Planning for Distribution System with Grey Wolf Optimization Method

Abstract

Nowadays Distribution generation (DG) has achieved to further precious awareness, especially inside the power system fields, so the strength and dependability specifically in the distribution system. Optimum scheduling of DG not only focuses on the size of DG only too puts a load on the optimal location of generators. Install for DG at the optimum location along with optimal size into the distribution system would improve the system performance and also give price effectual solved to the planning of the distribution network. The positive impact of optimum DG position into the distribution system would improve system voltage profile, reduction in line losses, improved power standard, make better reliability and strength of the distribution network. GWO is modeled based on the unique hunt, searching for a target, encircling target, and attacking prey, are executing to perform the optimization. The GWO is determined to the IEEE-16, 30, 57 and 118-bus test systems radial distribution network as well as considering multiplier DG units in the system. The better study outcome of the attained to without DG, with DG, type 1 DG, type 2 DG, with type 3 DG at 0.9 pf and with type DG at unity pf. Moreover, the obtained is compared as well as the net outcome of the proposed procedure for the sequence to see the efficiency and effectual and the distribution systems.

Optimal parameter estimation of overhead transmission line considering different bundle conductors with the uncertainty of load modeling

“…The primary purpose of the electrical transmission and distribution system is to transmit electrical energy from the source of production to the consumption stage. 37,38 Moreover, load modeling has a vital role in the electrical power system. Static and dynamic models are strategies to calculate load modeling.…”

Optimal allocation of DSTATCOM and PV array in distribution system employing fuzzy-lightning search algorithm

“…Improved harmony search algorithms (IHSA) [23] have an impressive advantage of easy implementation, thus it never finds a fulfilled result. Multi-objective grey wolf optimizer algorithm (GWO) [24,25] has a notable quality of easy implementation, though it has many disadvantages like slow convergence, low-solving precision, etc. Analytical crow search optimization (CSO) algorithm [26] can easily solve the complex engineering problems but the search speed is very slow, so the computation takes more time.…”

Energy Loss Reduction of Distribution Systems Equipped with Multiple Distributed Generations Considering Uncertainty using Manta-Ray Foraging Optimization

“…The authors of (Bai and Yildizbasi 2020), (Yuan et al 2020) used the coyote optimization algorithm (COA) for allocating battery energy storage into distribution systems with and without photovoltaic (PV) panels with the objective of minimizing the total system losses. Grey wolf optimization (GWO) was applied in (Ansari et al 2020) for DG planning into distribution systems to improve voltage profile, reliability, and strength of systems. The study included different distribution systems and DG types with different power factors.…”