A Hierarchical Hybrid Rule-Based and SARSA Reinforcement Learning Framework for Safe Energy Management in Renewable Microgrids

Abstract

Microgrids must efficiently manage energy under uncertainties in renewable generation and load demand to ensure reliable and cost-effective operation. This paper investigates a microgrid system that involves renewable energy through the photovoltaic system, wind system, battery energy storage, and local load requirement with a centralized energy management system. The inflexible nature of traditional rule-based and optimizationbased approaches to solving problems can often create issues in reflection to dynamic operating conditions, and reinforcement learning approaches can produce unsafe control behavior in exploration stages. To address these issues, this paper suggests a hierarchical hybrid energy management structure that will integrate rule-based supervision and a SARSA reinforcement learning controller. Supervisory layer ensures that the system is safe by ensuring that there are operational limits such as battery state of charge limits as well as power balance conditions. The learning agent on the other hand optimizes the control choices to reduce operational costs and grid energy consumption. The outcomes of the simulation indicate that the suggested approach saves more money, learns quicker, and operates a microgrid in a stable way compared to stand alone rule-based and reinforcement learning techniques. The findings demonstrate that deterministic safety rules with adaptive reinforcement learning is an effective and helpful approach to managing energy in smart microgrids.