Abstract
In today’s world, distributed generation (DG) is an outstanding solution to tackle the challenges in power grids such as the power loss of the system that is intensified by the exponential increase in demand for electricity. Numerous optimization algorithms have been used by several researchers to establish the optimal placement and sizing of DGs to alleviate this power loss of the system. However, in terms of the reduction of active power loss, the performance of these algorithms is weaker. Furthermore, the premature convergence, the precision of the output, and the complexity are a few major drawbacks of these optimization techniques. Thus, this paper proposes the multileader particle swarm optimization (MLPSO) for the determination of the optimal locations and sizes of DGs with the objective of active power loss minimization while surmounting the drawbacks in previous algorithms. A comprehensive performance analysis is carried out utilizing the suggested approach on the standard IEEE 33 bus system and a real radial bus system in the Malaysian context. The findings reveal a 67.40% and an 80.32% reduction of losses in the two systems by integrating three DGs with a unity power factor, respectively. The comparison of the results with other optimization techniques demonstrated the effectiveness of the proposed MLPSO algorithm in optimal placement and sizing of DGs.
Highlights
The exponential growth of energy demand has been witnessed during the last few decades owing to the complicated life cycle of human beings, and the power losses and the voltage drops of the distribution systems have increased
The multileader particle swarm optimization (MLPSO) algorithm has minimized the active power loss of the system using the integration of three distributed generation (DG) to 68.460 kW which shows an active power loss reduction of 67.40%
Considering all the results presented for the three optimization algorithms under the standard IEEE 33 bus system, the premature convergence, and the less uniformity of results due to having a single leader could be identified as the limitations of the particle swarm optimization (PSO) and comprehensive learning PSO (CLPSO) algorithms
Summary
The exponential growth of energy demand has been witnessed during the last few decades owing to the complicated life cycle of human beings, and the power losses and the voltage drops of the distribution systems have increased. The majority of these systems are either radial or weakly meshed systems. The line loss and the voltage drop associated with the feeder ends of these distribution systems are more significant owing to the inherited high R/X ratio of distribution systems [1] These networks are centralized with unidirectional power flow and energized by fossil fuels. In [3], DGs are defined as power generation units with a maximum capacity of
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