Optimal location and sizing of hybrid system by analytical crow search optimization algorithm

This study presents a strategy for selecting an optimal location and placing the optimal photovoltaic (PV) and energy storage system. The power loss, voltage stability of the system, and also sizes of PV and storage are the major objectives, which are obtained through analytical crow search optimization (CSO) algorithm. Initially, the Newton-Raphson load flow analysis is performed; and voltage, and losses of active and reactive power are calculated. The dynamic modelling of the proposed system time interval varies between 1 and 24 hours. The injection of active and reactive power is based on the nondispatchable PV, which can work under the optimal load flow. The major calculation of this work includes the fill factor, load, maximum power, battery, energy loss, and voltage stability. The two test systems, IEEE 33 and IEEE 69 bus systems, have been utilized for validating the performance of a proposed strategy. Finally, the proposed strategy has been compared with some different methods such as improved analytical (IA), exhaustive load flow (ELF), analytical multiobjective index (AIMO), analytical particle swarm optimization (A-PSO), and fast IA (FIA). From the comparison results, the proposed strategy proves the efficient PV size selection for an optimal location to minimize power and voltage losses.