networks transmit the electrical power generated by generation systems to consumers. The distribution network poses a significant challenge in a power system, as it incurs the highest losses due to its low voltage and high current characteristics. Therefore, addressing the issue of reducing distribution network losses is crucial. Over the past 46 years, distribution network reconfiguration (DNR) has been extensively studied as a combinatorial optimization problem. DNR is one of the most intensively investigated topics, accompanied by new challenges. This study focuses on the optimal reconfiguration of the distribution network and the allocation of shunt capacitors to reduce losses and enhance voltage profiles. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation. The cultural algorithm (CA) and the cuckoo search algorithm (CSA) were compared for solving network reconfiguration and capacitor allocation by using direct load flow to minimize power loss and voltage deviation. By comparing the performance of these two algorithms and examining their advantages and disadvantages, the appropriate algorithm can be selected for future studies. The performance of the simulations was verified using MATLAB software on IEEE 33-bus and IEEE 69-bus systems.

In this paper, shunt capacitors are effectively placed in two radial distribution networks with 69 and 85 nodes for the purpose of reducing the sum of capacitor investment cost and energy loss cost by using a novel metaheuristic, called slime mould optimization algorithm (SMOA). The main duty of SMOA is to find the most suitable position of the shunt capacitors and to determine optimal generation of the shunt capacitors over a year with three load levels. In addition to comparison with previous methods in the literature, SMOA is also compared to two other applied methods including bonobo optimization algorithm (BOA) and tunicate swarm algorithm (TSA). The novelty of the paper is to apply three new methods in which SMOA and TSA were developed in early 2020 and BOA was introduced in 2019. The three methods can reach the same success rate of 100%, but SMOA is more powerful. In fact, SMOA can reach better minimum, mean and maximum total costs, faster convergence speed and more effective stability of fifty independent runs. BOA and TSA cannot find one the same good solution as SMOA even they are run 50 times for each study case. Comparison with previous methods in the literature indicates that SMOA can find better position and more suitable generation for shunt capacitors and it can get less total cost, and use smaller population size and a lower number of iterations. The best result from SMOA is also the main contribution of the study and it is recommended that SMOA should be used for placing capacitors in radial distribution networks.

This study provides an overview of distributed generation (DG) and shunt capacitor banks (SCB) integration in radial distribution networks (RDN). The integration of DG and SCB (IDG-SCB) supplies active and reactive power to the network to fulfill the network’s increasing power demand, improve power quality, and increase network stability and security. This work also takes into consideration network reconfiguration (N-Rec). The N-Rec is rearranging RDN topology or structure to accomplish particular goals including load balancing, loss decreasing, regulating voltage and enhanced reliability. Reconfiguring the RDN makes sure that no single line is overloaded while others are left unused by dividing loads across its branches. Through load balancing, the system’s overall capacity is maximized and the chance of overloading and related losses is decreased. In addition, N-Rec establishes channels for two-way power flow and maximizes the network’s ability to manage fluctuating generation patterns; reconfiguration makes it easier to integrate IDG-SCBs into the RDN. The purpose of this work is to conduct a review of IDG-SCB with N-Rec in the last five years. The focus of this survey is on the allocation of DG and SCBs, their sizes both continuous and discrete. The optimization methods are categorized based on whether they are single-objective, multi-objective, or hybrid, and then further classified based on the objective functions applied in the method, constraints subjected to optimization, and the RDN that has been tested in the method. Additionally, a statistical analysis is conducted to analyze the most frequently used methods, objective functions, constraints, and RDNs in the field. Finally, As a case study, an analysis was conducted to propose the results of the most objective function used, i.e. power loss, in the most frequently used RDN, which are IEEE 33 and 69-bus RDN. The analysis considered IDG-SCB with N-Rec. The results show the effectiveness of IDG-SCB with N-Rec in improving power distribution system resilience and efficiency, as evidenced by the significant reduction in power loss of up to 97.95% that can be achieved by its implementation.

The optimal configuration problem of distributed generators (DGs) and shunt capacitors (SCs) in a radial distributed network (RDN) is to find the best installation locations and optimal capacities of DGs and SCs for optimizing a certain performance indicator. The discontinuity characteristic and huge computation of optimizing DGs and SCs in RDN make it no longer applicable by traditional methods. In this paper, a new bus processing method and an improved butterfly algorithm are proposed to solve the optimal configuration of DGs and SCs. The method of processing nodes considers not only the power loss index (PLI) but also the voltage amplitude of the original system, forming a sequence of candidate nodes to guide algorithm to optimizing, which can simplify the algorithm search space and improve search efficiency. Meanwhile, the butterfly algorithm with constriction factor (BF-CF) combines the inertia coefficient to introduce a constriction factor, improves the speed update model and the local search pattern, and overcomes the disadvantages of the original butterfly algorithm which is easy to fall into the local optimum and the precision of result is not high. To verify the performance of the proposed method, minimizing the active power loss and voltage deviation are selected as the objective function and reactive power loss and the worst voltage are taken as reference targets, which are performed in three standard test systems of 33-bus, 69-bus and 119-bus, respectively. The simulation results illustrate that, compared with the original butterfly algorithm and other intelligent algorithms, the method proposed in this paper has more obvious advantages and performance in solving the configuration problem of DGs and SCs in systems of various scales.

[7] proposed a method developed which dependent on enhanced genetic algorithm for the determination of the optimal position of tie and sectionalizing switches, and to provide the network with optimal efficiency. [...]in this paper, the reconfiguration is applied using Newton Raphson (NR) method based on binary particle swarm optimization (BPSO) with three different cases of loads, constant and variable loads with regulation ratio (nominal load 100%, light load 50%, and heavy load 160%). Gauss-Seidel load flow solution tends to be useful in smaller systems, but as the system size increases, the computation time increases. [...]the fast decoupled load flow and the Newton Raphson methods are more common methods in large systems. Another study of the forward/backward methods (a popular power flow method applied to distribution systems) are capable of performing power flow analysis, however, it is limited to radial networks and does not have the ability to consider the influence of distributed generation, On the other hand, NR method typically can deal with any topology type (i.e. radial, weakly meshed and meshed) and can consider the influence of distributed generation, the formulation and origin of the NR approach has been dated back to the late 1960's [14].

This work presents a methodology for segregation of transmission losses of active and reactive power flows based on load flow study with unity vs actual power factor of load buses. The proposed methodology applied on existing 12 bus Indian power system having five transmission lines, 12 power transformers, five load buses, 13 shunt capacitor banks. Hourly actual system parameters of test system, viz. active and reactive power demand, tap position of transformers, ON/OFF status of shunt capacitor banks and swing bus voltage gathered from the grid substations and simulation studies performed on MiPower software for separation of losses. In second part, bus reactive demand is mitigated through automatic switched shunt capacitor banks and its effect on system voltage profile, loading of transmission system elements, number of operations of on load tap changer and losses are presented.

This paper proposed the multiobjective optimal allocation of renewable distributed generation and shunt capacitors in the distribution system using corona virus herd optimization techniques. The work aimed to achieve a technical benefit, total electricity cost reduction, and enhancement of greenhouse safety. The objectives considered are real power loss, voltage profile index (VPI), voltage stability index (VSI), total electricity cost (TEC), and total greenhouse gas emission (TGHGe). Weight function was used to combine the objectives for the six cases considered with different priorities. The proposed CHIO is validated on the standard IEEE 33 bus system and implemented on Dada 46 bus, a Nigerian practical distribution network. Various cases were considered for the two test systems. For IEEE 33 bus, the proposed method achieved 89.44% and 86.77% reduction in real and reactive power, respectively, with 93.73% and 39.27% in TGHGe and TEC. Also, for Dada 46 bus system, 89.44% and 86.77% reduction in real and reactive power loss respectively was achieved with 98.66% and 64.42% in TGHGe and TEC. Furthermore, the highest level of greenhouse gas emission reduction was achieved (says 99.69%) when high priority was placed on the reduction in TGHGe; this shows the significant impact of renewable energy in the distribution system. The results obtained are compared with the existing methods, such as PSO, GA, ABC, GABC, WOA, WCA, to mention a few. In other to show the performance of the proposed CHIO compared to others, the outcome reveals the excellent performance of the proposed algorithm in terms of an optimal result.

ABSTRACT Day to day the importance and usage of electric power is increasing, in order to reach this power demand, generation of power is being increased. But the transmission grid fails to meet the developments in generation, as construction of new transmission lines involve more time than building new generation facilities. So, updating or upgrading the existing facilities will be more beneficial than building new ones. This is achieved by providing proper reactive compensation. This main objective of the paper is placement of shunt capacitor for voltage profile improvement and reactive power compensation. The voltage stability index, called voltage collapse prediction index (VCPI), decides the shunt capacitor placement. This paper deals with placement of shunt capacitor banks to improve voltage at weakest buses. The shunt capacitor banks connected to the system to prevent the low voltages during the high loading conditions. The reactive compensation and reduction of losses and power transfer capability can be increased by placing of shunt capacitor. The proposed technique is validated with load flow analysis on IEEE 14 bus system carry out by Mi-power software. From the results obtained it is verified that the load flow analysis can be easily done with Mi-power which is a fast and robust tool with a powerful GUI to solve. Finally, the voltage profile improvement is verified with the placement of shunt capacitor.

Power systems depend on discrete devices, such as shunt capacitors/reactors and on-load tap changers, for their long-term reliability. In transmission systems that contain large wind farms, we must take into account the uncertainties in wind power generation when deciding when to operate these devices. In this paper, we describe a method to schedule the operation of these devices over the course of the following day. These schedules are designed to minimize wind-power generation curtailment, bus voltage violations, and dynamic reactive-power deviations, even under the worst possible conditions. Daily voltage-control decisions are initiated every 15 min using a dynamic optimization algorithm that predicts the state of the system over the next 4-hour period. For this, forecasts updated in real-time are employed, because they are more precise than forecasts for the day ahead. Day-ahead schedules are calculated using a two-stage robust mixed-integer optimization algorithm. The proposed control strategies were tested on a Chinese power network with wind power sources; the control performance was also validated numerically.