Facing development requirements and changes in the global energy context, Morocco has begun a process of diversification of the national energy mix in favor of renewable energy, while ensuring a competitive energy, in terms of costs, availability of products and their security and sustainability. Within this framework, Morocco launched in 2009 a national energy strategy whose major orientations focus on the security of energy supply and the generalization of its access, the preservation of the environment, through the use of renewable energy, energy efficiency, the strengthening of interconnection and regional cooperation. Through this article, the current state of the Moroccan network will be studied, as well as its potential in terms of renewable energy. Some strategies to overcome the challenges facing smart grid deployment in Morocco will also be presented. Then, the long-term energy demand, generation capacity, and renewable energy evolution in Morocco around 2030 will be estimated based on a time series using the artificial neural network method, which can be injected into the grid without causing any transit restrictions on the utility network or on the whole power system. As a result, the wind power available capacity was estimated to be 4087 MW, and the solar power available capacity was estimated to be 4713 MW by 2030. These results will be then compared to those estimated with the mathematical method. As well as, with the accuracy results of similar studies with different time series forecasting techniques. The accuracy value of this study is between 1.2% and 3.5%. So, the performance and viability of the proposed model can be studied.

Micro-grids can be considered as the ideal way to integrate renewable energy sources into electricity generation and to give consumers the opportunity to participate in the electricity market as consumers and producers. Our study aims to implement a micro-grid with solar and wind power generation and storage systems. However, the integration of a battery storage system into a micro-grid requires a thorough control of charge and discharge techniques due to the different load conditions. In this study, the proposed system can transfer electricity to and from the main grid. Although, the objective of the simulation is to control at the same time the energy input and output of the principal grid in order to maximize the profit and minimize the cost. To cope with the uncertainties of the system, a fuzzy logic controller for charge-discharge as well as a scheduling of battery energy storage systems is simulated on Matlab, in order to ensure the energy availability on demand and to take a proper decision whether to store or sell energy.

In response to the increasing complexity of modern production environments driven by heightened competition, customer demands, and sustainability goals, this work presents a focused methodology for forming product families within reconfigurable manufacturing systems (RMSs). Unlike traditional approaches, our method emphasizes the practical implementation of the Analytic Hierarchy Process (AHP) and the Average Linkage Clustering (ALC) algorithm to optimize RMS configurations. By evaluating specific comparison criteria—such as assembly sequence, machining sequence, components, production tools, and production demand—we aim to enhance resource utilization and adaptability to market changes. The proposed methodology enables a systematic assessment of RMS performance tailored to diverse product requirements. A detailed example of machining systems demonstrates the use of machining sequence and tool usage as primary criteria, showcasing the practical application and decision-making capabilities of the approach. This work contributes to the field by providing a structured framework for decision-making in RMS, facilitating efficient and precise product family formation to meet evolving manufacturing demands.