Case studies on optimal location and sizing of renewable energy generators in distribution system

Abstract

Optimal location and sizing of different types of renewable energy generation system at the distribution networks for the purpose of active power loss minimization is revealing much attention of electric power utilities in the recent days. In the present work, optimal location and sizing of different types of renewable energy generators (REGs) have been proposed for IEEE 33-bus test system. The vastness of the REGs is determined by minimizing the power loss in the distribution system. Power distribution losses have been minimized with the determination of optimal location as well as optimum size of distributed generator. In this work, various case studies such as active power loss, reactive power loss, combined real and reactive power, optimum power factor, and multi REGs placements have been considered for optimal location of REGs. A simple but efficient Repeated Load Flow (RLF) technique has been used to ascertain optimal location of REGs. The simulation results obtained from this RLF technique have also been compared with an outcome of other approach results. The proposed approach has been validated on IEEE 33-bus and 69 radial distribution test systems.

Hybrid Harris Hawks optimizer for integration of renewable energy sources considering stochastic behavior of energy sources

“…For second case, with 1, 2, and 3 RES‐DG units, the comparison is with HGWO and improved iterative analytical (IIA) because these two algorithms have evaluated similar case. Whereas, GA 45 conducted Radial load flow (RLF) 46 and this case for 1 DG unit and 3 DG units, respectively. The optimal locations suggested by all these methods and HHHO are same that are 30 for 1 DG unit, 12 and 30 for 2 DG units and 13, 24, and 30 for 3 DG units, but the optimal sizes are different.…”

“…The chromosomes (ie, hawks) of each generation consists of three parts:thegenes of the first part represent the candidate bus number for DG installation; the genes of the second parts represent the DG capacity; the genes of the third part represent the power factor. Crossover probability has a direct influence on the convergence, therefore, instead of classical evolution crossover probability is varied systematically 44 according to Equations (45) and (46).…”

Finding Optimal Solutions for the Placement of Reactive and Active Power Generation Components in Distribution Networks Using a High-Performance Metaheuristic Algorithm

“…Different types of DGs have been installed for loss minimization, and several deterministic algorithms based on derivatives have been run for comparison. In [32], DGs were renewable energy generators that can produce both active and reactive powers. A repeated power flow (RPF) method was proposed to determine the site and capacity of each DG.…”