Reinforcement Learning for Sustainable Energy: A Survey

TL;DR

This survey reviews how reinforcement learning techniques are applied to various sustainable energy challenges, highlighting current methods, themes, and future research directions to facilitate the energy transition.

Contribution

It systematically connects reinforcement learning approaches with sustainability challenges, providing a comprehensive overview and identifying key themes and standardization needs.

Findings

Reinforcement learning effectively models energy management problems.

Multi-agent and safe RL are prominent themes in sustainable energy applications.

Standardized environments are crucial for advancing research in this field.

Abstract

The transition to sustainable energy is a key challenge of our time, requiring modifications in the entire pipeline of energy production, storage, transmission, and consumption. At every stage, new sequential decision-making challenges emerge, ranging from the operation of wind farms to the management of electrical grids or the scheduling of electric vehicle charging stations. All such problems are well suited for reinforcement learning, the branch of machine learning that learns behavior from data. Therefore, numerous studies have explored the use of reinforcement learning for sustainable energy. This paper surveys this literature with the intention of bridging both the underlying research communities: energy and machine learning. After a brief introduction of both fields, we systematically list relevant sustainability challenges, how they can be modeled as a reinforcement learning problem, and what solution approaches currently exist in the literature. Afterwards, we zoom out and identify overarching reinforcement learning themes that appear throughout sustainability, such as multi-agent, offline, and safe reinforcement learning. Lastly, we also cover standardization of environments, which will be crucial for connecting both research fields, and highlight potential directions for future work. In summary, this survey provides an extensive overview of reinforcement learning methods for sustainable energy, which may play a vital role in the energy transition.