An Overview of the Prospects and Challenges of Using Artificial Intelligence for Energy Management Systems in Microgrids

TL;DR

This paper surveys the prospects and challenges of applying artificial intelligence to energy management systems in microgrids. It synthesizes AI techniques across deep learning, RL, GANs, and graph-based methods to enhance forecasting, peak shaving, predictive maintenance, and cybersecurity, and it discusses current proof-of-concept results. The authors identify key obstacles including data quality, interoperability, scalability, regulation, explainability, and bias, and they propose future directions such as self-healing microgrids, blockchain integration, IoT, and generative AI. The work underscores the potential of AI-enabled EMS to improve energy efficiency, resilience, and cost-effectiveness in microgrids, while outlining a pragmatic roadmap for research and standardization to enable real-world deployment.

Abstract

Microgrids have emerged as a pivotal solution in the quest for a sustainable and energy-efficient future. While microgrids offer numerous advantages, they are also prone to issues related to reliably forecasting renewable energy demand and production, protecting against cyberattacks, controlling operational costs, optimizing power flow, and regulating the performance of energy management systems (EMS). Tackling these energy management challenges is essential to facilitate microgrid applications and seamlessly incorporate renewable energy resources. Artificial intelligence (AI) has recently demonstrated immense potential for optimizing energy management in microgrids, providing efficient and reliable solutions. This paper highlights the combined benefits of enabling AI-based methodologies in the energy management systems of microgrids by examining the applicability and efficiency of AI-based EMS in achieving specific technical and economic objectives. The paper also points out several future research directions that promise to spearhead AI-driven EMS, namely the development of self-healing microgrids, integration with blockchain technology, use of Internet of things (IoT), and addressing interpretability, data privacy, scalability, and the prospects to generative AI in the context of future AI-based EMS.

Figures (6)

• Figure 1: General overview of tasks related to EMS
• Figure 2: Basic wheel diagram of AI and machine learning methods
• Figure 3: Representation of nodes of autoencoders
• Figure 4: Operation costs as a function of penetration and uncertainty level of RESs as a percentage of the hourly total demand
• Figure 5: Convergence processes of five different types of MGs with HRL and general fuzzy Q-learning