This paper presents an intelligent energy management method to control the voltage and frequency at the primary and secondary control levels of micro-grids. The proposed model is based on the model predictive control (MPC) at the primary and intelligent neural network (INN) at the secondary. The purpose of this paper is to control the voltage and frequency based on the uncertainties of power generation resources and loads. In order to validate, the results of the proposed model are compared in different scenarios with other methods such as Fuzzy and PID controllers. The performance of the proposed method in primary and secondary as well as the coordination of this method with a high degree of sensitivity along with HESS and diesel generator units, in addition to increasing stability and reliability, protects the operation of the micro-grid against fluctuations based on uncertainties. The results show that this method, in addition to accuracy, has a higher operating speed than other controllers. As a result, the voltage and frequency restoration in the primary and the compensation of deviations from the reference in the secondary are associated with acceptable results. In order to validate the simulation results and experimental results are also presented.

本文提出了一种智能能源管理方法来控制微电网一次和二次控制级别的电压和频率。所提出的模型基于初级的模型预测控制 (MPC) 和次级的智能神经网络 (INN)。本文的目的是根据发电资源和负载的不确定性来控制电压和频率。为了验证，所提出的模型在不同场景下的结果与其他方法（如模糊和 PID 控制器）进行了比较。所提出的方法在一次和二次中的性能以及该方法与 HESS 和柴油发电机组的高度灵敏度的协调，除了增加稳定性和可靠性之外，保护微电网的运行免受基于不确定性的波动。结果表明，该方法除了精度外，还比其他控制器具有更高的运行速度。因此，初级电压和频率恢复以及次级参考偏差的补偿与可接受的结果相关。为了验证模拟结果和实验结果也被提出。