This article proposes a two-stage system for the daily energy management of micro-grids (MGs) in the presence of wind turbines, photovoltaic (PV) panels, and electrical energy storage systems (ESSs). Each MG uses historical data to predict its consumers' load demand, wind speed, and solar irradiance in the first stage. In the second stage, the cooperative game method is used to determine the MG's daily dispatch and energy transaction. The paper develops a prediction model using artificial neural network (ANN) and rough neuron water cycle (RNWC) algorithms, called deep learning artificial neural network (DLANN), which is a combination of technology from the artificial neural network and WC algorithm in order to predict uncertain parameters. The above model is implemented in the 33bus power distribution system; the simulation results show that the DLANN method provides more accurate predictions than the ANN method. The results also show that a MG can achieve energy cost savings through an alliance of MGs using the cooperative game approach. Furthermore, analysis of the impact of the ESS on the operation of the MG shows that the absence of the ESS will reduce the power output of the wind turbine.

本文提出了一个两阶段系统，用于在风力涡轮机、光伏 (PV) 面板和电能存储系统 (ESS) 存在的情况下对微电网 (MG) 进行日常能源管理。每个MG使用历史数据来预测第一阶段其消费者的负载需求、风速和太阳辐照度。第二阶段，采用合作博弈的方法确定MG的日常调度和能源交易。本文使用人工神经网络 (ANN) 和粗糙神经元水循环 (RNWC) 算法开发了一种预测模型，称为深度学习人工神经网络 (DLANN)，它结合了人工神经网络和 WC 算法的技术，以便预测不确定参数。上述模型在33bus配电系统中实现；仿真结果表明，DLANN 方法比 ANN 方法提供更准确的预测。结果还表明，MG 可以通过使用合作博弈方法的 MG 联盟实现能源成本节约。此外，分析 ESS 对 MG 运行的影响表明，缺少 ESS 将降低风力涡轮机的功率输出。