Improved Coyote Optimization Algorithm for Optimally Installing Solar Photovoltaic Distribution Generation Units in Radial Distribution Power Systems

Abstract

This paper proposes an improved coyote optimization algorithm (ICOA) for optimizing the location and sizing of solar photovoltaic distribution generation units (PVDGUs) in radial distribution systems. In the considered problem, four single objectives consisting of total power losses, capacity of all PVDGUs, voltage profile index, and harmonic distortions are minimized independently while satisfying branch current limits, voltage limits, and harmonic distortion limits exactly and simultaneously. The performance of the proposed ICOA method has been improved significantly since two improvements were carried out on the two new solution generations of the conventional coyote optimization algorithm (COA). By finding four single objectives from two IEEE distribution power systems with 33 buses and 69 buses, the impact of each proposed improvement and two proposed improvements on the real performance of ICOA has been investigated. ICOA was superior to COA in terms of capability of finding higher quality solutions, more stable search ability, and faster convergence speed. Furthermore, we have also applied five other metaheuristic algorithms consisting of biogeography-based optimization (BBO), genetic algorithm (GA), particle swarm optimization algorithm (PSO), sunflower optimization (SFO), and salp swarm algorithm (SSA) for dealing with the same problem and evaluating further performance of ICOA. The result comparisons have also indicated the outstanding performance of ICOA because it could find much better results than these methods, especially SFO, SSA, and GA. Consequently, the proposed ICOA is a very effective method for finding the optimal location and capacity of PVDGUs in radial distribution power systems.

Optimal Reactive Power Generation for Transmission Power Systems Considering Discrete Values of Capacitors and Tap Changers

“…However, the real performance of COA for engineering problems such ass ORPD has not been demonstrated persuasively, since there has been no application so far. However, COA has not achieved good results for other problems in engineering, and it has been suggested to be modified [57][58][59]. For the cases of solving benchmark functions, many functions have real optimal solutions with zero values, and construction of COA has produced solutions around the real ones.…”

Active Power Loss Reduction for Radial Distribution Systems by Placing Capacitors and PV Systems with Geography Location Constraints

“…where ∆I l,x and ∆U m,x are penalty terms for the violation of the lth branch current and the violation of the mth node voltage in the xth solution [36].…”

Multiobjective coyote optimization algorithm for techno‐economic simultaneous placement of DGs and FCLs in distribution networks

“…COA has recently been widely used to solve a variety of optimization applications in electrical networks. 19,[32][33][34][35][36][37][38][39][40] It has been performed to design the fuel cell model 25 and high voltage transformers. 26 In Reference 27, it has been implemented for treating the dispatch problem integrated with renewable wind power.…”

“…Moreover, COA has been enhanced to be applicable in more optimization problems as improved Coyote optimization algorithm (ICOA) has been proposed for determining optimal location and sizing of solar photovoltaic distribution generation units. 35 In addition, the optimal parameters of proton exchange membrane fuel cell (PEMFC) and PEMFCs have been obtained using in References 36,37, respectively. The reactive power dispatch is solved by enhanced COA in Reference 19 considering the wind energy as one of renewable energies.…”