MANAGEMENT OF MICROGRIDS IN SOUTH ALGERIAN : ENGINEERING AND SCIENTIFIC CHALLENGES

Abstract

Over the past decades, numerous global initiatives have aimed to promote autonomous and sustainable renewable energy systems. These efforts have led to the development of power generation technologies capable of ensuring energy self-sufficiency through the integration of multiple renewable sources. Among these systems, hybrid microgrids play a crucial role, enabling the flexible incorporation of local resources while maintaining a stable and reliable power supply, especially in remote areas. In Algeria, particularly in the southern regions, solar and wind energy are the most abundant resources. The Algerian Sahara, located near the Tropic of Cancer, experiences extremely high temperatures, intense sunlight, and arid conditions. These circumstances make the Sahara ideal for deploying hybrid microgrids adapted to dispersed rural communities. The optimal design and management of such systems remain challenging. In this context, the integration of artificial intelligence (AI) in microgrid planning, control, and operation has emerged as a promising solution. AI allows the analysis of large datasets to optimize electricity generation, storage, and distribution in real time. Machine learning algorithms can predict solar and wind production and dynamically adjust management strategies. The main objective of this project is to propose intelligent hybrid microgrid architectures tailored to the climatic and socio-economic conditions of the Algerian Sahara. A comprehensive energy, economic, and environmental assessment will determine the most efficient and sustainable configurations. The study also highlights the role of AI tools in decision-making, generation forecasting, and smart demand management. Ultimately, this work aims to strengthen the energy autonomy of rural communities in the Algerian Sahara through the integration of renewable energy sources and artificial intelligence, paving the way for a more resilient, clean, and intelligent energy future.