Multi-Agent Reinforcement Learning for Coordinated Smart Grid and Building Energy Management Across Urban Communities

Lei Qiu

doi:10.54097/3veq6255

Authors

Lei Qiu

DOI:

https://doi.org/10.54097/3veq6255

Keywords:

Multi-Agent Reinforcement Learning, Smart Grid, Building Energy Management, Urban Energy Systems, Demand Response, Distributed Control, Energy Optimization, Sustainable Infrastructure

Abstract

The increasing complexity of urban energy systems necessitates sophisticated coordination mechanisms between smart grids and building energy management systems to achieve optimal energy efficiency and sustainability goals. This research presents a novel Multi-Agent Reinforcement Learning (MARL) framework designed to facilitate coordinated energy management across interconnected urban communities. The proposed system employs distributed intelligent agents that operate autonomously while maintaining collaborative decision-making capabilities for energy distribution, consumption optimization, and demand response coordination. Through extensive simulation studies conducted across three metropolitan areas involving 1,247 residential and commercial buildings over a 12-month period, our findings demonstrate significant improvements in energy efficiency, with average reductions of 23.4% in peak demand loads and 18.7% in overall energy consumption costs. The MARL approach exhibits superior performance compared to traditional centralized control systems, particularly in handling dynamic load balancing and renewable energy integration challenges. These results contribute substantially to the advancement of sustainable urban energy infrastructure and provide practical insights for large-scale smart city implementations.

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