Distribution Network Planning Enhancement via Network Reconfiguration and DG Integration Using Dataset Approach and Water Cycle Algorithm

Abstract

The integration of network reconfiguration and distributed generation (DG) can enhance the performances of overall networks. Thus, proper sizing and siting of DG need to be determined, otherwise it will cause degradation in system performance. However, determining proper sizing and siting of DG together with network reconfiguration is a complex problem due to huge solution search space. This search space mostly contains non-radial network configurations. Eliminating these non-radial combinations during optimization process increases computational overhead and may end up at local optimal solution. To reduce the searching complexity, this paper considers the discretized network reconfiguration via dataset approach. Water cycle algorithm (WCA) is used to obtain the near optimal solution of network reconfiguration, and sizing and sitting of DG. In addition, the power factor of DG is also optimized to reduce the power loss. The proposed method is tested on an IEEE 33-bus network and an IEEE 69-bus network considering different scenarios to show the effectiveness of simultaneous approach considering variable power factor. The results show that the discretization of reconfiguration search space avoids that WCA to get trapped in local optima. The proposed method outperforms other technique such as harmony search algorithm (HSA), fireworks algorithm (FWA), Cuckoo search algorithm (CSA) and uniform voltage distribution based constructive algorithm (UVDA) and improves the solution quality of IEEE 33bus network and 69-bus network by 29.20% and 27.88%, respectively.Index Terms--Reactive power injection, sizing and siting of distributed generation (DG), dataset approach, active distribution network, power system.

A Mixed Integer Linear Programming Based Load Shedding Technique for Improving the Sustainability of Islanded Distribution Systems

“…Loads ranked 1 to 11 were assumed to be non-critical, loads ranked 12 to 16 were classified as semi-critical, and the remaining four loads were ranked as critical, as seen in Table 1. The IEEE 69-bus system load data were taken from [32] and presented in Table 2; one bio-mass DG and two hydro DGs were placed in an optimal location with optimal ratings, as proposed in [32] and shown in Table 3. This system consisted of 48 lumped loads and three DGs: two mini-hydro DGs and one bio-mass DG.…”

Optimisasi Multi-objektif pada Rekonfigurasi Jaringan Distribusi Tenaga Listrik dengan Integrasi Pembangkit Terdistribusi Menggunakan Metode Sistem Kekebalan Buatan

“…Kendala dari masalah optimasi konfigurasi adalah persamaan aliran beban, batas atas dan bawah tegangan bus, serta batas atas dan bawah arus saluran [32]. Optimasi konfigurasi untuk minimisasi keh ilangan daya aktif dapat diformulasikan sebagai berikut [33]: [20]. Model DG yang telah digunakan dalam makalah in i terd iri dari fotovoltaik surya dan pembangkit listrik tenaga angin.…”

Energy Saving Maximization of Balanced and Unbalanced Distribution Power Systems via Network Reconfiguration and Optimum Capacitor Allocation Using a Hybrid Metaheuristic Algorithm

“…In [46], the GA and the branches exchange method were used for unbalanced systems. Monte Carlo simulation (MCS) was carried out in [47].…”

“…Distribution network enhancement using network reconfiguration and DG integration via different algorithms such as the dataset approach and the water cycle algorithm [47], the PSO-DA optimization techniques [48], and the stochastic fractal search algorithm [49].…”