In this paper, a hybrid technique is proposed for the system modeling and energy management of Microgrid (MG) connected sources. The proposed hybrid technique is the hybridization of Artificial Bees Colony (ABC) with Bat Search Algorithm (BAT) and Artificial Neural Network (ANN). Here, the novelty of the proposed method is that the search space of the scout bees is gained with the utilization of the BAT and the optimal solution is found from this scout bees search space. The performance of the scout bees is generously enhanced by BAT. So the combination of ABC and BAT is called as MABC technique. The proposed algorithm optimizes the configuration of the MG sources combination by considering the multi-objective function, while fulfilling the load demand. The considered MG connected system such as Wind Turbine, Photovoltaic array, Fuel Cell, Micro Turbine, Diesel Generator and battery storage. Here, ANN is utilized for the prediction of the required load demand by using the inputs of MG connected sources. Based on the load demand, MABC technique is utilized to choose an optimal configuration of MG, i.e., fuel cost minimization, emission factors, operating, and maintenance cost. The proposed technique is executed in MATLAB/Simulink platform and compared with existing strategies. In this paper, the cost accuracy percentage (CAP) for proposed and the existing technique is also investigated, the proposed technique achieves 7.37880 %.

本文提出了一种混合技术，用于微电网（MG）连接源的系统建模和能源管理。提出的混合技术是将人工蜂群（ABC）与蝙蝠搜索算法（BAT）和人工神经网络（ANN）进行杂交。在此，所提出的方法的新颖性在于利用BAT来获得侦察蜂的搜索空间，并从该侦察蜂的搜索空间中找到最佳解决方案。BAT极大地提高了侦察蜂的性能。因此，将ABC和BAT的组合称为MABC技术。该算法通过考虑多目标函数，在满足负载需求的同时，优化了MG源组合的配置。被认为与MG连接的系统，例如风力涡轮机，光伏阵列，燃料电池，微型涡轮机，柴油发电机和电池存储。在这里，通过使用MG连接源的输入，将ANN用于预测所需的负载需求。基于负载需求，MABC技术用于选择MG的最佳配置，即最小化燃料成本，排放因子，运行和维护成本。所提出的技术在MATLAB / Simulink平台上执行，并与现有策略进行了比较。在本文中，对提出的和现有技术的成本准确率（CAP）进行了研究，提出的技术达到了7.37880％。最小化燃料成本，排放因子，运营和维护成本。所提出的技术在MATLAB / Simulink平台上执行，并与现有策略进行了比较。在本文中，对提出的和现有技术的成本准确率（CAP）进行了研究，提出的技术达到了7.37880％。最小化燃料成本，排放因子，运营和维护成本。所提出的技术在MATLAB / Simulink平台上执行，并与现有策略进行了比较。在本文中，对提出的和现有技术的成本准确率（CAP）进行了研究，提出的技术达到了7.37880％。