ENERGY OPTIMIZATION IN SMART GRIDS WITH DEEP REINFORCEMENT LEARNING

Abstract

The escalating complexity, uncertainty, and data volumes in energy systems have rendered conventional methods ineffective in addressing decision-making and control challenges. As a result, data-driven approaches have become a crucial focus area. Deep reinforcement learning (DRL) represents a significant breakthrough in data-driven technology, earning its reputation as a true form of artificial intelligence (AI). By combining the capabilities of deep learning (DL) and reinforcement learning (RL), DRL gives rise to a robust and adaptive approach that excels in complex decision-making and control scenarios. With its successful applications in various domains, DRL has been increasingly applied to optimize energy systems, including energy management, demand response, smart grids, and operational control. This paper provides a thorough review of DRL's fundamental principles, models, and algorithm, followed by an in-depth exploration of its applications in energy optimization. Furthermore, the paper discusses recent breakthroughs in DRL, including its integration with traditional methods, and examines the opportunities and challenges of its applications in the energy sector