Dynamic Arithmetic Optimization Algorithm Under Load Uncertainty for Wind‐Solar‐Energy Storage‐Based Hybrid Radial Network - Dey, Indrajit; Kumar Roy, Provas (Optimal control applications & methods, 2025)
ABSTRACT The optimal design and installation of hybrid photovoltaic (PV), wind turbine (WT) distributed generation (DG), and battery energy storage system (BESS) in radial distribution network (RDN) using dynamic arithmetic optimization algorithm (DAOA) is the main purpose of this work. The DAOA algorithm is the improved version of arithmetic optimization algorithm (AOA) which is widely used in the field of Electrical Engineering to solve the different optimization tasks. The simultaneous minimization of active power loss and voltage deviation is taken as the objectives to improve the efficiency of the distribution network using hybrid PV/WT/BESS system. To establish the efficacy of the suggested DAOA method, it is examined on two very well‐known test systems (33‐bus and 69‐bus). The uncertainty of power generation from PV‐ and WT‐based DG due to solar irradiance and wind speed variation also is taken into consideration. The cost analysis of the hybrid PV/WT/BESS system as well as active power loss cost are also assessed in this study. The result comparison shows that the DAOA algorithm gives supreme result in case of active power losses, voltage deviation, and benefits in active power lost cost. This algorithm has good speed of response and solution quality when hybrid PV/WT/BESS system allocation problem of the distribution network with simultaneous power loss reduction and voltage deviation minimization is executed in RDN. The placement of hybrid PV/WT/BESS system in 33‐bus reduces the active power loss by 44.27%, 41.47%, 46.23% using DAOA and 44.26%, 39.87%, 45.18% using AOA for normal loading, 15% increased loading, and 10% decreased loading, respectively. Similar results are also observed for 69‐bus test system. Moreover, if the performance of the DAOA, AOA, immune clone selection algorithm (ICSA), clone selection algorithm (CSA), manta ray foraging optimization (MRFO), and particle swarm optimization (PSO) for PV, WT, BESS‐ based 33‐bus system are observed, it can be noticed that DAOA gives annual savings of 10$, 496$, 505$, 669$, and 554$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. Similarly for PV, WT, BESS‐based 69‐bus system, it is observed that DAOA gives annual savings of 29$, 3147$, 3154$, 3342$, and 3158$ as compared to AOA, ICSA, CSA, MRFO, and PSO, respectively. In case of solution quality, the DAOA method grants its superiority over several optimization methods found in the literature.