This dissertation shows energy scheduling in grid‐connected micro grid (MG) using an amazing hybrid method for optimal energy management. The hybrid method is the mixture of Radial Basis Neural Function Network (RBFNN) and Bumble Bees Mating Optimization (BBMO) and is therefore referred to as RBFNN–BBMO technique. The MG system consists of photovoltaic, wind turbine, battery storage and micro turbine systems. Here, batteries are used to maintain a constant and stable output power. The necessary load demand of the grid‐connected MG scheme is continually monitored using the RBFNN method. BBMO optimizes the perfect combination of MG taking into account the expected load demand. In addition to decrease the impact of renewable energy prediction mistakes, a two strategy for MG energy management is adopted. The first approach is to program various renewable energy sources (RESs) during the operation of MG in order to minimize the electricity costs. The second approach is power flow balancing and forecasting errors, decreasing from the scheduled energy reference based on the summarized rule. Access of RESs, power supply and charging modules are the constraints. Two existing methods such as the Genetic Algorithm and the Cuttlefish Algorithm compare the supremacy of the proposed method.

中文翻译：

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RBFNN-BBMO混合方法可在并网微电网中实现强大的能源管理

这篇论文展示了使用令人​​惊讶的混合方法进行最佳能源管理的并网微电网（MG）中的能源调度。混合方法是径向基神经网络（RBFNN）和大黄蜂交配优化（BBMO）的混合，因此被称为RBFNN–BBMO技术。MG系统由光伏，风力涡轮机，电池存储和微型涡轮机系统组成。此处，电池用于维持恒定且稳定的输出功率。使用RBFNN方法不断监测并网MG方案的必要负载需求。BBMO考虑到预期的负载需求，优化了MG的完美组合。除了减少可再生能源预测错误的影响外，MG能源管理还采用了两种策略。第一种方法是在MG运行期间对各种可再生能源（RES）进行编程，以最大程度地减少电费。第二种方法是功率流平衡和预测误差，它基于汇总规则从计划的能源参考中减少。限制使用RES，电源和充电模块。遗传算法和墨鱼算法等两种现有方法比较了该方法的优越性。