A study on challenges and solutions in artificial intelligence-driven demand side optimization and security enhancement for smart grids

In modern smart grids, demand-side management (DSM) plays a vital role in enhancing energy efficiency, balancing supply and demand, and managing distributed energy resources. Conventional DSM approaches, however, often lack the flexibility, scalability, and security required for decentralized energy systems. The integration of Advanced Metering Infrastructure (AMI) and growing dependence on communication networks further introduce new challenges. Artificial Intelligence (AI)-driven solutions offer promising pathways by enabling intelligent demand response and enhanced security. This paper reviews the role of Machine Learning (ML), Deep Reinforcement Learning (DRL), and blockchain in reshaping DSM strategies. ML and DRL provide intelligent adaptability through real-time data processing, demand forecasting, and autonomous decision-making, while blockchain ensures decentralized data security, privacy, and trust. The study highlights their combined potential for efficient, secure, and resilient DSM in future smart grids.