Search Results (283)

Extreme weather events have raised the frequency of power outages, posing critical challenges to the sustainability and resilience of modern power systems. In such cases, distributed energy resources (DERs) can effectively support the re-establishment of sustainable power supply for critical loads within the distribution network and reduce power outage losses. In this paper, a sustainable fault recovery framework based on an intentional islanding scheme is proposed to partition the distribution system in order to optimize the priority restoration of critical loads, while taking the operational constraints of the system into consideration. In addition, a blockchain-based P2P insurance mechanism is applied to mitigate the outage losses of the network’s users with a higher degree of security and transparency. By linking technical restoration decisions with financial risk-sharing mechanisms, the proposed framework improves economic sustainability and social equity among network users. For this purpose, a multi-layer, multi-objective optimization algorithm is proposed for optimal partitioning of the distribution network, management of DERs, and demand side management of flexible loads in order to minimize the outage losses and the insurance premium, while maintaining satisfactory performance of the network. To validate the feasibility of the proposed algorithm, the 45-node distribution network of Alexandria, Egypt is used. The results show that a reduction in peak load, outage losses, and operational costs are achieved, with an overall saving of 17.34%, in addition to a premium reduction of 41.3%. These results highlight the effectiveness of the proposed framework in enhancing the environmental, economic, and operational sustainability of distribution systems under outage conditions. Full article

The accelerated development of Smart Cities globally, driven by rapid urbanization and urgent climate challenges, underscores the critical role of advanced energy infrastructures integrated with emerging digital technologies. This article explores the evolution of smart cities from an energy-centric viewpoint, emphasizing the interdependence among energy systems, digitalization and cutting-edge communication technologies. Adopting a system-of-systems perspective, we examine how different urban subsystems, including energy grids, transportation networks and data management systems, interact to improve overall urban functionality and long-term viability. Through a structured analysis of recent literature, we highlight the transformative potential of renewable energy integration, intelligent energy management systems and the crucial transition from 5G to 6G communication infrastructures, which collectively promise significant enhancements in urban sustainability, efficiency and resilience. Additionally, we address key challenges such as cybersecurity vulnerabilities, fragmented standardization frameworks and the need for comprehensive data governance. Viewing smart cities as a complex system of systems, this article argues for a holistic and interdisciplinary approach, emphasizing enhanced interoperability, robust cybersecurity protocols and inclusive participatory governance frameworks. Full article

The aggregation of flexible resources contributes to sustainability because it impacts on CO₂ emissions, enables renewable energy integration, improves network efficiency and makes the electric power system more resilient. The research project DEMAND has tested the potential of bottom-up aggregation of smart prosumers with no intermediation by a third-party balancing service provider. The present work analyzes the electrical and environmental effects of this new type of aggregation in different scenarios, taking into account both simulated data and data obtained from four pilot sites where the DEMAND system has been implemented. The effectiveness of the proposed aggregation method is evaluated through the calculation of some KPIs: power peaks, grid losses, voltage drops and CO₂ emissions. Full article

Digital substations (DSs) play a critical role in modern Energy and Power Electrical Systems (EPESs), enabling intelligent control, monitoring, and automation. With increased reliance on communication and sensing technologies, DSs are vulnerable to cyberattacks such as False Data Injection (FDI). An adversary may falsify transformer temperature readings, misleading protection mechanisms and resulting in incorrect disconnection actions. These false disconnections may disrupt power delivery, cause economic losses, and reduce equipment lifespan. To address these challenges, we propose a reinforcement learning-based approach for cyber protection of smart grids against false temperature data injection attacks. Specifically, this work designs a Long Short-Term Memory Deep Deterministic Policy Gradient (LSTM-DDPG) deep reinforcement learning algorithm that learns to detect normal patterns and responds to suspicious thermal patterns by dynamically adjusting disconnection decisions. The agents process sequential state features to differentiate between legitimate overload conditions and sudden anomalies caused by FDI attacks. We implement the proposed approach on the IEEE 30-bus distribution network using the Pandapower simulator. The experimental results indicate that the LSTM-DDPG controller outperforms conventional DDPG and DQN baselines, achieving a recall of 0.897, F1 of 0.945, precision of 1.00 and accuracy of 0.981 with a confidence interval of 95%. In addition, grid stability reaches up to 0.9815, 1.0, 1.0, 0.9926 with respect to the voltage stability score, transformer stability value, disconnection stability, and stability index, respectively. The proposed method led to fewer false disconnections, providing improved robustness against sensor manipulations. Full article

Timely fault detection, classification, and localization are fundamental to enabling fast service restoration in modern distribution networks, and are especially vital for maintaining the reliability and resilience of smart city electricity infrastructures. A new AI-based method for classifying and localizing fault types is presented in this paper, which enhances situational awareness in smart distribution grids that supply dense urban loads and critical smart city services. The proposed approach targets various fault conditions, which include three-phase-to-ground, three-phase, two-phase-to-ground, two-phase, and single-phase-to-ground faults. The proposed method utilizes a wavelet-based signal processing technique to analyze the feeder’s current data captured by waveform measurement units (WMUs) and extracts features for fault analysis. As a result of these features, a multi-stage machine learning architecture incorporating deep learning components is developed to accurately determine the occurrence, type, and location of faults. To evaluate the performance of the proposed approach, simulations were conducted on a 16-bus distribution network. Results show a high level of accuracy in fault detection, classification, and localization. This indicates that the method can be a valuable tool for enhancing the resilience and intelligence of future power grids, as well as supporting self-healing and fast service restoration in smart city services. Full article

Accurate multivariate long-term time series forecasting (LTSF) is critical for smart grid operations. However, non-stationary distribution shifts frequently induce compounding error accumulation in conventional architectures. This study proposes the Mamba-LSTM-Attention (MLA) framework, a distribution-aware architecture engineered for forecasting stability. The pipeline integrates Reversible Instance Normalization (RevIN) to neutralize statistical drift. To address computational bottlenecks, the architecture utilizes a linear-time Selective State Space Model (Mamba) to capture global trend dynamics, cascaded with a single-layer gated Long Short-Term Memory (LSTM) unit to model localized non-linear residuals. A terminal information bottleneck structurally bounds cross-step error propagation. Empirical results across standard ETT and Electricity benchmarks reveal a precision–stability trade-off. By prioritizing structural resilience, the MLA framework limits error accumulation on highly volatile datasets, yielding MSEs of 0.210 and 0.128 on ETTh2 and ETTm2 at the T = 96 horizon. This structural bottleneck inherently smooths high-frequency periodic patterns, yielding lower absolute accuracy on stationary benchmarks such as ETTh1 and ETTm1. Ultimately, the architecture establishes a computationally efficient, structurally stable baseline tailored for non-stationary anomaly tracking in smart grids. Full article

This paper investigates blockchain applications in the EU’s energy sector, particularly its integration into Internet of Things (IoT)-enabled smart grid systems. The study begins by mapping current EU regulations and incentives for smart energy solutions and reviews emerging smart grid technologies across Europe. The goal is to develop an Innovative Success Framework by analyzing European case studies, aiming to guide energy managers with practical strategies for improving smart grid efficiency. Key findings underscore the role of blockchain in ensuring secure, transparent energy transactions, addressing data security, energy distribution, and decentralized markets. Detailed case studies reveal common success factors: strong regulations, robust technology, and stakeholder engagement. The resulting framework aids energy managers in navigating smart grid complexities, promoting sustainable development through efficient, resilient, and low-carbon energy infrastructures. This research enriches discussions on smart energy, offering policymakers and industry professionals a tool to harness blockchain for advancing sustainable and secure energy systems in line with long-term EU development goals. Full article

The integration of artificial intelligence (AI) into solar energy systems has emerged as a transformative pathway to enhance efficiency, reliability, and sustainability in renewable energy. This review examines recent advances in AI-driven optimization and integration strategies across photovoltaic and solar thermal technologies with elements of bibliometric analysis to identify trends, methodologies, and research directions. A particular emphasis is placed on machine learning and deep learning techniques applied to solar irradiance forecasting, maximum power point tracking, fault detection, energy management, and predictive maintenance. Unlike earlier reviews that focused on isolated applications, this work highlights the systemic role of AI in enabling smart grids, hybrid systems, and large-scale energy storage integration. The novelty of this contribution lies in mapping the evolution from traditional control methods to intelligent, self-adaptive frameworks that couple physical modeling with data-driven approaches, offering a structured roadmap for future developments. Furthermore, the review identifies challenges such as data scarcity, computational demand, and interpretability of AI models, while outlining opportunities for process intensification, resilience, and techno-economic optimization. By bridging technical progress with implementation prospects, this article provides an updated reference for researchers, policymakers, and industry stakeholders seeking to accelerate the deployment of AI-enhanced solar energy solutions. Full article

The transition toward sustainable energy systems is increasing the operational complexity of modern power grids due to the high penetration of renewable energy sources, distributed energy resources, and bidirectional energy flows. Artificial intelligence has emerged as a key enabling technology for forecasting, optimization, and control in smart grids. Current AI implementations in energy systems lack unified workflows integrating forecasting, decision-making, adaptive stability regulation, and distributed coordination. Moreover, existing control approaches rarely incorporate biologically inspired stability mechanisms such as homeostatic regulation, limiting system-level resilience under dynamic operating conditions. This work aims to develop an architectural framework in the form of a unified artificial intelligence meta-pipeline enabling homeostatic control and distributed resource optimization in sustainable energy systems through closed-loop intelligent operation. A layered artificial intelligence meta-pipeline architecture is proposed integrating system representation, data intelligence, decision intelligence, homeostatic feedback regulation, and distributed coordination. A formal Homeostatic Energy Index is introduced to quantify system stress and enable supervisory adaptive policy regulation. The framework is validated using a reproducible microgrid-level simulation combining reinforcement learning-based control with homeostatic feedback regulation. Experimental validation demonstrates stable closed-loop operation under stochastic demand and renewable variability. The framework maintains bounded system stress levels, achieving an average Homeostatic Energy Index of 18.17 while preserving near-zero energy imbalance performance, confirming that homeostatic feedback improves stability without degrading energy balancing performance. This work introduces a unified artificial intelligence meta-pipeline architectural framework and formally defines a homeostatic feedback layer for sustainable energy system control. The proposed approach enables stability-aware structured integration of heterogeneous AI components and provides a foundation for self-adaptive, resilient, and distributed intelligent energy systems. Full article

Electricity systems increasingly operate under deep uncertainty driven by geopolitical risk, volatile fuel markets, trade fragmentation, security threats, and technological change. Under such conditions, cost-optimal planning based on assumed trajectories may lead to fragile outcomes, particularly for small and geopolitically exposed systems such as Israel’s. This paper assesses the resilience of alternative electricity transition strategies for Israel using a qualitative robustness framework inspired by Decision Making under Deep Uncertainty and scenario-based energy security analysis. Six policy-relevant strategies are evaluated across structurally distinct stress scenarios. Resilience is assessed along three dimensions: security of supply, dependency exposure, and economic vulnerability, using anchored qualitative scoring and dominance rules. The results indicate that gas-centric strategies exhibit limited robustness, while strategies combining solar deployment with adaptive gas management, smart grids, microgrids, and domestic clean-technology capabilities achieve higher resilience across a wide range of futures. The paper contributes a structured qualitative approach to resilience assessment and offers policy-relevant insights for electricity transitions under deep uncertainty. Full article

The increasing penetration of intermittent renewable energy demands innovative solutions to maintain grid stability, resilience, and security in the body of smart cities. This paper presents a novel framework that redefines Bitcoin mining as a form of virtual energy storage, a flexible and controllable load capable of delivering large-scale demand response services, positioning it as a competitive alternative to traditional energy storage systems, including electrical, mechanical, thermal, chemical, and electrochemical storage solutions. By strategically aligning mining activities with grid conditions, Bitcoin mining can absorb excess electricity during periods of oversupply, converting it into digital assets, and reduce operations during times of scarcity, effectively emulating the behavior of conventional energy storage systems without the associated capital expenditures and material requirements. Beyond its operational flexibility, this paper explores the cyber–physical benefits of integrating Bitcoin mining into the power transmission systems as a defensive mechanism against false data injection (FDI) cyberattacks in smart city infrastructure. To achieve this goal, a decentralized and adaptive control strategy is proposed, in which mining loads dynamically adjust based on authenticated grid-state information, thereby improving system observability and hindering adversarial efforts to disrupt state estimation. In addition, to handle the proposed approach, this paper introduces a high-performance algorithm, a combination of quantum-augmented particle swarm optimization and wavelet-oriented whale optimization (QAPSO-WOWO). Simulation results confirm that strategic deployment of mining loads improves grid sustainability by utilizing curtailed renewables, enhances resilience by mitigating load-generation imbalances, and bolsters cybersecurity by reducing the impacts of FDI attacks. This work lays the foundation for a transdisciplinary paradigm shift, positioning Bitcoin mining not as a passive energy consumer but as an active participant in securing and stabilizing the future power grid in smart cities. Full article

The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience. Full article

Background: Blockchain and Multi-Agent System (MAS) are increasingly combined to support decentralized, secure, and autonomous peer-to-peer energy trading in microgrid environments. Objectives: This systematic review investigates how blockchain and MAS are integrated to support microgrid energy trading, identifies architectural and operational models, examines real-world implementations, and highlights technical, regulatory, and security challenges. Unlike prior reviews that focus on blockchain or MAS in isolation, this study provides a unified and comparative analysis of their joint integration. Methods: Following PRISMA 2020 guidelines, a systematic search was conducted in IEEE Xplore, ACM Digital Library, and ScienceDirect, with the last search performed on 10 January 2025. Eligible studies focused on blockchain–MAS integration in microgrid energy trading; non-energy and non-microgrid applications were excluded. Study selection was performed independently by two reviewers, and methodological quality was assessed using an adapted Joanna Briggs Institute (JBI) checklist. A narrative synthesis categorized integration levels, blockchain platforms, MAS roles, and implementation contexts. Results: A total of 104 studies were included. Three dominant integration levels were identified—basic, intermediate, and advanced—distinguished by how decision-making responsibilities are distributed between MAS and smart contracts. Ethereum and Hyperledger Fabric were the most commonly used platforms. MAS agents perform concrete operational functions such as bid and offer generation, price negotiation, matching, and local energy optimization, fundamentally transforming control and monitoring processes. By enabling distributed, intelligent agents to perform real-time sensing, analysis, and response, an MAS enhances system resilience and adaptability. This architecture allows for proactive fault detection, dynamic resource allocation, and coherent, large-scale operations without centralized bottlenecks. Blockchain ensured transparency, trust, and secure transaction execution. Major challenges include scalability constraints, interoperability limitations with legacy grids, regulatory uncertainty, and real-time performance issues. Limitations: Most included studies were simulation-based, with limited real-world deployment and substantial heterogeneity in evaluation metrics. Conclusions: Blockchain–MAS integration shows strong potential for secure, transparent, and decentralized microgrid energy trading. Addressing scalability, regulatory frameworks, and interoperability is essential for large-scale adoption. Future research should emphasize real-world validation, standardized integration architectures, and AI-enabled MAS optimization. Funding: No external funding. Registration: This systematic review was not registered. Full article

Electric vehicle charging stations (EVCSs) are a critical infrastructure in urban areas. However, because they depend on power grids and digital networks, they are prone to disruptions from grid failures, extreme weather, and cyber threats. Ensuring resilience is therefore essential to minimise service disruptions and ensure reliable transportation in urban areas. While interest in EVCS resilience is growing, current studies are dispersed across technical, environmental, and spatial domains, limiting a consolidated understanding of how resilience is conceptualised and assessed in urban areas. Despite this growing body of research, no prior systematic review has comprehensively synthesised resilience-specific evidence for EVCSs in urban areas. Thus, the objective of the study was to systematically synthesise empirical research on resilient EVCSs in urban areas to identify key factors influencing resilience and how resilience is assessed. A systematic literature review was conducted on 52 empirical articles from Web of Science and Scopus published between 2015 and 2025, following the PRISMA protocol. The review revealed an increasing trend in publications over time, with research geographically concentrated in Asia, the United States of America, and Europe. Results also showed that the resilience of EVCSs in urban areas is influenced by context-related factors (such as location, environment, and governance) and system-related factors (such as operational, technical, and financial), with location and technical issues being the most studied. The resilience of EVCSs is mainly assessed through accessibility, capacity, availability, and vulnerability, using tools such as indices, curves, scenarios, and optimisation models. However, gaps remain in governance, environment, modular design, predictive maintenance, social aspects, and developing economies. Future research should focus on integrating governance and equity into EVCS planning and developing modular, renewable-powered charging systems supported by smart technologies to enhance resilience in urban areas, particularly in developing economies. This review proposes a Factors-Dimensions Implementation framework that operationalises established resilience concepts by linking context- and system-related factors to measurable resilience dimensions of EVCSs in urban areas. Full article

Non-intrusive load monitoring (NILM) is a key enabler for smart-grid applications, yet practical deployment is often hindered by limited appliance-level labels and severe distribution shifts across households and operating conditions. As a result, many deep learning approaches become unreliable in small-sample and out-of-distribution (OOD) settings. In this paper, we propose CR-MAT, a causality-driven representation learning framework for few-shot NILM classification. Instead of relying on large-scale training or heavy data augmentation, CR-MAT injects causal representation learning into multi-appliance task modeling, encouraging the network to learn appliance-discriminative features that are stable across environments while suppressing spurious, domain-specific correlations. We conduct extensive experiments under multiple OOD scenarios and consistently observe improved classification robustness compared with deep NILM baselines. Further analysis indicates that causal representation learning enhances resilience to non-stationary consumption patterns and improves generalization under OOD scenarios. The proposed framework provides a practical route toward reliable NILM classification and supports downstream smart-grid applications such as flexible load control and demand response. Full article