A New Multi-Iteration Adaptive Dynamic Programming Method for Smart Grid Systems

This study introduces a novel energy optimization strategy for smart grid systems (SGS). It integrates renewable energy sources into energy management and incorporates the feedback sale of excess renewable energy, achieving superior optimization and reducing energy waste. A periodic iterative selflearning approach is designed based on the near-periodic nature of SGS data. The self-learning optimization technique leverages adaptive dynamic programming (ADP). Experimental results demonstrate that this innovative energy management approach significantly reduces electricity costs for users and outperforms conventional methods.