International Journal of Advanced Science and Technology Vol. 29, No. 4, (2020), pp. 10633–10643 10633 ISSN: 2005-4238 IJAST Copyright ⓒ 2020 SERSC Optimal Location and Sizing of Renewable Distributed Generators in Radial Distribution System Using Coyote Optimization Algorithm E. M. Abdallah 1 , M. I. ELsayed 2 , M. M. ELgazzer 3 . Amal A. Hassan 4 1,2,3 Department of Electrical and Machines, Faculty of Engineering, Al-Azhar University, Egypt 4 Electronics Research Institute, Cairo, Egypt eng.eman 1928@yahoo.com 1 , d_eng2009@yahoo.com 2 , mohamed.m.elgazzar@gmail.com 3 , amal_elramly@yahoo.com 4 Abstract Optimal location and sizing of renewable distributed generators (RDGs) in radial distribution systems (RDS) is the most preferable strategies for increasing the generation of the power systems, enhancing performance of DS and covering increasing in load demand . In This paper application of new Optimization algorithm (Coyote Optimization Algorithm (COA)) is presented to obtain optimal placement and sizing of RDGs at different power factors to enhance performance of DS by reducing power losses and enhancing voltage profile for all buses of the network. Minimizing of active power losses is the main objective of this work which can be improve system performance; reliability, and efficiency. Simulations using MATLAB tools are implemented on the IEEE RDS including (33and 69) bus to evaluate the possibilities of the proposed algorithm. The COA results are compared with other meta-heuristic algorithms that show the feasibility of the strategy proposed to obtain optimum location and sizing of RDG in RDS. Keywords: RDG, Photovoltaic, Wind Turbine, COA, Renewable Energy, Power Loss Reduction 1. Introduction The continuous growth in load demand in DS is a natural phenomenon but it make distribution system planners faces huge challenge to keep DS operating under load growth without any violation in power system constraints. Increasing in load demand has negative effect on the power system performance thus power losses is increased and voltage profile of the system is reduced [1]. Distributed generator (DG) acts as small electrical generation unite and may came from renewable sources or nonrenewable source. DGs based on renewable energy sources like wind energy and Photovoltaic (PV) is increasing more rabidly around the world due to low cost, small size and friendly environmental So, integration of RDGs in RDS play a major role for enhancing performance of DS by minimizing total power losses and enhancing voltage of all buses of the network [2] .to maximize the benefit from integration of RDGs in RDS location and size of DGs must be optimized because improper siting and sizing of DG can reduce the benefits through increasing power loss and voltage instability . Hence, many researchers have been performed to obtain optimal placement and sizing of DGs using optimization methods such as an evolutionary algorithm (EA) and Backtracking Search Optimization Algorithm (BSOA) [3-4]. In [5] genetic algorithm is implemented for optimal location of DGs aims to reduce total power loss (Ploss) of RDS. Anather GA was presented to obtain optimal siting and sizing of DGs for radial and ring DS [6]. In [ 7 ] modified teaching-learning- algorithm was implemented to find optimal sizing and location of DGs to minimize Ploss . Optimal siting and sizing of DGs in RDS for reduction Ploss and enhancement voltage profile using SPSO (Selective Particle Swarm Optimization) algorithm was presented in [8]. Authors in [9] combined two optimization methods, the loss sensitivity factor (LSF) to obtain the best siting of DGs and Simulated