Advancing microgrid cyber resilience: Fundamentals, trends and case study on data-driven practices

Microgrids (MGs) serve as the core components of the upcoming sustainable power systems, and ensuring their security against cyber threats presents a critical research challenge due to the widespread use of advanced energy technologies. This paper explores various strategies for maintaining the cyber-resilient operation of MGs, focusing on technical, economic, and regulatory frameworks, in addition to their operational essentials for seamless functionality. In this paper, cyber-resilient operation refers to the system’s ability to withstand, respond to, and recover from cyber incidents, thereby ensuring the continuous and reliable operation of the MG. An outline of the various security challenges linked to different cyber-attacks and MG frameworks, highlighting the importance of developing effective and adaptable solutions, is also studied in this paper. While model-based approaches offer precise detection accuracy under steady-state conditions, they often struggle in real-time dynamic scenarios due to their complexity and dependence on accurate system modeling. Conversely, data-driven approaches offer enhanced flexibility and adaptability, enabling swift responses to emerging cyber threats. This makes them a compelling alternative to dynamic model-based methods for ensuring cyber-secure operations of MGs. This study focuses on data-driven techniques, acknowledging the comparative strengths and limitations of both paradigms. This paper also outlines crucial steps for crafting scalable and efficient data-driven cyber solutions, highlighting their key characteristics that enhance MG security. It provides a thorough overview of recent data-driven cyber solutions for MGs, offering careful analysis to evaluate the effectiveness of these methods in enhancing security while identifying operational and implementation challenges. A case study on a two-area isolated microgrid is presented, where a data-driven framework optimized by Bayesian learning approximation is examined. This case study demonstrates the capability of the studied data-driven framework in enhancing the resilience of IMGs against cyber threats. Ultimately, the paper concludes with recommendations for the field of data-driven cyber solutions and MGs, aiming to foster further advancements in sustainable and reliable cybersecurity measures for MG frameworks.