Research explores potential of smart grid energy optimization

SUNY Poly Assistant Professor Dr. Mahmoud Badr and friends just lately published analysis titled “Reinforcement Learning for Fair and Efficient Charging Coordination for Smart Grid,” within the journal Energies. The analysis investigates the usage of reinforcement studying (RL) to enhance the coordination of house battery system charging in a wise grid.

The first goal of the research was to boost each grid effectivity and equity amongst customers. The system makes use of an actor-critic RL algorithm to regulate charging schedules dynamically, balancing grid constraints, particular person battery capacities, and client wants. The research experiences vital positive factors in complete rewards, equity in vitality distribution, and general buyer satisfaction.

This analysis has the potential to optimize vitality utilization inside sensible grids, which is more and more vital as renewable energy sources and distributed vitality storage programs turn into extra widespread.

By implementing honest and environment friendly charging mechanisms, the method can assist stability vitality provide and demand whereas decreasing pressure on the grid. That is essential for enhancing the steadiness of sensible grids, enhancing consumer satisfaction, and supporting the combination of renewable vitality programs, contributing to the broader aim of sustainable vitality administration.