Integrating solar power into smart grids is challenging because of the variable nature of solar energy.This study focuses on implementing reinforcement learning(RL)using a Deep Q-Network algorithm to enhance the stabi...Integrating solar power into smart grids is challenging because of the variable nature of solar energy.This study focuses on implementing reinforcement learning(RL)using a Deep Q-Network algorithm to enhance the stability and efficiency of a grid.A custom environment was designed using OpenAI Gym,in which real-time simulation of grid operations was conducted using real-time data on solar power,weather,and other grid metrics.The trained RL agent exhibited high predictability in optimally distributing the load and managing the battery storage,with R-squared=0.886,mean average error=1,173,046.55 Wh,and root mean squared error=2,075,515.10 Wh.The model effectively captured the seasonality and daily variations in solar power generation.Forecasting using the proposed model provides insights into future energy trends and uncertainties.The reward function will be further refined and scaled for more complex energy systems by incorporating additional variables and hybrid approaches.This study highlights the potential of RL-based adaptive control strategies for developing more efficient and resilient integration of renewable energy sources into smart grids.展开更多
文摘Integrating solar power into smart grids is challenging because of the variable nature of solar energy.This study focuses on implementing reinforcement learning(RL)using a Deep Q-Network algorithm to enhance the stability and efficiency of a grid.A custom environment was designed using OpenAI Gym,in which real-time simulation of grid operations was conducted using real-time data on solar power,weather,and other grid metrics.The trained RL agent exhibited high predictability in optimally distributing the load and managing the battery storage,with R-squared=0.886,mean average error=1,173,046.55 Wh,and root mean squared error=2,075,515.10 Wh.The model effectively captured the seasonality and daily variations in solar power generation.Forecasting using the proposed model provides insights into future energy trends and uncertainties.The reward function will be further refined and scaled for more complex energy systems by incorporating additional variables and hybrid approaches.This study highlights the potential of RL-based adaptive control strategies for developing more efficient and resilient integration of renewable energy sources into smart grids.
