Energy-Efficient Resource Allocation for Smart Grids using Deep Reinforcement Learning in IoT Cloud Systems

This study describes the use of deep reinforcement learning (DRL) models to fine-tune resource allocation for smart grids in order to reduce energy waste and operational costs. The research experiment evaluates four different DRL algorithms, which include SAC, DDPG, PPO, and A3C. It demonstrates performance through various evaluation metrics, which include impeccable energy efficiency, perfect resource utilization, load balancing ratio, average response time, and computational overhead. SAC is proven to achieve 92.5% energy efficiency, 88.7% resource utilization, 90% load balancing ratio, and response time of 120ms. The computational overhead assessed supplements to 15 GFLOPS. DDPG showcased 90.1% energy efficiency and 85.4% resource utilization, 88.9% load balancing, a response time of 110 ms, and a computational overhead of 14.8 GFLOPS. On the other hand, PPO and A3C also proved to work well and have a promising future to be implemented in a dynamic grid. This analysis demonstrates that practical applications from DRL, including real-time decision-making and adaptive control approaches, can prove vital while assigning and working on energy tasks and renewable conduct. These models prove important and beneficial in enhancing the operational efficiency of the smart grid, understanding and learning continuously from real-time data. It will be a vital concept and idea that would be helpful in ensuring that smart grids work perfectly to help in a reduction in carbon footprints and effective energy resource management to ensure end-use service provider satisfactions.