Decentralized Multi-AI Agent Control for Smart Grid Energy Management: Recent Advances and Applications

Modern power grids need ways to manage new forms of renewable energy as well as changing load patterns, all of which will require improved management tools. This paper reviews literature that explores the application of decentralized multi-agent AI systems for smart grid energy management. A number of researchers have demonstrated that autonomous multi-agents working cooperatively with one another can perform smarter and more efficient utility operations than traditional centralized management systems. The research summarized here demonstrates how decentralized AI multi-agent systems utilizing deep reinforcement learning (DRL) can enhance demand response, load forecasting, and overall system stability. In one simulation study with five prosumers, participants saved 8.2–48.8% on their electricity bills while the service provider increased its profit by 25.7% and cut reserve power usage by 16%. The paper also discusses several barriers to implementation including computational costs, data quality, and regulatory compliance, along with possible future applications of quantum computing and cross-grid coordination.