Abstract
Increased electrification of various critical infrastructures has been recognized as a key to achieving decarbonization targets worldwide. This creates a need to better understand the risks associated with future power systems and how such risks can be defined, assessed, and mitigated. This paper surveys prior work on power system risk assessment and management and explores the various approaches to risk definition, assessment, and mitigation. As a result, the paper proposes how future grid developments should be assessed in terms of risk causes, what methodology may be used to reduce the risk impacts, and how such approaches can increase grid resilience. While we attempt to generalize and classify various approaches to solving the problem of risk assessment and mitigation, we also provide examples of how specific approaches undertaken by the authors in the past may be expanded in the future to address the design and operation of the future electricity system to manage the risk more effectively. The importance of the metrics for risk assessment and methodology for quantification of risk reduction are illustrated through the examples. The paper ends with recommendations on addressing the risk and resilience of the electricity system in the future resilient implementation while achieving decarbonization goals through massive electrification.

Abstract
This paper presents an adaptive protection strategy for multi-microgrid (MMG) systems with inverter-based resources (IBRs) in medium voltage (MV) networks, using the IEEE 33-bus test system. The approach combines overcurrent (OC) and undervoltage (UV) protections through an offline-optimized, clustering-based scheme and real-time selection of setting groups. A metaheuristic algorithm determines optimal relay settings for representative scenarios, ensuring responsive and coordinated protection. Hardware-in-the-loop validation on OPAL-RT confirms the method's effectiveness across varying loads, DER outputs, and fault conditions. Results demonstrate reliable fault isolation, smooth mode transitions, and uninterrupted supply to healthy segments. Identified limitations in high-impedance fault handling suggest future improvements.

Abstract
The increasing penetration of inverter-based resources (IBR) in medium voltage (MV) networks presents significant challenges for traditional overcurrent (OC) protection systems, particularly in ensuring selectivity, reliability, and fault isolation. This paper presents an adaptive protection system (APS) that dynamically adjusts protection settings based on real-time network conditions, addressing the challenges posed by distributed energy resources (DER). The methodology builds on ongoing research and development efforts, combining an offline phase, where operational scenarios are simulated using historical data, clustered with fuzzy c-means (FCM), and optimized with evolutionary particle swarm optimization (EPSO), and an online phase. To overcome the static nature of conventional schemes, a machine learning (ML)-based classifier is integrated into the APS, enabling real-time adaptation of protection settings. In the online phase, a centralized substation protection controller (CPC) leverages real-time measurements, communicated via IEC 61850 standard protocols, to classify network conditions using a support vector machine (SVM) classifier and activate the appropriate protection settings. The proposed APS has been validated on a Hardware-in-the-Loop (HIL) platform, demonstrating significant improvements in fault detection times, selectivity, and reliability compared to traditional OC protection systems. As part of a continued effort to refine and expand the system's capabilities, this work highlights the potential of integrating artificial intelligence (AI) and real-time/online decision-making to enhance the adaptability and robustness of MV network protection in scenarios with high DER penetration. © 2025 Elsevier B.V., All rights reserved.