Optimal sizing and location of multiple distributed generation for power loss minimization using genetic algorithm

Abstract

This paper presents a Genetic Algorithm (GA) for optimal location and sizing of multiple distributed generation (DG) for loss minimization. The study is implemented on a 33-bus radial distribution system to optimally allocate different numbers of DGs through the minimization of total active power losses and voltage deviation at power constraints of 0 – 2 MW and 0 – 3 MW respectively. The study proposed a PQ model of DG and Direct Load Flow (DLF) technique that uses Bus Incidence to Branch current (BIBC) and Branch Current to Bus Voltage (BCBV) matrices. The result obtained a minimum base case voltage level of 0.9898 p.u at bus 18 with variations of voltage improvements at other buses after single and multiple DG allocations in the system. Besides, the total power loss before DG allocation is observed as 0.2243 MW, and total power loss after DG allocation was determined based on the power constraints. Various optimal locations were seen depending on the power limits of different DG sizes. The results have shown that the impact of optimal allocation and sizing of three DG is more advantageous concerning voltage improvement, reduction of the voltage deviation and also total power loss in the distribution system. The results obtained in the 0 – 2 MW power limit is consistent to the 0 – 3 MW power limits regarding the influence of allocating DG to the network and minimization of total power losses.

Using Arithmetic Optimization Algorithm to Allocate and Size Wind Energy Systems in RDSs

“…32 Additionally, this approach has been introduced for determining the ideal location and size of renewable energy sources. 33 In the study conducted by, 34 the goal function of minimizing power loss was used to determine the optimal DG in a RDS using GA. However, the analysis primarily focused on reducing power loss, without considering additional goals such as voltage level enhancement or system stability.…”

A Short Review on the Optimization Methods Using for Distributed Generation Planning

“…Ref. [39], the optimum size and location of the distributed network in order to reduce active power privations and voltage fluctuations, the construction of a 33 bus system with genetic algorithm has been investigated. Ref.…”

Optimization of location and size of distributed generations for maximizing their capacity and minimizing power loss of distribution system based on cuckoo search algorithm

“…For searching the suitable location and capacity of DGs embedded to distribution system, there are two main groups of methods comprising of conventional mathematical techniques and modern mathematical techniques. The linear programming [5], mixed integer [6] and dynamic programming [7] are typical representatives in the first group while genetic algorithm [8], [9], particle swarm optimization [10], [11], honey bee mating optimization [12], adaptive cuckoo search (ACSA) [13], salp swarm algorithm (SSA) [14], stochastic fractal search (SFS) [15], coyote algorithm (COA) [16], combination of teaching learning based optimization and grey wolf optimizer (TLBO-GWO) [17], firefly algorithm [18], crow search algorithm [19] and whale optimization algorithm [20] are typical representatives for the second group. Compared to the first group, the second group of methods is stronger developed than the first one due to many advantages such as the better quality of obtained solution and the easier handle constraints.…”