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This paper presents the integration of shunt capacitors in the radial distribution grids (RDG) with constant and time-varying load consideration for the reduction of power losses and total annual cost, which turns to enhance the voltage profile and annual net savings. To gather the stated goals, three objective functions are formulated with system constraints. To solve this identified problem, a novel optimization technique based on the modification of particle swarm optimization is proposed. The solution methodology is divided into two phases. In phase one, potential candidate buses are nominated using the loss sensitivity factor method and in phase two the proposed technique first selects the optimal buses for the capacitor placement among the potential buses then it decides the optimal sizing of the capacitors as well. To demonstrate the performance in terms of efficiency and strength, the proposed technique is tested on IEEE 15, 33, and 69 bus system for the optimal placement and sizing of capacitors (OPSC) problem. The results are achieved in terms of annual net savings for 15 bus (47.66%case−1, 32.76%case−2, 26.46%case−3), 33 bus (33.09% case−1, 27.06%case−2, 24.15%case−3), and 69 bus (34.51% case−1, 29.43%case−2, 25.83%case−3) which are comparable to other state of the art methods, and it also indicates the success of the proposed technique.

The improvement of energy efficiency plays an important role to ensure sustainable electrical operation in large-scale buildings. In relation to the low-cost electrical components, a capacitor is an electrical component that can be used to sustain or improve the operating performance of an unbalanced electrical system in large-scale buildings so that energy efficiency improvement can be obtained. This is important to overcome the ineffective utilization of energy caused by the occurrence of power losses in an unbalanced electrical system of large-scale buildings. Further improvement of energy efficiency can be obtained by reducing an excessive amount of incoming power through the determination of tap setting for incoming transformer, and this is classified under the concept of conservative voltage regulation (CVR) approach. In order to solve the problem, the optimal capacitor placement and sizing (OCPS) with CVR is introduced as a new approach for energy efficiency improvement while ensuring a sustainable operation in an unbalanced electrical system of large-scale buildings. The proposed technique utilizes the artificial intelligence (AI) based differential evolution particle swarm optimization (DEPSO) technique with the objective function of total cost minimization for the real power losses, real power consumption, and capacitors installation. The effectiveness of the proposed technique to achieve energy efficiency improvement is investigated through a case study of an unbalanced electrical system in a large-scale office building. The significance of the research output is related to its low-cost technology that has the potential for a comprehensive, pragmatic implementation in large-scale buildings, and subsequently, it will significantly accelerate the increase of national agenda in energy efficiency.

In the present work, a cuckoo search optimisation-based approach has been developed to allocate static shunt capacitors along radial distribution networks. The objective function is adopted to minify the system operating cost at different loading conditions and to improve the system voltage profile. In addition to find the optimal location and values of the fixed and switched capacitors in distribution networks with different loading levels using the proposed algorithm. Higher potential buses for capacitor placement are initially identified using power loss index. However, that method has proven less than satisfactory as power loss indices may not always indicate the appropriate placement. At that moment, the proposed approach identifies optimal sizing and placement and takes the final decision for optimum location within the number of buses nominated with minimum number of effective locations and with lesser injected VArs. The overall accuracy and reliability of the approach have been validated and tested on radial distribution systems with differing topologies and of varying sizes and complexities. The results shown by the proposed approach have been found to outperform the results of existing heuristic algorithms found in the literature for the given problem.

This study introduces a heuristic‐based approach to allocate static capacitors along radial distribution networks using an accelerated particle swarm optimisation algorithm. In general practice, high potential buses for capacitor placement are initially identified using power loss indices. However, that method has proven less than satisfactory as power loss indices may not always indicate appropriate placement. In the proposed approach, the algorithm identifies optimal sizing and placement and takes the final decision for optimum location within the number of buses nominated. The result is enhancement of the overall system voltage stability index and potential achievement of maximum net savings. Sizing of fixed and switchable capacitors has been considered for different loading conditions. The overall accuracy and reliability of the approach have been validated and tested on radial distribution systems with differing topologies and of varying sizes and complexities. In this study, the results are compared with those obtained using recent heuristic methods and show that the proposed approach is capable of producing high‐quality solutions with good performance of convergence and demonstrated viability.

Electric power generated from the power stations can be distributed to the consumers using different networks. Among those the radial distribution network is the attractive one. Power loss occurred in this network can be reduced and the voltage profile can be improved by placing optimal sized capacitors. There are various algorithms and techniques, which have been used previously to inspect the situation where the capacitors are needed to be placed at suitable nodes with optimal sized. This paper proposes another near approach, which will decide the most appropriate nodes on the essential feeders, laterals and sublaterals of any radial distribution network for ideal capacitor integration in order to enhance power loss reduction and also to enhance the voltage profile utilizing loss sensitivity factor (LSF) strategy and hybrid ABC-PSO calculation. The established LSF approach is utilized here to locate the most appropriate nodes and the optimal capacitor size can be settled with the hybrid ABC-PSO calculation. Capacitor size is an exceedingly nonlinear issue and henceforth fuzzy inference system (FIS) technique is chosen as the most suitable transports for the capacitor position. The sizes of the capacitors relating to least genuine power misfortune are resolved. The proposed technique has been implemented on IEEE 69-node and 34-node radial distribution networks.