Physics-aware reinforcement learning for integrated energy systems management

This chapter introduces the concepts needed to understand physics-aware reinforcement learning (RL) for managing integrated energy systems (IESs), highlighting its potential to replace traditional methods. The IES incorporates various forms of energy supply, energy storage equipment, and energy conversion systems, facilitating the integration of different types of energy across several links such as source, charge, and network. To attain optimal techno-environmental-economic operational goals while ensuring a reliable power grid amidst intermittent renewable generation, novel operating paradigms are required for computationally swift and precise decision-making in dynamic environments. Research on RL has attracted scholars and scientists from various disciplines, such as smart grid, energy management, renewable energies, energy storage, and automation. The objective of this method is to coordinate multiple energy sources and achieve optimal operation across various hourly dispatches, thereby enhancing overall energy utilization efficiency. Eventually, an example of IES management using RL method is provided in the hybrid networks. Taking a holistic approach, this chapter establishes a fundamental framework for the topic and discusses the engineering design constraints, optimal planning, reducing emissions, and economical operation, as well as applications of IESs in hybrid networks.