Optimal Placement and Sizing of DGs in Distribution Networks Using MLPSO Algorithm

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.

Power Factor Improvement of Wind based DGs in the Distribution System Using (Fruit Fly and Cat Swarm) Optimization Techniques by Reducing the Harmonics

“…The multileader particle swarm algorithm is used to minimize the power loss (67.40 % and 80.32%) by integrating three DGs with unity power factor (Karunarathne et al 2020). A differential evolution algorithm (DEA) is used here in loss reduction (12.11%) and voltage profile improvement.…”

The Optimal Placement and Sizing of Distributed Generation in an Active Distribution Network with Several Soft Open Points

“…The first category randomly identifies the optimal sitting positions and the second category employs an index to determine the best-suited position for the DG integration. One of the common indexes for determining the weakest node in the system is the voltage stability index (VSI) [34]. However, it is not feasible to address the necessity in locations for the integration of simultaneous multiple DGs because only a single node of integration is given by the second method.…”

Optimal Reliability of a Smart Grid

“…The analysis shows that the appropriate use of DG would significantly reduce energy losses in a network. In [14], the author proposed the multi-leader particle swarm optimization for the optimal allocation and size of distributed generation with the goal to minimize the active losses. The author implemented the method in IEEE 33node test feeder and Malaysian network.…”