In this paper, allocation of hybrid photovoltaic panels, wind turbines and battery storage (PV/WT/BA) system in distribution network is presented aimed active losses cost minimization, voltage profile enhancement and minimizing power purchased from the hybrid system by the network. Meta-heuristic improved whale optimizer algorithm (IWOA) is used to determine the optimal location and size of the PV/WT/BA system components as decision variables. The conventional WOA is inspired by social behavior and the hunting of humpback whales and in this study its performance is improved by using crossover and mutation operators of differential evolution (DE) method to avoid getting caught in local optimal and reinforcement to achieve global optimal. The methodology is implemented on IEEE 33 bus network considering seasonal variations. The results indicated that optimal determination of the decision variables in the network minimizes the active losses cost, voltage deviations and cost of power purchased from the hybrid system by the network using IWOA. The superiority of the IWOA is confirmed in benchmark test functions solution with very competitive results and also better results in statistic analysis and higher convergence speed and accuracy in comparison with the WOA, DE and particle swarm optimization (PSO). Also the results showed that the highest losses and better voltage are related to the summer and the lowest values are obtained in autumn season. The solar panels only participate in energy generation in spring and summer seasons, cost of power purchased by the network is highest in summer and lowest in autumn season. Moreover, the network is placed in stable current and voltage condition and the network power losses and voltage deviations are minimized.

在本文中，混合光伏电池板、风力涡轮机和电池存储 (PV/WT/BA) 系统在配电网络中的分配旨在最小化有源损耗成本、增强电压分布和最小化网络从混合系统购买的电力。元启发式改进的鲸鱼优化器算法（IWOA）用于确定 PV/WT/BA 系统组件的最佳位置和大小作为决策变量。传统的 WOA 受到社会行为和座头鲸狩猎的启发，在本研究中，通过使用差分进化 (DE) 方法的交叉和变异算子来提高其性能，以避免陷入局部最优和强化以实现全局最优。该方法在考虑季节性变化的 IEEE 33 总线网络上实施。结果表明，网络中决策变量的最佳确定使网络使用 IWOA 从混合系统购买的有功损耗成本、电压偏差和电力成本最小化。与 WOA、DE 和粒子群优化 (PSO) 相比，IWOA 的优越性在基准测试函数解决方案中得到证实，具有非常有竞争力的结果，并且在统计分析方面也有更好的结果以及更高的收敛速度和准确性。结果还表明，最高的损耗和更好的电压与夏季有关，而最低的值出现在秋季。太阳能电池板仅在春夏季节参与发电，网络购电成本夏季最高，秋季最低。而且，