This article provides a comprehensive review of power grid resilience, including current metrics and definitions, as well as the procedures used to ensure and improve the resilience of a system. We also describe the different strategies used by users to ensure their own resilience. Additionally, this article highlights areas for future research and opportunities for the integration of emerging technologies such as computer vision. The main objective of this study was to explore the metrics and strategies used in power grids and for the users to improve and ensure resilience in case of events.

This document lists some challenges that researchers face when implementing transactive energy in weak power grids. These challenges often include high voltage fluctuations, limited generation, high line loadability, and unbalanced grids. The operation of transactive energy, as well as optimization techniques, are also considered, highlighting the performance and functionalities depending on power grid characteristics and market topology. Some of the most used optimization techniques for market clearing, considering the characteristics and topology, are presented as part of the research work.In addition, this paper compares different market topologies and highlights their advantages and challenges. Furthermore, this paper contains a brief description of the interoperability frameworks applied to a smart grid.As a result, it was determined that interoperability is necessary for the proper functioning of the grid. Moreover, all methods were found to be effective for their purpose from the user’s point of view as each technique has different characteristics relevant to the user and the grid. It was concluded that it is convenient to combine the optimization techniques to consider different constraints in the market clearing.

The sum-capacity of the multi-antenna Gaussian broadcast channel is known to be achieved by Dirty Paper Coding techniques, that require full channel state information at the base station. It has been recently shown that a sum-rate having the same scaling law of the sum-capacity with respect to the number of users n for a fixed signal to noise ratio (i.e., M log log n where M is the number of transmitting antennas) can be achieved by using reduced feedback (or equivalently reduced channel state information at the transmitter). In particular, it has been proved that n real and n integer numbers are enough to guarantee the optimal scaling law. In this paper, the optimal scaling law of the sum-rate is shown to be achievable with an even smaller amount of feedback and, more precisely, with 1) n log2(M + 1) bits, if a deterministic feedback scheme is employed; 2) an average number of feedback bits that scales as M log2 M log n with the number of users n, if a selective (random) feedback scheme is employed.

Power grids are increasingly exposed to high-impact disturbances driven by extreme weather, cyber–physical threats, and the growing penetration of converter-based renewable resources. In this context, Machine Learning (ML) has emerged as a key enabler for resilience-oriented monitoring, prediction, control, and restoration. This paper presents a structured review of ML strategies for power-grid resilience applications using a four-phase resilience lens (Prevention and Improvement, Control and Mitigation, Restoration, and Cyber Resilience). The literature is organized through a functional taxonomy that includes fault diagnosis, event prediction, control and stability support, restoration, and cyber resilience. In addition to the qualitative synthesis, a quantitative analysis of a dataset of 13,647 peer-reviewed publications (2015–2026) is conducted to characterize research activity across resilience functions and implementation contexts. This analysis is used to illustrate the increasing adoption of machine learning approaches and to distinguish between simulation-based and real-world applications. The results indicate a methodological shift toward Deep Learning and Reinforcement Learning for complex tasks, while federated and edge-based approaches are gaining attention for privacy preserving and real-time applications. These findings provide a structured view of current research directions and support the growing relevance of machine learning in resilience-oriented power system applications, offering a foundation for the development of intelligent and scalable cyber–physical energy systems.