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Title |
Autonomous Energy Management System for Smart Cities via Demand Response and P2P Trading |
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Author |
Sami Ben Slama and Ahmed Albugmi |
| Citation |
Vol. 26 No. 3 pp. 1-14 |
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Abstract |
Smart cities are increasingly dependent on efficient reliable, and sustainable energy systems. To address coordination challenges posed by rebewable energy management strategy was designed. This approach integrates demand response analysis and peer-to-peer energy trading within urban energy communities, using artificial intelligence. Learning-based models forecast local demand and production, which reinforcement learning manages dynamic scheduling for community energy storage and exchange. Through improved anticipation of household demand, local production, storage operations, and inter-community energy exhanges, the strategy delivers three key benefits: reduced electricity costs, lower peak demand, and enhanced system resilience during outages. THese results highlight the critical role of smart coordination in aligning energy supply and vehicle charging in modern cities. |
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Keywords |
Artificial Intelligence, Smart Cities, Energy management, Demand response, Peer-to-peer energy trading, Renewable energy integration, Grid resilience |
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