Artificial Intelligence in the Hierarchical Control of AC, DC, and Hybrid AC/DC Microgrids: A Review

Growing concerns about the energy and environmental crisis are accelerating the transition to a sustainable energy generation landscape through the integration of distributed generators (DGs) into the electric power systems. Microgrids (MGs) have developed as autonomous, localized energy solutions for integrating DGs, improving grid functionality and reliability by supporting both grid-connected and islanded operational modes. Furthermore, the dc nature of numerous DGs and domestic appliances, along with advances in power electronics, have driven research away from traditional ac MGs towards dc and even hybrid ac/dc MGs. This transition introduces greater complexity and a wider range of control scenarios. To address the challenges of these increasingly complex systems, this paper reviews the application of artificial intelligence (AI) in the hierarchical control structures of ac, dc, and hybrid ac/dc MGs. AI techniques show significant promise in enhancing control and operation due to their ability to learn and adapt from the system operational data. However, integrating these AI algorithms into real-world applications remains challenging, with substantial progress still required to make these methods viable in practical scenarios. This review highlights the latest research efforts, identifies the key contributions of AI to MG control, and outlines the limitations and future research opportunities in the field.