In this article, a meta-heuristic technique based on a backtracking search algorithm (BSA) is employed to produce solutions to ascertain distributed generators (DGs). The objective is established to reduce power loss and improve network voltage profile in radial distribution networks by determining optimal locations and sizes of the DGs. Power loss indices and bus voltages are engaged to explore the initial placement of DG installations. The study cares with the DG type injects active and reactive power. The proposed methodology takes into consideration four load models, and their impacts are addressed. The proposed BSA-based methodology is verified on two different test networks with different load models and the simulation results are compared to those reported in the recent literature. The study finds that the constant power load model among various load models is sufficed and viable to allocate DGs for network loss and voltage studies. The simulation results reveal the efficacy and robustness of the BSA in finding the optimal solution of DGs allocation. total network active power loss total network reactive power loss active power losses of the line between the nodes i and j reactive power losses of the line between the nodes i and j resistance of the line between the nodes i and j reactance of the line between the nodes i and j total effective real power load fed through bus j total effective reactive power fed through bus j angle of impedance of line i–j injected active and reactive power components at bus i voltage magnitude and angle at bus i and j, respectively difference between δ i and δ j impedance magnitude between bus i and j real power generation at bus i conductance between bus i and j susceptance between bus i and j reactive power generation at bus i number of network buses total number of lines number of distributed generators (DGs) maximum allowable number of DGs units along the network (user defined) number of connected loads number of DG units connected to bus i injected active power of ith DG injected active power of ith DG active power demand of load at bus i reactive power demand of load at bus i with DGs installations without installations of DGs lower limit of DG active output power upper limit of DG active output power lower limit of DG reactive output power upper limit of DG reactive output power actual line flow of line i rated power capacity of line i penalty for the voltage constraints penalty for the line flow constraint penalty for the maximum allowable DGs active power penalty for the maximum allowable DGs reactive power power loss decline of bus m power loss index of bus m maximum loss decline minimum loss decline load flow cumulative voltage deviation load real and reactive power exponents, respectively load bus voltage and load nominal voltage of bus j, respectively real and reactive power at bus j, respectively real and reactive operating point at bus j, respectively population size problem dimension uniform distribution [0, 1] lower bound of optimized parameter j upper bound of optimized parameter j target individual i in the population P 
Study impact of various load models on DG placement and sizing using backtracking search algorithm