Search Results (695)

The increasing penetration of renewable energy sources, distributed generation, and advanced control technologies has transformed microgrids into complex, dynamic systems that pose significant challenges for protection coordination. This paper presents a comprehensive bibliometric analysis of the scientific literature on protection strategies in modern microgrids. Using a curated dataset from the Scopus database, four types of analyses were conducted: trend topic analysis, dendrogram clustering, co-occurrence network mapping, and thematic map analysis. The trend topic analysis highlights the temporal evolution of specific topics. The dendrogram analysis reveals thematic groupings and highlights concepts that have received limited attention. The co-occurrence network analysis reveals interactions between terms, and the thematic map analysis identifies basic, niche, and motor themes, as well as emerging or declining themes. These insights provide a structured overview of current knowledge and potential future research directions in microgrid protection. This study serves as a valuable reference for researchers and practitioners aiming to understand and address the evolving challenges associated with protection coordination in modern microgrids. Full article

Metaheuristic optimization algorithms often suffer from structural imbalance between exploration and exploitation, leading to premature convergence and performance degradation in high-dimensional or constrained problems. To address this issue, a symmetry-enhanced Improved Enterprise Development Optimization Algorithm (IEDOA) is proposed. The algorithm establishes a dynamic symmetry between global exploration and local exploitation through three coordinated strategies: a performance-feedback-based adaptive activity selection mechanism, a multi-elite-guided structural evolution strategy, and a lifecycle-aware exploration mechanism inspired by technological scheduling dynamics. The proposed symmetric regulation framework improves population diversity while preserving convergence stability, thereby enhancing search efficiency in complex landscapes. To validate its performance, IEDOA is evaluated on CEC2017 (30/50 dimensions) and CEC2022 (10/20 dimensions) benchmark suites and compared with several advanced metaheuristic algorithms. Experimental results demonstrate superior convergence accuracy, robustness, and scalability. Statistical analyses using the Wilcoxon signed-rank and Friedman tests further confirm its significant performance advantages. To demonstrate practical applicability, IEDOA is applied to a grid-connected microgrid economic dispatch problem involving renewable generation units, controllable generators, and energy storage systems under 24 h operational constraints. Simulation results show that the proposed method achieves lower operational costs and smaller performance variance across independent runs. Overall, IEDOA provides an effective symmetric optimization framework for complex engineering systems characterized by nonlinearity, multi-constraints, and high dimensionality. 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

Remote off-grid microgrids are often locked into diesel-backed operation because renewable variability creates multi-day and seasonal energy gaps that short-duration batteries cannot economically bridge. This work examines how renewable hydrogen can complement batteries and dispatchable biomass to push an existing hybrid microgrid toward near-autonomous, low-carbon operation, while remaining robust under future electrification demands. The analysis is based on real operational load insights from a remote off-grid system, combined with techno-economic optimization in HOMER Pro. The examined architecture includes PV panels, battery energy storage, a biomass CHP unit, and a diesel generator as backup; the hydrogen pathway additionally incorporates an electrolysis, storage and a PEMFC. Three scenarios are considered: a baseline PV/BAT configuration, an intermediate PV/BAT/BIO configuration that strengthens dispatchable renewable supply and short-term flexibility, and a PV/BAT/BIO/H₂ configuration targeting an increase in renewable energy penetration (REP). Results show that hydrogen integration shifts the system from curtailment-limited, diesel-supported operation to storage-enabled operation: surplus renewable production that would otherwise be curtailed is converted into hydrogen and later dispatched during prolonged deficits, enabling deep diesel displacement without compromising reliability. Hydrogen-enabled configurations achieve 90–99% REP, reduced diesel consumption, and lower CO₂ emissions, primarily by converting curtailed surplus into storable hydrogen. A rule-based EMS highlights technology complementarity across timescales, with batteries providing diurnal balancing and hydrogen covering longer deficits, which also reduces battery cycling stress. Overall, the study clarifies key design trade-offs, especially the need for coordinated PV expansion and storage sizing, and illustrates how a multi-storage portfolio can support high renewable penetration in such 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 transition towards sustainable energy systems requires a paradigm shift from purely economic optimization to a holistic framework that internalizes environmental and social externalities. This article integrates social and environmental aspects into the multi-objective dispatch model based on mixed-integer linear programming (MILP) for the economic, environmental, and social dispatch (EEDS) of a polygeneration microgrid. Unlike traditional approaches that treat social impact as a static planning constraint, this study introduces a quantified “Social Shadow Price” into the operational objective function, aiming to operationalize the concept of energy justice. The model is applied to a case study featuring a high-load factor industrial demand profile, integrated with thermal generation, solar PV, wind power, and BESS storage. Results demonstrate that internalizing environmental and social costs significantly alters the merit order dispatch, reducing the utilization of socially contentious technologies while leveraging storage arbitrage to mitigate intermittency. Furthermore, a sensitivity analysis is conducted to determine the optimal capacity of renewable energy sources, revealing that a balanced mix of solar and wind minimizes the composite sustainability index. The findings suggest that this EEDS framework provides a viable pathway for policymakers to achieve a socially equitable energy transition in industrial sectors. Full article

Conventional dispatch models, which are primarily focused on cost minimization, prove insufficient to address the multidimensional challenges of a Just Energy Transition. In order to address this discrepancy, the present paper puts forth the Economic, Environmental, and Social Dispatch (EEDS) model. The EEDS model is a Mixed-Integer Linear Programming (MILP) Unit Commitment formulation that explicitly incorporates socio-environmental externalities. The methodology implements a two-stage rolling horizon simulator (Day-Ahead and Real-Time) with high temporal resolution (5 min), validated on a polygeneration microgrid integrated with Battery Energy Storage Systems (BESS). The numerical results indicate that the incorporation of quantified social costs substantially modifies the merit order, effectively displacing technologies that are deemed to be socially regressive. Moreover, the analysis demonstrates that demand morphology is a pivotal factor in determining system performance, achieving zero Unserved Energy (ENS) and competitive prices across diverse profiles. Finally, the application of scenario analysis demonstrates that BESS is essential for managing diverse demand morphologies and moderating price volatility across different operational contexts. Therefore, the EEDS framework provides a rigorous quantitative foundation upon which economic efficiency, sustainability, and operational social justice can be balanced. Full article

The Gulf Cooperation Council (GCC) countries are accelerating their transition toward sustainable mobility as part of broader national strategies to diversify economies and reduce dependence on hydrocarbons. This paper explores the development of electric vehicle (EV) charging infrastructures and their integration with renewable energy sources across the GCC countries. It highlights key government policies, renewable energy potential, and emerging technologies such as solar-powered charging, smart grids, and vehicle-to-grid systems. While progress is evident in nations like Saudi Arabia, the UAE, and Qatar, challenges persist, including limited charging infrastructure, high costs, and climatic constraints. The study identifies opportunities for advancing sustainability through microgrids, hydrogen mobility, and regional policy harmonisation. It concludes that the decarbonisation benefits of EV charging infrastructure depend on how closely its expansion is aligned with renewable energy growth in the GCC. Full article

In the Latin American Pacific region, rivers are the primary transportation routes for isolated and non-interconnected areas; however, river transport relies heavily on fossil fuels, resulting in high operating costs, CO₂ emissions, and energy dependence. To address this challenge, this study proposes a methodology for the optimal sizing of renewable-based charging stations specifically adapted to the environmental and operational conditions of the Colombian Pacific coast. This research fills a critical gap in the literature by moving beyond urban-centric charging models and simplified theoretical assumptions, instead integrating real river navigation data with technical modeling of electric boat energy consumption. The methodology evaluates the technical, economic, and operational performance of photovoltaic and hybrid photovoltaic–hydrokinetic microgrids designed to ensure reliability under the region’s extreme resource seasonality and bimodal pluvial regime. Results indicate that while purely photovoltaic systems offer lower initial investment costs, hybrid configurations significantly enhance energy resilience by leveraging complementary renewable sources during periods of low solar irradiation. Crucially, the transition to electric propulsion reduces annual CO₂ emissions by more than 98%, mitigating approximately 3421 kg per vessel compared to conventional 20 HP gasoline engines. A comparative analysis shows that the 1.1 kW electric boat is a cost-effective solution, with a 1.76-year return on investment. In contrast, the 4 kW model offers operational performance comparable to conventional gasoline boats, with a 4.95-year payback. This study provides a foundational framework for sustainable mobility in high-vulnerability territories by adapting technological solutions to site-specific environmental realities. Full article

Automation and sustainability are closely intertwined in the evolution of Industry 4.0 and 5.0, where artificial intelligence (AI) plays a key role in transforming energy consumption and production efficiency. For Industry 4.0, AI-based automation has optimized production, logistics, and resource management, reducing waste and improving throughput through predictive analytics and intelligent control systems. These systems have enabled energy-efficient production lines by automatically adjusting processes to minimize downtime and energy consumption. However, the increasing use of AI and digital infrastructure has also led to an increase in demand for computing energy, raising concerns about data center efficiency and carbon footprint, leading to the division between Green AI and Red AI. Industry 5.0 expands this paradigm, focusing on human–machine collaboration and sustainable design, where AI supports personalization, circular economy practices, and the integration of renewable energy. Generative AI and digital twins (DTs) enable real-time energy modeling, helping companies simulate outcomes and choose the most sustainable paths. Automation also enables predictive maintenance, extending machine life and reducing material waste. At the same time, AI is contributing to the development of decentralized energy systems, such as smart grids and microgrids, which increase resilience and reduce emissions. A key challenge is balancing the energy efficiency benefits of automation with the sustainability of the AI infrastructure itself, which requires innovation in energy-efficient computing and green algorithms. From this perspective, AI-based automation represents both a solution and a challenge: it accelerates the achievement of sustainable development goals while requiring responsible technological management to ensure long-term ecological sustainability. Full article

Extreme hydro-meteorological events are intensifying under climate change, disproportionately disrupting last-mile healthcare in flood-prone geographies. In this study, flood-adaptive primary care clinics (FAPCCs) integrated with islandable smart microgrids and a rapid-deploy medical technology stack (MedTech) are developed and evaluated to ensure continuity of essential services (triage, maternal and child health, vaccination cold-chain, minor procedures, diagnostics, and telemedicine) during fluvial, pluvial, and coastal flooding. Evidence on resilient health facilities, microgrid architectures, distributed energy resources, and modular clinical systems is presented in a multi-layer systems design: (1) a modular, amphibious, and elevatable clinic chassis; (2) a photovoltaic–battery–diesel hybrid system with demand-aware energy management; (3) redundant connectivity long-term evolution/fifth-generation, satellite, and very high frequency; (4) a rapid-deploy MedTech kit including point-of-care diagnostics, low-temperature cold-chain, negative-pressure isolation, and sterilization modules; and (5) flood-aware logistics using unmanned aerial vehicle/unmanned surface vehicle. A mixed-integer linear programming sizing is formulated and dispatched with a continuity-of-care reliability metric that couples energy availability to clinical throughput. Simulation across three archetypal sites (peri-urban delta, inland riverine, coastal estuary) shows that FAPCCs achieve the service availability of higher than 99.5% across 7-day grid outage scenarios while reducing fuel use by 62–81% relative to diesel-only baselines, maintaining vaccine temperatures within 2–8 °C with <0.1% thermal excursion time, and sustaining telemedicine quality of service with <150 ms median uplink latency in hybrid networks. A life-cycle cost analysis indicates a 7.1–9.8 year discounted payback from fuel displacement and avoided service loss. Deployment playbooks and policy guidance are also proposed for Ministries of Health and Disaster Agencies in monsoon-impacted regions. Full article

The increasing use of integrated renewable energy sources (RESs) is undoubtedly reshaping the structure of power systems. In such conditions, achieving energy efficiency and sustainability requires the development and integration of digital solutions to manage energy flows and assets optimization. This paper aims to provide a comprehensive review of the successful integration of artificial intelligence (AI) in the energy sector, particularly in relation to the high penetration of renewable energy. The paper presents trends and potential scenarios in the digitalization of energy, along with the associated challenges. It analyzes particular applications of AI tools in strategic areas of the energy sector. Five key areas of the energy sector are identified where AI tools are applied: forecasting electricity generation from RES; forecasting demand and price fluctuations on the electricity spot market; the real-time management of energy flows and assets in active microgrids; and data processing and analyzing, and general industrial direction. The article also attempts to summarize the current status, goals, key areas, and activities in the irreversible transformation of power structures into digital intelligent ones. This digital transformation is a gradual process with consecutive steps. To improve understanding and clarity, the authors present a three-phase roadmap of AI adoption. To develop an adequate AI integration strategy, it is necessary to understand the technologies, algorithms, hierarchical structure, and connections within this structure. Accordingly, the article presents a taxonomy of the hierarchical structure of AI. The subsequent step involves the sequential construction of a digitalization model. Here, the authors consider it necessary to present a 4-layer structure model of AI energy democracy. Finally, through a comparative analysis of different types of intelligent applications for energy problem solving, guidelines are provided for successful decision making in compliance with the specified harmonized standards and protocols. Full article

This review systematically synthesizes technological synergies within a Community Energy System (CES), emphasizing cold-climate contexts where heating-dominant demand profiles and strong seasonality create distinct operational challenges. Drawing on 115 studies (2010–2024), the paper explores how integrated thermal, electrical, and digital infrastructures support net-zero and climate-resilient communities in regions with substantial heating requirements. Thermal–electrical coupling emerges as a foundational mechanism in cold climates, where heating loads dominate annual energy demand and drive winter peak constraints. Power-to-Heat (P2H) systems, cold-climate heat pumps, and hybrid configurations combining Thermal Energy Storage (TES) with Battery Energy Storage Systems (BESS) enable multi-timescale flexibility, allowing renewable energy to be shifted from hours to seasons. District Energy Systems (DES) act as a thermal backbone, enabling this integration across extended heating seasons and transforming thermal demand into a grid-balancing resource. Digital technologies further enhance system coordination under variable climatic conditions. Artificial Intelligence (AI), the Internet of Things (IoT), and Advanced Metering Infrastructure (AMI) support real-time optimization, demand response, and cross-vector control within Renewable Energy Communities (RECs) and Virtual Power Plants (VPPs). At the system level, decentralized architectures—including microgrids, Non-Wire Alternatives (NWAs), and peer-to-peer (P2P) trading—strengthen resilience by maintaining thermal and electrical continuity during grid disruptions. Building on these findings, the review synthesizes cross-cutting technological synergies and proposes deployment pathways tailored to cold-climate CES, supported by comparative case studies. Despite demonstrated benefits, widespread adoption remains constrained by high upfront costs, interoperability challenges, and fragmented regulatory frameworks. The review concludes with policy, governance, and research recommendations to enable scalable, equitable, and climate-responsive CES deployment in heating-dominated regions. Full article

As the global energy landscape shifts toward sustainability, microgrids incorporating Photovoltaic (PV) generation and Battery Energy Storage Systems (BESS) are becoming essential in commercial and industrial facilities. This research tackles the challenge of maintaining uninterrupted power supply to sensitive loads when grid disturbances occur. We propose a novel loss-of-mains detection method capable of identifying grid faults in under 3 milliseconds—well within the 10-millisecond threshold required for critical equipment to ride through the transition without disruption. Building on this fast detection, we develop inverter control strategies that enable a smooth transfer from grid-following to grid-forming operation while limiting transient overvoltage and overcurrent. Additionally, a coordinated operating sequence is introduced to ensure grid code compliance and proper management of distributed energy resources throughout the islanding process. The complete approach is validated experimentally using a dedicated prototype and a Power-Hardware-in-the-Loop (P-HIL) microgrid demonstrator, confirming its effectiveness and advancing the technology readiness level toward real-world deployment. Full article

In response to the International Maritime Organization’s emission reduction targets, ship power systems are transitioning toward microgrid architectures with high renewable energy penetration. In islanded mode, the lack of main grid support and the low inertia of power electronic interfaces pose significant frequency stability challenges. Virtual Synchronous Generator (VSG) technology offers an effective solution, but conventional VSG control exhibits two inherent limitations: steady-state frequency deviation under load variations due to its primary regulation nature, and poor dynamic response characterized by large overshoot and prolonged settling time. This paper proposes an enhanced VSG control strategy integrating two key innovations: (i) a communication-free secondary frequency regulation loop that eliminates steady-state error, and (ii) an adaptive control scheme for virtual inertia and damping coefficients that dynamically responds to frequency deviations and their rate of change. The adaptive mechanism reduces overshoot by 57% (from 0.14 Hz to 0.06 Hz) and shortens settling time by 40% (from 0.38 s to 0.23 s) compared to non-adaptive secondary regulation, as demonstrated through MATLAB/Simulink simulations and 6 kW experimental prototype validation. The proposed strategy ensures both steady-state accuracy and enhanced transient performance, providing a reliable solution for improving power quality in islanded shipboard microgrids and contributing to maritime decarbonization goals. Full article