Smart grids are a major improvement on the conventional electrical grids as they incorporate digital communications, automated control, and sophisticated sensing systems in order to maximize power generation, distribution, and utilization. The paper will examine how Artificial Intelligence (AI) can be used to optimize energy systems in smart grid systems. AI, as a result of machine learning, reinforcement learning, and genetic algorithms, can solve the major problems of energy distribution, load balancing, fault detection, and renewable energy integration. The primary task of the paper is to review how the AI-oriented optimization methods can be used to improve the efficiency, stability, and cost-efficiency of smart grids. The approach implies the use of several AI methods on real-time energy usage data, renewable energy data, and grid performance indicators. The findings indicate that there is an essential improvement in the energy efficiency, cost decrease, and stability of the systems. The AI-optimized grid realized the 15 % energy efficiency, 12 % operational cost reduction, and a 20 % grid stability. Moreover, the percentage of renewable energy sources integration was increased by 18 %, which demonstrates the capabilities of AI to cope with the fluctuations in the generation of renewable energy. The results indicate that AI can transform smart grid management by making the systems more adaptive and efficient. Nevertheless, there are still difficulties, especially with the quality of the data, generalization of the model, and scalability. The future discipline of AI-based energy systems must focus on how to enhance the strength of AI models, how to integrate AI models with other emerging technologies like blockchain, and how to consider the policy implications of AI-based energy systems.
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