Applications of Artificial Intelligence in Smart Grid Systems and Their Impact on Accessible and Sustainable Energy

The rapid modernization of electrical grids necessitates the integration of intelligent computing technologies to handle the high volatility and bidirectional complexity of decentralized renewable energy networks. This paper provides a comprehensive analysis of Artificial Intelligence (AI) applications within modern smart grid systems and establishes their direct strategic contribution to United Nations Sustainable Development Goal 7 (SDG 7: Affordable and Clean Energy). Utilizing a systematic analytical synthesis and conceptual framework mapping approach, the study evaluates how machine learning, deep learning, and reinforcement learning optimize the three main vectors of SDG 7: universal access, increasing renewable energy shares, and doubling energy efficiency. The findings reveal that AI-driven predictive modeling reduces renewable generation forecasting errors by up to 25%, while deep-reinforcement-learning-based demand-side management optimizes distribution network efficiency and asset lifetimes. Furthermore, a detailed evaluation of multi-agent systems highlights their utility in optimizing Virtual Power Plants (VPPs) and smart grid edge devices, directly lowering operating costs and ensuring grid stability. The paper concludes with a strategic implementation framework tailored for global power utilities, highlighting governance, cyber security, and model interpretaability as critical factors for secure AI deployment in the transition to sustainable energy ecosystems.