Optimization of renewable energy integration in smart grids using AI and data analytics

This paper aims to optimize the integration of renewable energy sources into smart grids using artificial intelligence (AI) and data analytics, addressing the challenges posed by the intermittency and variability of renewable energy. The research methodology involves designing an AI-based energy management system that incorporates data analytics, optimization techniques, and renewable energy technologies. The system architecture includes modules for energy generation, storage, and transmission, with key technologies such as smart control strategies and real-time demand response systems. The study employs AI optimization algorithms, including Genetic Algorithms (GA), Particle Swarm Optimization (PSO), and Deep Learning (DL), to dynamically adjust the system based on real-time data inputs. The results demonstrate that Deep Learning outperforms GA and PSO across all metrics, achieving the highest energy efficiency (95%), cost savings (80%), and CO2reduction (70%). The significance of this research lies in its contribution to the reliability and sustainability of energy systems by effectively optimizing energy efficiency, cost savings, and environmental impact. The innovation of this paper is the introduction of a multi-objective optimization framework that simultaneously considers energy efficiency, economic cost, and environmental impact, enabling real-time optimization of the energy management system based on intelligent algorithms. This research underscores the importance of advanced AI-based optimization techniques in the design and operation of smart grids, particularly as the share of renewable energy sources continues to increase.

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