Real-Time Renewable Energy Optimization in Smart Grids: FPGA and Reinforcement Learning Synergy

Abstract

Sustainable energy distribution in smart grid environments depends on the effective use of renewable resources. By using reinforcement learning approaches, this research improves on current FPGA-based algorithms to enable optimal decision-making. The main goals are to precisely allocate power to ensure energy conservation and cost-effectiveness, automatically transition to alternate sources when unfavourable conditions arise, and continuously monitor the states of renewable resources. This study provides an extensive framework that includes hardware implementation, algorithmic decision-making, and circuit monitoring. Furthermore, the suggestion is to incorporate algorithms for reinforcement learning to improve decision-making. Through interaction with the surroundings, the reinforcement learning agents pick up the best practices for dynamically adjusting the distribution of power depending on real-time data inputs. This paper demonstrates, through research and testing, how the proposed method enhances system performance, reliability, and productivity.