Power Losses Reduction via Simultaneous Optimal Distributed Generation Output and Reconfiguration using ABC Optimization

Abstract

-Optimal Distributed Generation (DG) output and reconfiguration are among the well accepted approach to reduce power loss in a distribution network. In the past, most of the researchers employed optimal DG output and reconfiguration separately. In this work, a simultaneous DG output and reconfiguration analysis is proposed to maximize power loss reduction. The impact of the separated analysis and simultaneous analysis are investigated. The test result on the 33 bus distribution network with 3 units of DG operated in PV mode showed the simultaneous analysis gave the lowest power loss (global optimal) and faster results compared to other combined methods. All the analyses for optimizing the DG as well as reconfiguration are used the Artificial Bee Colony Optimization technique.

Optimal Daily Configuration of a Distribution Network Based on Photovoltaic Generation and System Loading Using Imperialist Competitive Algorithm and Selection Approach

“…The new position of the colony is given as equation ( 5) with uniform distribution where β is a parameter of ICA that greater than 1 so that the colony will get closer to its imperialist from both side and d is the distance between the imperialist and colony. In revolution, some colonies are selected randomly to undergo revolution by adding a random amount of deviation, θto the direction of movement as equation (6) where γ is a control parameter of ICA.…”

“…In ref. [6], the authors proposed simultaneous approach for network reconfiguration and DG sizing to reduce power loss using Artificial Bee Colony optimization (ABC).…”

Reconfiguration and displacement of DG and EVs in distribution networks using a hybrid GA–SFLA multi-objective optimization algorithm

“…DG sources are modeled as negative load, and its effect on power flow is considered as voltage constraints. So far, meta-heuristic algorithms with various efficiency and accuracy and with different objective functions have been presented for the reconfiguration and placement problem of DG such as GA (Saonerkar and Bagde, 2014;Ajmal et al, 2021;Mahdavi et al, 2021), PSO (Jena and Chauhan, 2016;Saleh et al, 2018;Rafi and Dhal, 2020b), SFLA (Arandian et al, 2014;Azizivahed et al, 2017;Onlam et al, 2019), fuzzy (Sedighizadeh and Bakhtiary, 2016;Mohammadi et al, 2017;Hosseinimoghadam et al, 2020), and ABC (Jamian et al, 2014;Quadri and Bhowmick, 2020;Dashtdar et al, 2022c).…”

Annual energy loss reduction of distribution network through reconfiguration and renewable energy sources

“…However, both the studies 52,53 did not consider renewable DG and reconfiguration. Researchers have also used other optimization techniques such as simulated annealing, 54 artificial immune algorithm, 55,56 vaccine-enhanced artificial immune system, 57 modified plant growth simulation algorithm, 58 PSO, 59,60 GA 61-63 , runner root algorithm, 64 harmony search algorithm, 65 artificial bee colony algorithm, 66,67 hybrid Harmony search and particle artificial bee colony algorithm, 68 interval analysis, 69 stochastic MILP, 70 Ant Colony Optimization, 71 hybrid receding horizon control and scenario analysis, 72 nondominated sorting GA, 73 fuzzy mutated GA, 74 tabu search, 75 Benders decomposition approach, 76 and evolutionary programming. 77 Summary of the reviewed literature is presented in Tables 1 and 2. From the above literature survey, it is seen that most of the researchers have studied either DG installation or network reconfiguration for power loss minimization.…”