Optimal allocation of renewable DGs using artificial hummingbird algorithm under uncertainty conditions

Renewable distributed generators (RDGs) have been widely used in distribution networks for technolog-ical, economic, and environmental reasons. The main concern with renewable-based distributed genera-tors, particularly photovoltaic and wind systems, is their intermittent nature, which causes output power to fluctuate, increasing power system uncertainty. As a result, it's critical to think about the resource's uncertainty when deciding where it should go in the grid. The main innovation of this paper is proposing an efficient and the most recent technique for optimal sizing and placement of the RDGs in radial distri-bution systems considering the uncertainties of the loading and RDGs output powers. Monte-Carlo sim-ulation approach and backward reduction algorithm are used to generate 12 scenarios to model the uncertainties of loading and RDG output power. The artificial hummingbird algorithm (AHA), which is considered the most recent and efficient technique, is used to determine the RDG ratings and placements for a multi-objective function that includes minimizing expected total cost, the expected total emissions, and the expected total voltage deviation, as well as improving expected total voltage stability with con-sidering the uncertainties of loading and RDGs output powers. The proposed technique is tested using an IEEE 33-bus network and an actual distribution system in Portugal (94-bus network). Simulations show that the suggested method effectively solves the problem of optimal DG allocation. In addition of that the expected costs, the emissions, the voltage deviation, are reduced considerably and the voltage stability is also enhanced with inclusion of RDGs in the tested systems.

Automated summary

This paper proposes an efficient and recent technique for optimal sizing and placement of Renewable Distributed Generators (RDGs) in distribution systems, taking into account the uncertainties of loading and RDGs output power. Through simulations and validations, the effectiveness of the proposed technique is demonstrated.