Enhancing Energy Efficiency in Smart Grids through Reinforcement Learning-Based Control Strategies

Abstract

The rapid growth of smart grids has ushered in new opportunities for enhancing energy efficiency through advanced control strategies. This paper explores the potential of reinforcement learning (RL) to improve the energy efficiency of smart grids, focusing on RL-based control strategies. We begin with a comprehensive review of existing technologies, examining a range of architectures and methods used to implement RL in smart grid environments. This review highlights both the benefits and limitations of these approaches, offering a balanced analysis of their effectiveness in addressing the unique challenges of smart grid management. Following this review, we propose a new RL-based control strategy designed to optimize energy efficiency. Our approach leverages the strengths of state-of-the-art RL algorithms while addressing common shortcomings identified in previous work. We evaluate our strategy using a detailed simulation that reflects real-world smart grid scenarios. The results demonstrate significant improvements in energy efficiency compared to traditional control methods. Finally, we discuss best practices for applying RL in smart grids, providing guidelines for researchers and practitioners seeking to implement these strategies. Our recommendations focus on maximizing energy efficiency while ensuring stability and scalability in smart grid systems. Through this work, we aim to contribute to the ongoing development of sustainable and efficient smart grid technologies.