Abstract
Contemporary researches offer that most researchers have concentrated on either network reconfiguration or Distributed Generation (DG) units insertion for boosting the performance of the distribution system (DS). However, very few researchers have been studied optimum simultaneous distributed generation units insertion and distribution networks reconfiguration (OSDGIR). In this paper, the stochastic meta-heuristic technique belong to swarm intelligence algorithms is proposed. Salp Swarm Algorithm (SSA) is inspired by the behavior of salps when navigating and foraging in the depth of the ocean. It utilized in solving OSDGIR. The objective function is to reduce power loss and voltage deviation in the Distribution System. The SSA is carried out on two different systems: IEEE 33-bus and local Iraqi radial (AL-Fuhood distribution network). Three cases are implemented; only reconfiguration, only DG units insertion, and OSDGIR. Promising results were obtained, where that power loss reduced by 93.1% and recovery voltage index enhanced by 5.4% for the test system and by 78.77% reduction in power loss and 8.2% improvement in recovery voltage for AL-Fuhood distribution network after applying OSDGIR using SSA. Finally, SSA proved effectiveness after an increase in test system loads by different levels in terms of reduced power loss and voltage deviation comparison with other methods
Highlights
Network Reconfiguration (NR) and Distributed Generator (DG) are considered major techniques used to minimize system power losses (PL), improve the voltage profile (VP) and mitigation of overloads on heavy buses in the Distribution System (DS)
Where: H is the maximum number of DG units, is power supplied by DG, is power supplied by the substation, is the power received by loads, and is the power losses dissipated by lines
Four cases are implemented on both test system IEEE 33-bus and AL-Fuhood distribution systems, which are: Case1: base case without reconfiguration and DG units’ insertion
Summary
Network Reconfiguration (NR) and Distributed Generator (DG) are considered major techniques used to minimize system power losses (PL), improve the voltage profile (VP) and mitigation of overloads on heavy buses in the Distribution System (DS). Researchers in [4] proposed modified particle swarm optimization (MPSO) for finding the optimum solutions in terms of minimum PL and minimum voltage deviation (VD) in DS It tested on two test systems, and different loads are considered. Researchers in [9] proposed a Genetic Algorithm (GA) for solving the optimal size and position of DG units in DS with different load levels and utilized one DG unit. The authors in [14], proposed a hybridized Harmony Search Algorithm (HSA) and teaching learning-based optimization (TLBO) to develop a comprehensive teaching-learning harmony search optimization algorithm (CTLHSO) for solving OSDGIR and the aims are to reduce PL and improve VD with considered different load levels.
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