Chaotic Quasi‐Oppositional Crayfish Optimization Algorithm for Wind‐Solar‐Energy Storage Based Hybrid Radial Network Under Load Uncertainty

ABSTRACT The photovoltaic (PV), wind turbine (WT), and battery energy storage (BES) based hybrid system design and optimal placement using chaotic quasi‐oppositional crayfish optimization algorithm (CQOCOA) in a radial distribution network (RDN) under load uncertainty is the main objective of this study. Here, crayfish optimization algorithm (COA) is modified and improved by adding quasi‐oppositional behavior to it. Then chaos theory is added to speed up the convergence pace and avoid the local optimality. For optimal placement of hybrid PV/WT/BES system, simultaneous active power loss and annual operation costs minimization is taken as the objective to enhance the efficacy of the RDN. The uncertainty modeling of PV and WT distributed generation (DG) is considered for power generation as solar irradiance and wind speed can change. This algorithm is validated on 69‐bus and 94‐bus to establish the potency of the suggested CQOCOA algorithm. The active power loss cost is also evaluated after the installation of hybrid PV/WT/BES system. Adjusting the growing load demand, 25% increased load and 10% decreased load is considered for load uncertainty modeling. In both (69‐bus and 94‐bus) systems, the placement of hybrid PV/WT/BES system using the CQOCOA method reduces the active power loss by 58.93%, 60.53%, 53.53%, and 62.19%, 65%, 62.99% for normal, 25% increased, and 10% decreased loading conditions, respectively. In yearly running cost of hybrid system design by CQOCOA method for 69‐bus at normal and 10% decreased load gives yearly savings of 25 364$, 31 951$, 88 951$ and 16 511$, 1527$, 25 608$ than COA, DAOA, and AOA methods. The comparative study of results revealed that the CQOCOA algorithm is better than several optimization algorithms.