Heuristic optimization techniques for connecting renewable distributed generators on distribution grids

Proposing a high effective objective function by utilizing optimal weighting factors plays an important role in power systems to boost the quality, attitude, and efficiency of evaluating the position and capacity of renewable distributed generators (RDGs) optimally. This research introduces a comprehensive study of different effective objective functions. A comprehensive analysis between the most modern optimization techniques, like hybrid particle swarm optimization (PSO) with Quazi-Newton, hybrid PSO with gravitational search algorithm, grasshopper optimization algorithm, moth-flame optimization, and slap-swarm algorithm, is done in order to determine the best optimizer with respect to high performance, high accuracy, and the minimum convergence time. The best prepared methodology is proposed and compared with other modern techniques to validate its performance. The suggested scheme is exercised by studying the impact of the RDGs integration for 33 and 69 nodes of IEEE distribution grids, in addition to one of the Egyptian radial distribution networks as a practical case study within 24 h at different load levels. The numerical results confirmed the importance and usefulness of incorporating electing the efficient objective function and superior optimization algorithm in the power system to achieve a successful global optimum solution to ensure the power quality through enhanced voltage levels, while minifying the system power losses and the operating prices.