Power distribution reliability can be evaluated probabilistically by using the Monte Carlo Simulation (MCS) method. However, the evaluation of large distribution networks is time consuming, computationally intensive and often relies on simplified network reductions that do not adequately capture the reliability impact of downstream networks. To address this challenge, this paper proposes a method for developing distribution network reliability equivalents for integrated risk and reliability analysis. The method is enabled by an optimised MCS algorithm that reduces computational memory requirements and simulation time, and uses impact aggregation to derive Reliability Equivalent Low Voltage Networks (RELNs) that accurately represent the reliability contribution of detailed LV subsystems. The resulting reliability equivalents support rapid reliability and risk assess ment of large distribution networks with substantially lower computational burden. The proposed method of aggregating the impact of distribution network reliability equivalents is validated using three generic network topologies based on rural, suburban, and urban areas, in the United Kingdom and Europe, supplying progressively up to 10,000 customers. The average values and probability distributions of the resultant risk and reliability indices demonstrate that the use of equivalents can accurately represent the quality of supply of a wide range of distribution systems without the need for detailed modelling of all network components, allowing network operators to focus on developing solutions for their intrinsically complex networks.

The increasing reliance on Networked Microgrids (NMGs) for decentralized energy management introduces unprecedented cybersecurity risks, particularly in the context of False Data Injection Attacks (FDIA). While traditional FDIA studies have primarily focused on network-based intrusions, this work explores a novel cyber-physical attack vector leveraging Uncrewed Aerial Vehicles (UAVs) to execute sophisticated cyberattacks on microgrid operations. UAVs, equipped with communication jamming and data spoofing capabilities, can dynamically infiltrate microgrid communication networks, manipulate sensor data, and compromise power system stability. This paper presents a multi-objective optimization framework for UAV-assisted FDIA, incorporating Non-dominated Sorting Genetic Algorithm III (NSGA-III) to maximize attack duration, disruption impact, stealth, and energy efficiency. A comprehensive mathematical model is formulated to capture the intricate interplay between UAV operational constraints, cyberattack execution, and microgrid vulnerabilities. The model integrates flight path optimization, energy consumption constraints, signal interference effects, and adaptive attack strategies, ensuring that UAVs can sustain long-duration cyberattacks while minimizing detection risk. Results indicate that UAV-assisted cyberattacks can induce power imbalances of up to 15%, increase operational costs by 30%, and cause voltage deviations exceeding 0.10 p.u.. Furthermore, analysis of attack success rates vs. detection mechanisms highlights the limitations of conventional rule-based anomaly detection, reinforcing the need for adaptive AI-driven cybersecurity defenses. The findings underscore the urgent necessity for advanced intrusion detection systems, UAV tracking technologies, and resilient microgrid architectures to mitigate the risks posed by airborne cyber threats.

The 2025 blackout across the Iberian Peninsula marked a paradigm shift in the nature of power system failures, revealing how modern grids—dense with inverter-based resources and automated controls—can exhibit emergent instabilities that traditional design frameworks may not fully anticipate. This review examines the event through the lens of complex systems engineering, drawing on publicly available information and prior research to illustrate why a well-instrumented, high-renewables grid might still struggle to contain rapidly evolving disturbances. We begin by highlighting structural features—such as bottlenecks and asymmetric interconnections—that have been shown in the literature to influence the propagation of instability in transmission networks. Network-science tools, including algebraic connectivity, spectral radius and betweenness centrality, are discussed as methods used in previous studies to reveal latent fragilities. Attention then shifts to synchronization dynamics, where low-inertia conditions can amplify frequency deviations and interact with heterogeneous inverter response characteristics. Using models of cascading failures and percolation-type transitions, we outline how disruptions can propagate nonlocally, highlighting mechanisms identified in analytical and simulation studies. The analysis extends to fragility scoring systems, modular segmentation strategies and the spatial deployment of virtual inertia and fast-response resources—approaches proposed in prior research as part of a resilience toolkit aimed at containment and stabilization. Finally, we summarize emerging redesign directions emphasizing harmonized protection logic, regional operational cells and complexity-aware digital twins as increasingly important under conditions of uncertainty.

The 2025 Iberian blackout, a highly disruptive large-scale grid failure, underscores the increasing fragility of deeply interconnected and decarbonizing power systems. Traditional security paradigms, such as the N-1 criterion, were designed for isolated and predictable contingencies but are increasingly unable to anticipate or contain cascading failures. Using the Iberian blackout as a reference case, this review synthesizes global case studies to identify common structural drivers of modern power system vulnerability. This review argues that the reliability paradigm must shift from static, component-level robustness toward the management of dynamic failure propagation pathways, reduced system inertia, and cross-sector interdependencies. To operationalize this paradigm shift, this review proposes an integrated, multi-layered resilience framework. From an analytical perspective, this review details the transition toward probabilistic dynamic security assessment for characterizing failure propagation trajectories. At the engineering layer, controllable regional islanding, adaptive protection schemes, and artificial-intelligence-enhanced control execution enable rapid system stabilization within narrow operational time windows. To address computational complexity, this review proposes an integrated interdisciplinary methodological framework that leverages stochastic interaction graphs and digital twins to achieve multi-scale system observability, while employing deep reinforcement learning and coalitional game theory to coordinate fast-timescale physical control with slow-timescale market mechanisms. Finally, this review outlines a governance transition from prescriptive compliance toward continuous, state-aware risk management supported by unified cross-border resilience metrics. By reframing resilience as a dynamically managed systemic attribute, this review presents a structured agenda for safeguarding interconnected power grids against escalating cascading risks.

The increasing reliance on Networked Microgrids (NMGs) for decentralized energy management introduces unprecedented cybersecurity risks, particularly in the context of False Data Injection Attacks (FDIA). While traditional FDIA studies have primarily focused on network-based intrusions, this work explores a novel cyber-physical attack vector leveraging Unmanned Aerial Vehicles (UAVs) to execute sophisticated cyberattacks on microgrid operations. UAVs, equipped with communication jamming and data spoofing capabilities, can dynamically infiltrate microgrid communication networks, manipulate sensor data, and compromise power system stability. This paper presents a multi-objective optimization framework for UAV-assisted FDIA, incorporating Non-dominated Sorting Genetic Algorithm III (NSGA-III) to maximize attack duration, disruption impact, stealth, and energy efficiency. A comprehensive mathematical model is formulated to capture the intricate interplay between UAV operational constraints, cyberattack execution, and microgrid vulnerabilities. The model integrates flight path optimization, energy consumption constraints, signal interference effects, and adaptive attack strategies, ensuring that UAVs can sustain long-duration cyberattacks while minimizing detection risk. Results indicate that UAV-assisted cyberattacks can induce power imbalances of up to 15%, increase operational costs by 30%, and cause voltage deviations exceeding 0.10 p.u.. Furthermore, analysis of attack success rates vs. detection mechanisms highlights the limitations of conventional rule-based anomaly detection, reinforcing the need for adaptive AI-driven cybersecurity defenses. The findings underscore the urgent necessity for advanced intrusion detection systems, UAV tracking technologies, and resilient microgrid architectures to mitigate the risks posed by airborne cyber threats.

The island of Ushant, like many island communities, faces major energy challenges, exacerbated by climate change and dependence on fossil fuels. In this context, the design of sustainable energy solutions, reducing greenhouse gas emissions and ensuring reliable access to energy and maritime transport is essential. To achieve these goals, a sizing simulation tool based on Matlab Simulink has been developed. This tool integrates several renewable energy sources, including wind, tidal turbines and photovoltaic systems, as well as hydrogen production and storage. The aim of this work is to model and evaluate different energy scenarios for the island, taking into account its specific energy demand and the needs of its maritime mobility. Taking into account both local grid and maritime mobility energy needs is one of the originality of this work. Four scenarios have been formulated. The first and second aim to achieve total energy autonomy for the island and its maritime mobility, using a hybrid hydrogen vessel for the first and a battery powered electric vessel for the second. The third scenario ensures the complete coverage of the island’s electrical load, whereas the fourth scenario covers only 70

The effective management of shared resources within energy communities poses a significant challenge, particularly when balancing renewable energy generation and fluctuating demand. This paper introduces a novel optimization framework that integrates people flow data, modeled using the Social Force Model (SFM), with energy management strategies to enhance the efficiency and sustainability of energy communities. By combining SFM with the Non-dominated Sorting Genetic Algorithm III (NSGA-III), the framework addresses multi-objective optimization problems, including minimizing energy costs, reducing user waiting times, and maximizing renewable energy utilization. The study employs synthesized data to simulate an energy community with shared facilities such as electric vehicle (EV) charging stations, communal kitchens, and laundry rooms. Results demonstrate the frameworks ability to align energy generation with resource demand, reducing peak loads and improving user satisfaction. The optimization model effectively incorporates real-time behavioral dynamics, showcasing significant improvements in renewable energy utilization---reaching up to 88% for EV charging stations---and cost reductions across various scenarios. This research pioneers the integration of people flow modeling into energy optimization, providing a robust tool for managing the complexities of energy communities.

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Renewable energy-based microgrids (MGs) strongly depend on the implementation of energy storage technologies to optimize their functionality. Traditionally, electrochemical batteries have been the predominant means of energy storage. However, technological advancements have led to the recognition of hydrogen as a promising solution to address the long-term energy requirements of microgrid systems. This study conducted a comprehensive literature review aimed at analysing and synthesizing the principal optimization and control methodologies employed in hydrogen-based microgrids within the context of building microgrid infrastructures. A comparative assessment was conducted to evaluate the merits and disadvantages of the different approaches. The optimization techniques for energy management are categorized based on their predictability, deployment feasibility, and computational complexity. In addition, the proposed ranking system facilitates an understanding of its suitability for diverse applications. This review encompasses deterministic, stochastic, and cutting-edge methodologies, such as machine learning-based approaches, and compares and discusses their respective merits. The key outcome of this research is the classification of various energy management strategy methodologies for hydrogen-based MG, along with a mechanism to identify which methodologies will be suitable under what conditions. Finally, a detailed examination of the advantages and disadvantages of various strategies for controlling and optimizing hybrid microgrid systems with an emphasis on hydrogen utilization is provided.

In an era characterized by extensive use of and reliance on information and communications technology (ICT), cyber-physical power systems (CPPSs) have emerged as a critical integral of modern power infrastructures, providing vital energy sources to consumers, communities, and industries worldwide. The integration of ICT in these systems, while beneficial, introduces a rapidly evolving range of cybersecurity challenges that significantly threaten their confidentiality, integrity, and availability. To address this, our article offers a comprehensive and timely survey of the current landscape of cyber vulnerabilities in CPPS, reflecting the latest developments in the field up to the present. This includes an in-depth analysis of the diverse types of cyber threats to CPPS and their potential consequences, underscoring the necessity for a broad, multidisciplinary approach. Our review is distinguished by its thoroughness and timeliness, covering recent research to offer one of the most current overviews of cybersecurity in CPPSs. We adopt a holistic perspective, integrating technical, societal, environmental, and policy implications, thereby providing a more comprehensive understanding of cybersecurity in CPPSs. We delve into the complexities of cyberattacks, exploring sophisticated, targeted attacks alongside common threats, and emphasize the dynamic nature of cyber threats, providing insights into their evolution and future trends. Additionally, our review highlights critical yet often overlooked challenges, such as system visibility and standardization in security protocols, arguing their significance in enhancing CPPS resilience. Furthermore, our work gives special attention to the aspects of restoration and recovery postcyberattack, an area less emphasized in the existing literature. Through this comprehensive overview of the current state and evolving challenges of CPPS security, our article serves as an indispensable resource for research, practice, and policymaking dedicated to safeguarding the safety, reliability, and resilience of ICT-empowered energy systems.

Proactive participation of uncertain renewable generation in the day-ahead (DA) wholesale market effectively reduces the system marginal price and carbon emissions, whilst significantly increasing the volumes of real-time balancing mechanism prices to ensure system security and stability. To solve the conflicting interests over the two timescales, this article: 1) proposes a novel hierarchical optimization model to align with the actual operation paradigms of the hierarchical market, whereby the capacity allocation matrix is adopted to coordinate the DA and balancing markets; 2) mathematically formulates and quantitatively analyses the long-term driving factors of balancing actions, enabling system operators (SOs) to design efficient and well-functioning market structures to meet economic and environmental targets; 3) empowers renewable generating units and flexible loads to participate in the balancing market (BM) as 'active' actors and enforces the non-discriminatory provision of balancing services. The performance of the proposed model is validated on a modified IEEE 39-bus power system and a reduced GB network. Results reveal that with effective resource allocation in different timescales of the hierarchical market, the drop speed of balancing costs soars while the intermittent generation climbs. The proposed methodology enables SOs to make the most of all resources available in the market and balance the system flexibly and economically. It thus safeguards the climate mitigation pathways against the risks of substantially higher balancing costs.

Reliability analysis of large power networks requires accurate aggregate models of low voltage (LV) networks to allow for reasonable calculation complexity and to prevent long computational times. However, commonly used lumped load models neglect the differences in spatial distribution of demand, type of phase-connection of served customers and implemented protection system components (e.g., single-pole vs three-pole). This paper proposes a novel use of state enumeration (SE) and Monte Carlo simulation (MCS) techniques to formulate more accurate LV network reliability equivalents. The combined SE and MCS method is illustrated using a generic suburban LV test network, which is realistically represented by a reduced number of system states. This approach allows for a much faster and more accurate reliability assessments, where further reduction of system states results in a single-component equivalent reliability model with the same unavailability as the original LV network. Both mean values and probability distributions of standard reliability indices are calculated, where errors associated with the use of single-line models, as opposed to more detailed three-phase models, are quantified.

In order to reduce our dependence on fossil fuels, alternative energy production and storage methods are being actively pursued. Hydrogen, as an energy carrier, has been studied extensively for its applications to achieve this objective. This paper presents a case study for hydrogen generation and storage, and its further application in hydrogen-based local emobility on an island named Ouessant in France. Since hydrogen-based mobility has been the focus of latest research, the study in this paper proposes a first of its kind mobile hydrogen refueling station in France. The mobile refueling station comprises of several novel technologies, where the anion exchange membrane (AEM) electrolyser results in a more stable and reliable performance. Moreover, it suffers from lower degradation as compared to alkaline based electrolysers. Accordingly, the proposed mobile hydrogen station is highly versatile and can be adapted to operate on various renewable energy resources to ensure reduced dependence on fossil fuel-based systems. The refuelling station uses novel electrochemical compression methods to compress hydrogen to very high pressures. Due to compression being electrochemical, as opposed to mechanical, there is less wear and tear in the system which ensures long lifetime of the equipment, which also results in lower energy consumption for compression. The initial results show that such system is highly suitable for remote applications where other forms of hydrogen production and supply are not suitable.

The thermal model of dwellings is the basis for flexible energy management of smart homes, where heating load is a big part of demand. It can also be operated as virtual energy storage to enable flexibility. However, constrained by data measurements and learning methods, the accuracy of existing thermal models is unsatisfying due to time-varying disturbances. This paper, based on the edge computing system, develops a dark-grey box method for dwelling thermal modelling. This dark-grey box method has high accuracy for: i) containing a thermal model integrated with time-varying features, and ii) utilising both physical and machine-learning models to learn the thermal features of dwellings. The proposed modelling method is demonstrated on a real room, enabled by an Internet of Things (IoT) platform. Results illustrate its feasibility and accuracy, and also reveal the data-size dependency of different feature-learning methods, providing valuable insights in selecting appropriate feature-learning methods in practice. This work provides more accurate thermal modelling, thus enabling more efficient energy use and management and helping reduce energy bills.

Maritime transport is one of the main drivers of a country’s economic growth. Up to 90% of world trade is carried out by sea. However, the high consumption of fossil fuels leads to significant greenhouse gas emissions and other pollutants emissions, such as the nitrogen dioxide (NOx) and the sulfur oxides (SOx). Decarbonizing maritime transport, through the transition to electrified propulsion, is a major challenge for researchers and engineers in the naval construction sector. This paper studies electric, thermal and hybrid energy/propulsion solutions for ships serving the island of Ouessant, located near Brittany’s coast in France. Using a simplified methodology based on power flows between the various components (batteries, generators, fuel cells), the most suitable type of energy is investigated according to the case study, for efficient and effective vessel use. The simulations show that the energy consumption of the fully electric solution is the best with 5.78 MWh consumed against, for example, 13.89 MWh equivalent of fuel for the classic diesel topology.

Effective utility system management is fundamental and critical for ensuring the normal activities, operations, and services in cities and urban areas. In that regard, the advanced information and communication technologies underpinning smart cities enable close linkages and coordination of different subutility systems, which is now attracting research attention. To increase operational efficiency, we propose a two-stage optimal co-management model for an integrated urban utility system comprised of water, power, gas, and heating systems, namely, integrated water-energy hubs (IWEHs). The proposed IWEH facilitates coordination between multienergy and water sectors via close energy conversion and can enhance the operational efficiency of an integrated urban utility system. In particular, we incorporate social-aware peer-to-peer (P2P) resource trading in the optimization model, in which operators of an IWEH can trade energy and water with other interconnected IWEHs. To cope with renewable generation and load uncertainties and mitigate their negative impacts, a two-stage distributionally robust optimization (DRO) is developed to capture the uncertainties, using a semidefinite programming reformulation. To demonstrate our model's effectiveness and practical values, we design representative case studies that simulate four interconnected IWEH communities. The results show that DRO is more effective than robust optimization (RO) and stochastic optimization (SO) for avoiding excessive conservativeness and rendering practical utilities, without requiring enormous data samples. This work reveals a desirable methodological approach to optimize the water–energy–social nexus for increased economic and system-usage efficiency for the entire (integrated) urban utility system. Furthermore, the proposed model incorporates social participations by citizens to engage in urban utility management for increased operation efficiency of cities and urban areas.

This paper investigates voltage regulation in low voltage (LV) networks under different loading conditions of a supply network, with increased levels of distributed generation, and in particular with a diverse range of locational solar photovoltaic (PV) penetration. This topic has been researched extensively, with beneficial impacts expected up to a certain point when reverse power flows begin to negatively impact customers connected to the distribution system. In this paper, a voltage-based approach that utilizes novel voltage-based reliability indices is proposed to analyse the risk and reliability of the LV supply feeder, as well as its PV hosting capacity. The proposed indices are directly comparable to results from a probabilistic reliability assessment. The operation of the network is simulated for different PV scenarios to investigate the impacts of increased PV penetration, the location of PV on the feeder, and loading conditions of the MV supply network on the reliability results. It can be seen that all reliability indices improve with increased PV penetration levels when the supply network is heavily loaded and conversely deteriorate when the supply network is lightly loaded. Moreover, bus voltages improve when an on-load tap changer is fitted at the secondary trans-former which leads to better reliability performance as the occurrence and duration of low voltage violations are reduced in all PV scenarios. The approach in this paper is opposed to the conventional reliability assessment, which considers sustained interruptions to customers caused by failure of network components, and thus contributes to a comprehensive analysis of quality of service by considering transient events (i.e., voltage related) in the LV distribution network.

This paper proposes a novel real option (RO)-based network investment assessment method to quantify the flexibility value of battery energy storage systems (BESS) in distribution network planning (DNP). It applied geometric Brownian motion (GBM) to simulate the long-term load growth uncertainty. Compared with commonly used stochastic models (e.g. normal probability model) that assume a constant variance, it reflects the fact that from the point of prediction, uncertainty would increase as time elapses. Hence, it avoids the bias of traditional net present value (NPV) frameworks towards lumpy investments that cannot provide strategic flexibility relative to more flexible alternatives. It is for the first time to adopt the option pricing method to evaluate the flexibility value of distribution network planning strategies. To optimize the planning scheme, this paper compares the static NPVs and flexibility values of different investment strategies. A 33-bus system is used to verify the effectiveness of the formulated model. Results indicate that flexibility values of BESS are of utmost importance to DNP under demand growth uncertainties. It provides an analytical tool to quantify the flexibility of planning measures and evaluate the well-timed investment of BESS, thus supporting network operators to facilitate flexibility services and hedge risks from the negative impact of long-term uncertainty.

With the increased penetration of renewables, energy storage has become a critical issue in microgrid and small household applications. Accordingly, this paper undertakes a feasability study the varying limitations from conventional batteries in residential buildings, such as capacity-loss over time and aging, as well as the alternative application and challenges of hydrogen-based storage for the domestic sector. The paper considers a test case study where an analysis is performed on the practicality of hydrogen-based storage, in addition to lithium-ion battery storage. Various scenarios are considered based on solar installation sizes, self-consumption, battery capacity, autonomy rates and grid extraction. A detailed analysis is carried out on both thermal and electrical demands of a residential household, which also includes the energy performance and applications of heat pumps. While the obtained results from various scenarios are compared and analysed, these anticipate that the potential integration of hydrogen can improve the autonomy rate of residential buildings, The cost of hydrogen storage is expected to reduce significantly, opening opportunities for hydrogen application.