Reinforcement learning-driven optimization of incentive-based demand response in distribution network with optimal placement of DG

Key Points

• Demand response optimization improves energy management for distribution networks with better results.
• The model uses reinforcement learning techniques to enhance demand response efficiency and system reliability.
• Analysis of smart grids demonstrates successful optimal placement of distributed generation sources for better energy distribution.
• Findings indicate potential for enhanced energy efficiency, supporting broader integration of renewable energy sources.