To improve electrical distribution network reliability, some portions of the network could operate in autonomous mode, provided that the related technical issues are addressed. More specifically, when there is not a path from those portions to the primary substation due to a fault in the network, such portions could be disconnected from the main network and supplied by local generation only. Such a mode of operation is known as “intentional islanding” and its effectiveness, in terms of adequacy, depends on the ability of the local generation to meet the island’s load. In fact, the ratio between the available local generation and load demand can frequently change during islanding due to load variations and, especially, due to the strongly irregular behavior of the primary energy sources of renewable generators. This paper proposes an analytical formulation to assess local generation adequacy during intentional islanding, accounting for the aforementioned variations. More specifically, the fluctuations of load and green-energy generators during islanding are modeled by means of Markov chains, whose output quantities are encompassed in the proposed analytical formulation. Such a formulation is used by the analytical equations of load points’ outage rate and duration. The evaluation of the reliability indices accounts for a protection scheme based on an appropriate communication infrastructure. Therefore, a brief overview on the telecommunications technologies has been presented with reference to their suitability for the specific application. In particular, distribution network safety issues have been considered as the main concern. The results show that neglecting load and generation fluctuations leads to a strong overestimation of the ability of distributed generators to meet the island load. Through a case study it is observed that the error on the load point outage rate is greater than the one affecting the outage duration.

The growing penetration of Electric Vehicles (EVs) in power distribution networks presents both challenges and opportunities for grid operators and planners. This paper provides a comprehensive review of recent advances in optimization techniques and machine learning (ML) approaches for the efficient management of EV charging and integration in low- and medium-voltage distribution systems. Optimization methods are analyzed with reference to their objectives—such as load flattening, voltage regulation, loss minimization, and infrastructure cost reduction—and their capability to handle multi-objective, stochastic, and real-time constraints. Concurrently, the role of ML is explored in load forecasting, user behavior modeling, anomaly detection, and adaptive control strategies. Particular attention is given to hybrid approaches that combine optimization algorithms (e.g., MILP, heuristic methods) with data-driven models (e.g., neural networks, reinforcement learning), highlighting their effectiveness in enhancing grid flexibility and resilience. This review adopts a unified system-level perspective that links EV management objectives, optimization techniques, and machine learning-based solutions within distribution networks. In addition, particular attention is devoted to data availability, reproducibility, and practical deployment aspects, with the aim of identifying current limitations and providing actionable insights for future research and real-world applications. This study aims to support the development of intelligent energy management strategies for EVs, fostering a sustainable and reliable evolution of distribution networks.

Traditional voltage regulation systems for distribution networks fail to achieve their objective in presence of local generation resources as they are designed for passive distribution systems and based on assumptions that may not hold if distributed generation (DG) is connected to the network. An advanced centralised voltage regulation system has been studied by the authors to allow a greater DG penetration and a high quality service to customers. However, depending on the characteristics of the specific network, it could be sometimes impossible to solve some operating conflicts between voltage regulation and correct DG operation. In practice, in order to connect a greater amount of DG, it might be necessary, not only to revise the employed regulation criteria, but also to act at the planning stage to make specific changes in the structure of a distribution system. In this view the paper presents a planning procedure that, besides existing distribution system data bases, load variation forecast, environmental/climatic conditions, and planners experience, takes into account information directly provided by the operation of the aforementioned advanced voltage regulation system.

The energy transition and sustainability are the critical challenges of our time, and cities are at the forefront of this transformation. As key players, these urban centers are pioneering innovative strategies to reduce carbon emissions, enhance energy efficiency, and promote renewable energy sources. Central to the transition is the role of electricity, which acts as a cornerstone in the “cocktail” of sustainable solutions. The development of an efficient and resilient “Electricity City Grid” is essential to support this shift. This article explores the target functions and challenges associated with designing and operating the Smart Electricity City Grid of the future. It delves into the infrastructural costs required for this transition and examines the economic and technical hurdles that must be overcome. Finally, the article looks ahead to the future of research in this field, highlighting the areas that will be crucial for the continued evolution and success of smart urban grids. Through this comprehensive analysis, we aim to suggest a roadmap for cities striving to achieve sustainability through advanced electrical infrastructure.